Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest Intracranial Tumor Volume for Stereotactic Radiosurgery-Treated Brain Metastasis Patients

Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest... Abstract BACKGROUND Two intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV). OBJECTIVE To determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components—LITV—with CITV. METHODS We compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients. RESULTS In both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model. CONCLUSION After accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients. Brain metastases, Cumulative intracranial tumor volume, Net reclassification improvement, Prognostic model, Radiosurgery ABBREVIATIONS ABBREVIATIONS AIC Akaike Information Criterion BM brain metastases CI confidence interval CITV cumulative intracranial tumor volume IDI integrated discrimination improvement KPS Karnofsky Performance Score LITV largest intracranial tumor NIH National Institutes of Health ROC receiver operating characteristic SIR scored index for radiosurgery SRS stereotactic radiosurgery WBRT whole brain radiation therapy NRI net reclassification improvement Recent estimates suggest that 25% to 45% of the cancer patient population will develop brain metastases (BM).1 Because most chemotherapeutic agents are unable to penetrate the blood–brain barrier, surgery and radiation are the mainstay treatments for BM.2 For surgically inaccessible BMs or small BMs without associated neurological deficits, radiation is the preferred treatment. Radiation delivery can be achieved by use of whole brain radiation therapy (WBRT),3 hippocampal sparing WBRT,4 and stereotactic radiosurgery (SRS).5 Because of potential deleterious neurocognitive effects associated with WBRT,6 SRS is increasingly utilized for BM patients with a limited number of metastases.7,8 Based on the Surveillance Epidemiology and End Results multistate cancer registry, SRS as treatment for BM has more than tripled in the past decade.9 While recent studies show increased survival for a subset of patients with BMs after the introduction of molecularly targeted therapies10 and immunotherapies,11 the survival expectation for patients with BM as a whole remains poor.12 With an expected median survival of 2 to 7 mo for all BM patients,2,13 a major challenge in clinical management involves weighing the inherent risks of potential intervention relative to survival expectation.14 In this context, several prognostic scales have been developed for patients afflicted with BM.15-17 While the specifics of these scales differ, they generally involve the same clinical variables, including age, Karnofsky Performance Score (KPS), extracranial disease status, histology, and the number of metastases.15-17 Intracranial tumor volume is an important prognostic factor for SRS-treated BM patients.17-20 The first prognostic scale to acknowledge the importance of intracranial tumor volume is termed Scored Index for Radiosurgery (SIR). SIR included 5 prognostic factors, including age, KPS, extracranial disease status, number of brain lesions, and volume of the largest intracranial tumor (LITV).17 Our previous studies suggested that the cumulative intracranial tumor volume (CITV), or the sum volume of all intracranial metastases, may also serve as a critical prognostic factor for SRS-treated BM patients.18-20 Here, we utilized the framework established by the SIR to test the prognostic performance of CITV relative to LITV. METHODS Study Cohorts All data collection was performed under Institutional Review Board approved retrospective review, which included a waiver of informed consent. The first dataset consists of 3061 consecutive BM patients treated by TS (1998-2014). This study population is termed the “TS cohort”. A posthoc sample size analysis of the TS cohort receiver operating characteristic (ROC) curves indicated that a minimum of 2055 patients would be necessary for a power of 0.8021 to detect the effect size seen in the cohort. To ensure our validation cohort was sufficiently powered to detect a difference of similar magnitude, we combined the cohorts of patients treated by MH and CC (1994-2014) to a resulting data set of 2793 patients. This study population is termed the “MC” cohort. Details of these cohorts have been previously published.18-20,22 In brief, every patient had a primary tumor confirmed outside of the central nervous system and underwent SRS without craniotomy. All patients were referred for SRS by a radiation or medical oncologist as well as a neurosurgeon. A multidisciplinary tumor board reviewed each of the SRS candidates. Patients who were recommended and underwent surgical resection were not included in the present analysis. For the purposes of our analyses, patients who underwent multiple radiosurgery treatments (n = 273—4.7% of all analyzed patients) were labeled as new patients, based on previously published findings that survival is comparable between patients receiving a singular round of SRS vs those receiving multiple SRS treatments.20 Decisions to pursue SRS vs WBRT were personalized based on the patient's general medical status as well as the status of their oncologic disease. In general, the clinical practice pattern of the 3 treating surgeons conform to generally accepted guidelines, which include reserving WBRT for those with prohibitively large tumor volumes, those unable to follow up with surveillance MRIs, those with miliary disease, and those receiving radiation for the sole purpose of palliation.23-25 Patient baseline characteristics for each data set are available in Table 1. SRS was performed as previously described.18 TABLE 1. Demographics and Clinical Characteristic of Study Patientsa   TS cohort  MC cohort  Number of patients  3061  2793  Socio-demographics       Age (mean, SD)  64.5 ± 11.1  63.2 ± 12.0   Sex (% male)  59.2%  58.9%  Patient properties       KPS (mean, SD)  85.5 ± 14.4  85.0 ± 13.4   Number of metastases (mean, SD)  6.6 ± 9.0  5.7 ± 8.9   Active extracranial disease (%)  68.6%  85.6%   Largest lesion (cc; mean, SD)  5.3 ± 6.8  6.4 ± 8.9   CITV (cc; mean, SD)  8.3 ± 10.2  8.7 ± 11.8  Type of cancer (%)       Breast  11.0%  11.9%   Gastrointestinal  12.7%  9.1%   Lung  65.5%  61.6%   Urogenital  6.2%  4.8%   Other  4.6%  12.6%  Survival       Overall survival (years) (mean, SD)  1.1 ± 1.4  0.9 ± 1.4   Patients with minimum 1 yr survival  33.4%  27.6%    TS cohort  MC cohort  Number of patients  3061  2793  Socio-demographics       Age (mean, SD)  64.5 ± 11.1  63.2 ± 12.0   Sex (% male)  59.2%  58.9%  Patient properties       KPS (mean, SD)  85.5 ± 14.4  85.0 ± 13.4   Number of metastases (mean, SD)  6.6 ± 9.0  5.7 ± 8.9   Active extracranial disease (%)  68.6%  85.6%   Largest lesion (cc; mean, SD)  5.3 ± 6.8  6.4 ± 8.9   CITV (cc; mean, SD)  8.3 ± 10.2  8.7 ± 11.8  Type of cancer (%)       Breast  11.0%  11.9%   Gastrointestinal  12.7%  9.1%   Lung  65.5%  61.6%   Urogenital  6.2%  4.8%   Other  4.6%  12.6%  Survival       Overall survival (years) (mean, SD)  1.1 ± 1.4  0.9 ± 1.4   Patients with minimum 1 yr survival  33.4%  27.6%  acc, cubic centimeters; CITV, cumulative intracranial tumor volume; KPS, Karnofsky performance score; SD, standard deviation. View Large Radiosurgery Parameters and Overall Survival In both cohorts, in-house electronic medical record data were gathered, including age, KPS, extracranial disease status, number of BM, CITV, LITV, and overall survival. Only patients for whom each of these variables were known were analyzed; patients with any missing data (515 of 6369, or 8.1%) were excluded for the purposes of our analyses. We defined overall survival as the time (in months) from SRS to time of death. LITV was defined as the largest contiguous lesion present on the pre-SRS (T1-weighted postcontrast image) as calculated by the Elekta's Gamma Plan software. As previously published,18 CITV was defined as the sum volume of all treated lesions, such that in patients with a single lesion, the CITV and LITV values were identical. To facilitate comparison of the prognostic value of LITV and CITV, we set out to compare the sensitivity and specificity of 1-yr survival prognostication by SIR relative to a model where LITV was replaced with CITV (termed CITV-modified SIR). The SIR scale (termed SIR model) used in this study is as originally proposed17 and included age, KPS, number of metastases, extracranial disease status, and LITV (see Table 2). We used the optimized cut-offs of LITV (<5, 5-13, and >13 cc) as previously reported by Weltman et al.17 For the CITV-modified SIR model, all variables of SIR were included except that LITV was replaced with CITV. The optimized CITV cut-points (<3.5, 3.5-13, and >13 cc) were derived using methods similar to those used by Weltman et al17 for LITV (see Table 2). Median survival time (in months) for each of the point groupings is shown in Table 3. TABLE 2. Scoring Components for SIR- and CITV-Modified SIR Modelsa   SIR model  CITV-modified SIR model    0 points  1 point  2 points  0 points  1 point  2 points  Age  ≥60 yr  51-59  ≤50 yr  ≥60 yr  51-59  ≤50 yr  KPS  ≤50  60-70  ≥80  ≤50  60-70  ≥80  Number of lesions  ≥3  2  1  ≥3  2  1  Extracranial disease status  PD  PR  CR  PD  PR  CR  Largest lesion size  <5 cc  5-13 cc  >13 cc  –  –  –  CITV  –  –  –  <3.5 cc  3.5-13 cc  >13 cc    SIR model  CITV-modified SIR model    0 points  1 point  2 points  0 points  1 point  2 points  Age  ≥60 yr  51-59  ≤50 yr  ≥60 yr  51-59  ≤50 yr  KPS  ≤50  60-70  ≥80  ≤50  60-70  ≥80  Number of lesions  ≥3  2  1  ≥3  2  1  Extracranial disease status  PD  PR  CR  PD  PR  CR  Largest lesion size  <5 cc  5-13 cc  >13 cc  –  –  –  CITV  –  –  –  <3.5 cc  3.5-13 cc  >13 cc  aKPS, Karnofsky performance score; PD, progressive disease; PR, partial remission; CR, complete remission; cc, cubic centimeters; CITV, cumulative intracranial tumor volume; SIR, scored index for radiosurgery. View Large TABLE 3. Median Survival Time (in months) of SIR- and CITV-Modified SIR Point Groupingsa Point total  0  1  2  3  4  5  6  7  8  9  10  SIR model  2.7  2.2  3.4  4.3  7.0  7.7  9.5  13.6  21.6  23.3  42.0  CITV-SIR model  2.5  2.6  3.8  5.1  7.3  8.4  10.6  15.3  21.2  27.4  42.0  Point total  0  1  2  3  4  5  6  7  8  9  10  SIR model  2.7  2.2  3.4  4.3  7.0  7.7  9.5  13.6  21.6  23.3  42.0  CITV-SIR model  2.5  2.6  3.8  5.1  7.3  8.4  10.6  15.3  21.2  27.4  42.0  aSIR, scored index for radiosurgery; CITV, cumulative intracranial tumor volume. View Large Statistical Methods In order to justify direct comparison of CITV to LITV within the SIR model, we first needed to identify CITV as an independent predictor of survival. This was achieved by first performing a series of univariable analyses comparing the proportions and distributions of each SIR factor (ie, age, KPS, extracranial disease status, number of BM, LITV) across CITV groupings, to determine the extent of variability amongst SIR factors within distinct CITV groupings. Continuous factors were compared using ANOVA tests, while categorical factors were compared using chi-square tests. We next performed both a multivariable linear regression analysis and Cox proportional analysis to examine the predictive significance of CITV for patient survival (in months) after accounting for the remaining SIR factors (age, KPS, extracranial disease status, and number of BM). For relative comparison, the same set of multivariable analyses were performed examining the predictive capacity of LITV (ie, using the original SIR model factors). To more thoroughly compare the prognostic significance of CITV to LITV, a final set of multivariable analyses (linear regression and Cox proportional hazards) were performed including both CITV and LITV as predictors against the outcome of continuous survival; these models were examined to determine which variables retained prognostic significance upon the inclusion of both CITV and LITV. For all multivariable models, the Akaike Information Criterion (AIC) was calculated, such that the goodness of fit could be compared between models which included CITV vs LITV. In brief, the AIC allows for the quantification of a model's goodness of fit while at the same time penalizing models that are over-fit and would limit the external validity of the model to new data (ie, other patients not contained within the dataset). When comparing 2 models, a lower AIC indicates an improved goodness of fit; a reduction in AIC of at least 2 is necessary for one model to be deemed superior to the other.26 Finally, to directly compare the prognostic value of our CITV-modified SIR model to the traditional SIR model, we relied on the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices, as well as the calculated change in area under the ROC curve (ΔAUC) to determine the extent of prognostic improvement afforded by the replacement of LITV with CITV within the SIR model. In each case, patient survival was defined according to the discrete time point of 12 mo. This time point was chosen in part due to its prevalent usage in the literature.27-29 Calculating ΔAUC allowed for comparison of true positive and false positive rates for a given test as a whole. Calculating NRI and IDI helps quantify the number of correctly reassigned cases (ie, false negative to true negative, false positive to true positive) after implementing a change in the model such as the replacement of LITV with CITV. Taken together, all 3 values provided complementary information as to the relative improvement in a prognostic model which may result from a change in one (or more) of its variables.30 Statistical comparisons were performed with R version 3.2.3 and the predictABEL package for ΔAUC, NRI, and IDI calculations. R’s plotROC package was used to plot the ROC curves. All P < .05 were considered statistically significant. RESULTS Comparisons of LITV and CITV Detailed descriptions of the 2 study cohorts (TS and MC) have been previously published.18-20,22 Pertinent demographic and clinical variables pertaining to these cohorts are shown in Table 1. Both cohorts showed comparable demographics, with similar distribution in age (mean age of 64.5 and 63.2) and sex (59.2% and 58.9% male). The distribution in cancer types was also similar, with a predominance of lung cancer patients in both cohorts (65.5% and 61.6% for TS and MC cohorts, respectively). We first characterized relative distribution of LITV and CITV for each SRS-treated patient in the TS cohort. We found that LITV and CITV differed significantly for the majority of the patients. The CITV was at least 50% greater than the LITV in ∼80% of the patients (Figure 1A). CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV. These results suggest that the majority of the BM patients treated harbored multiple intracranial lesions. Moreover, the sum volume of the intracranial lesions (CITV) differed significantly from that of the largest tumor volume (8.33 vs 5.33 cc, P < .001). FIGURE 1. View largeDownload slide Discrepancy between CITV and LITV in the TS cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. FIGURE 1. View largeDownload slide Discrepancy between CITV and LITV in the TS cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. Prognostic Value of CITV The results of our univariable analyses are shown in Table 4. As shown in this table, KPS, extracranial disease status, number of lesions, and LITV were all found to differ for patients according to their CITV point grouping (ie, <3.5, 3.5-13, >13 cc). Only age was found to not significantly differ according to CITV point grouping. TABLE 4. Univariable Analysis of Each SIR Factor as Distributed Across CITV Groupings (<3.5, 3-13, >13)a   CITV grouping      >13cc  3.5-13cc  <3.5cc  Statistical significance  Age in years (mean, SD)  64.3 ± 12.0  64.8 ± 11.1  64.3 ± 10.8  P = .843  KPS (mean, SD)  77.2 ± 15.0  84.2 ± 14.5  90.8 ± 11.7  P < .001  Number of metastases (mean, SD)  7.9 ± 10.3  6.7 ± 9.3  5.7 ± 8.0  P < .001  Extracranial disease status (col %)           • PD  65.9%  65.7%  72.4%  P < .001   • PR  8.0%  12.2%  13.4%     • CR  26.1%  22.1%  14.2%    LITV in cc (mean, SD)  14.0 ± 9.5  5.4 ± 2.9  1.0 ± 0.8  P < .001    CITV grouping      >13cc  3.5-13cc  <3.5cc  Statistical significance  Age in years (mean, SD)  64.3 ± 12.0  64.8 ± 11.1  64.3 ± 10.8  P = .843  KPS (mean, SD)  77.2 ± 15.0  84.2 ± 14.5  90.8 ± 11.7  P < .001  Number of metastases (mean, SD)  7.9 ± 10.3  6.7 ± 9.3  5.7 ± 8.0  P < .001  Extracranial disease status (col %)           • PD  65.9%  65.7%  72.4%  P < .001   • PR  8.0%  12.2%  13.4%     • CR  26.1%  22.1%  14.2%    LITV in cc (mean, SD)  14.0 ± 9.5  5.4 ± 2.9  1.0 ± 0.8  P < .001  aCITV, cumulative intracranial tumor volume; cc, cubic centimeters; SD, standard deviation; KPS, Karnofsky performance score; PD, progressive disease; PR, partial remission; CR, complete remission; LITV, largest intracranial tumor volume. View Large Following our univariable analyses, we performed a series of multivariable analyses to determine the degree of prognostic significance of CITV for patient survival in the context of covariates from the SIR model. The results of our first multivariable analysis—a linear regression analysis, using the outcome of patient survival defined in months alive—are shown in Table 5. As shown in this table, CITV was found to be significantly predictive of patient survival (in months) after accounting for patient age, KPS, number of metastases, and extracranial disease status. The AIC calculated for this was lower than that of a similar model in which LITV is used in place of CITV (25501 vs 25511), indicating an improved goodness of fit over the original SIR model. Finally, a third model, in which CITV and LITV were both included, found that CITV retained its prognostic significance (P < .001), while LITV dropped out of the model as a prognostic factor (P = .173). TABLE 5. Multivariable Analysis of Survival (in months), Demonstrating Statistical Significance of CITV as Prognostic Factor After Correcting for SIR Variables in a Point-Based SIR Modela   Outcome = survival in months    CITV-modified SIR  SIR model  SIR model + CITV    Estimate  Statistical  Estimate  Statistical  Estimate  Statistical    coefficient  significance  coefficient  significance  coefficient  significance  Age grouping (per point increase)  0.91  P = .029  0.85  P = .029  0.91  P = .030  KPS grouping (per point increase)  5.1  P < .001  5.31  P < .001  5.02  P < .001  Number of metastases grouping (per point increase)  2.34  P < .001  2.69  P < .001  2.45  P < .001  Systemic disease status grouping (per point increase)  6.98  P < .001  6.97  P < .001  6.99  P < .001  LITV grouping (per point increase)    2.375  P < .001  0.85  P = .173  CITV grouping (per point increase)  2.44  P < .001    1.89  P < .001  Akaike Information Criterion (AIC)  25501  25511  25501    Outcome = survival in months    CITV-modified SIR  SIR model  SIR model + CITV    Estimate  Statistical  Estimate  Statistical  Estimate  Statistical    coefficient  significance  coefficient  significance  coefficient  significance  Age grouping (per point increase)  0.91  P = .029  0.85  P = .029  0.91  P = .030  KPS grouping (per point increase)  5.1  P < .001  5.31  P < .001  5.02  P < .001  Number of metastases grouping (per point increase)  2.34  P < .001  2.69  P < .001  2.45  P < .001  Systemic disease status grouping (per point increase)  6.98  P < .001  6.97  P < .001  6.99  P < .001  LITV grouping (per point increase)    2.375  P < .001  0.85  P = .173  CITV grouping (per point increase)  2.44  P < .001    1.89  P < .001  Akaike Information Criterion (AIC)  25501  25511  25501  aCITV, cumulative intracranial tumor volume; SIR, scored index for radiosurgery; KPS, Karnofsky performance score; LITV, largest intracranial tumor volume. View Large We performed a second multivariable analysis using a Cox proportional hazards model. The results of this analysis again showed that CITV remained a significant prognostic factor for survival after accounting for patient age, KPS, number of metastases, and extracranial disease status (Table 6). The AIC for our CITV model was lower than that of SIR (42428 vs 42445), indicating an improved goodness of fit over the original SIR model. Finally, when examining both CITV and LITV together in the same Cox model, only CITV retained its prognostic significance (P < .001), while LITV became statistically insignificant (P = .393). TABLE 6. Cox Proportional Hazards Analysis Demonstrating Prognostic Superiority of CITV Over LITV Within the SIR Modela   Outcome = death (survival time in months)    CITV-modified SIR  SIR model  SIR model + CITV    Hazard  Statistical  Hazard  Statistical  Hazard  Statistical    ratio  significance  ratio  significance  ratio  significance  Age grouping (per point increase)  0.915  P = .001  0.917  P = .001  0.914  P = .001  KPS grouping (per point increase)  0.572  P < .001  0.559  P < .001  0.574  P < .001  Number of metastases grouping (per point increase)  0.872  P < .001  0.854  P < .001  0.869  P < .001  Systemic disease status grouping (per point increase)  0.681  P < .001  0.684  P < .001  0.681  P < .001  LITV grouping (per point increase)    0.854  P < .001  0.967  P = .393  CITV grouping (per point increase)  0.842  P < .001    0.860  P < .001  Akaike Information Criterion (AIC)  42428  42445  42429    Outcome = death (survival time in months)    CITV-modified SIR  SIR model  SIR model + CITV    Hazard  Statistical  Hazard  Statistical  Hazard  Statistical    ratio  significance  ratio  significance  ratio  significance  Age grouping (per point increase)  0.915  P = .001  0.917  P = .001  0.914  P = .001  KPS grouping (per point increase)  0.572  P < .001  0.559  P < .001  0.574  P < .001  Number of metastases grouping (per point increase)  0.872  P < .001  0.854  P < .001  0.869  P < .001  Systemic disease status grouping (per point increase)  0.681  P < .001  0.684  P < .001  0.681  P < .001  LITV grouping (per point increase)    0.854  P < .001  0.967  P = .393  CITV grouping (per point increase)  0.842  P < .001    0.860  P < .001  Akaike Information Criterion (AIC)  42428  42445  42429  aCITV, cumulative intracranial tumor volume; SIR, scored index for radiosurgery; KPS, Karnofsky performance score; LITV, largest intracranial tumor volume. View Large Taken together, these results suggest that CITV allows for finer survival discrimination within each value of LITV. To confirm this hypothesis, we generated an ROC for LITV and CITV as prognostic factors for 1 yr survival after SRS treatment in a univariable analysis (Figure 1B). Consistent with our hypothesis, the AUC for CITV (red) was greater than that of LITV (black), suggesting that CITV was a superior prognostic variable. Prognostic Value of LITV Relative to CITV in the SIR Model Finally, in order to directly compare the prognostic utility of CITV to LITV within the SIR model, we determined the prognostic value of the SIR in the TS cohort and compared this value to a model where LITV was replaced with CITV using the aforementioned statistical measures: NRI > 0 and IDI. For both measures, the prognostic value of the SIR model was significantly improved when LITV was replaced with CITV. The NRI > 0 was 0.2574 (95% confidence interval [CI] 0.1890-0.3257, P < .001) when comparing the SIR model where LITV was replaced with CITV (termed CITV-modified SIR model) relative to conventional SIR model (Table 7). IDI revealed an improvement of 0.0088 (95% CI 0.0057-0.0119, P < .001) when comparing the CITV-modified SIR model relative to the conventional model. These results both support the notion that CITV is a superior prognostic factor relative to LITV. TABLE 7. Prognostic Performance of the SIR and CITV-Modified SIR Models for TS and MC Cohortsa   TS cohort Value  95% CI  ΔAUC  0.0098  P = .001  Continuous NRI > 0  0.2574  0.1890-0.3257 (P < .001)  IDI score  0.0088  0.0057-0.0119 (P < .001)    MC cohort Value  95% CI  ΔAUC  0.0093  P = .007  Continuous NRI > 0  0.2604  0.1796-0.3411 (P < .001)  IDI score  0.0051  0.0030-0.0073 (P < .001)    TS cohort Value  95% CI  ΔAUC  0.0098  P = .001  Continuous NRI > 0  0.2574  0.1890-0.3257 (P < .001)  IDI score  0.0088  0.0057-0.0119 (P < .001)    MC cohort Value  95% CI  ΔAUC  0.0093  P = .007  Continuous NRI > 0  0.2604  0.1796-0.3411 (P < .001)  IDI score  0.0051  0.0030-0.0073 (P < .001)  aΔAUC, area under curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement. View Large Result Validation in an Independent Cohort To validate our findings, we analyzed LITV and CITV in an independent cohort of 2793 patients treated by MH and CC (termed MC cohort). These analyses showed results highly comparable to those observed in the TS cohort. As was previously seen in the TS cohort, the CITV was at least 50% greater than the LITV in ∼80% of the patients in the MC cohort (Figure 2A). Similarly, CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each LITV category. Univariable analyses (Table A, Supplemental Digital Content) showed that, consistent with our TS cohort, univariable analyses of our MC cohort found that KPS, number of metastases, extracranial disease status, and LITV all differed for patients according to their CITV point grouping (ie, <3.5, 3.5-13, and >13 cc). CITV was better associated with survival than LITV in our multivariable analyses (results are shown in Tables B and C, Supplementary Digital Content). Further, our CITV-based ROC curve showed a greater AUC relative to our LITV-based ROC (Figure 2B). When LITV in the SIR was replaced with CITV, NRI > 0 showed an improvement of 0.2604 (95% CI 0.1796-0.3411, P < .001), and IDI showed an improvement of 0.0051 (95% CI 0.0030-0.0073, P < .001; Table 7). These results validate the findings observed in the TS cohort and support the superior prognostic value of CITV relative to LITV for SRS-treated brain metastasis patients. FIGURE 2. View largeDownload slide Discrepancy between CITV and LITV in the MC cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. Results were derived from the TS cohort. FIGURE 2. View largeDownload slide Discrepancy between CITV and LITV in the MC cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. Results were derived from the TS cohort. DISCUSSION Relevance of Findings We previously demonstrated the importance of CITV as a prognostic factor for SRS-treated BM patients18-20. Here we compared CITV and LITV17 in terms of their prognostic significance through a variety of statistical models, all of which demonstrate a superiority of CITV over LITV for predicting patient survival within the framework of the SIR model. Admittedly, the improvement in survival prognostication when comparing CITV and LITV was modest. However, this modest effect is to be expected since CITV includes LITV. The fundamental question is to what extent the tumor volume not accounted for by the LITV contributes to prognostication. Our study suggests these tumor volumes contribute to survival prognostication in a statistically significant albeit modest manner. To the best of our knowledge, this study is the first to address this issue. Our analysis indicates that ∼80% of the patients with comparable LITV exhibited significantly different CITV, suggesting that CITV offers a more granular stratification of the SRS-treated BM population. When LITV in the SIR scale was replaced with CITV, sensitivity and specificity of predicting 1-yr survival was significantly improved in 2 independent cohorts of ∼6000 SRS-treated BM patients. Impressively, the gain in prognostication, as measured by NRI > 0 in both cohorts, exceeds the Cohen criteria of significance11,18,30 and was on par with that observed for High-Density Lipoprotein (HDL) as a prognostic factor for myocardial infarction.31 In aggregate, these results suggest that the CITV-modified SIR model offers superior prognostic value for SRS-treated BM patients relative to the SIR model with LITV, a finding further supported by the failure of prior studies to find a significant prognostic value for LITV.16,31 Potential Explanations There are several potential explanations for the superior prognostic value of CITV relative to LITV. Clinically, the differing CITV in patients with comparable LITV may influence the maximal dose of radiation dose delivered, thereby influencing the likelihood of local control.32 For instance, in 2 patients with identical LITV, the patient with significantly higher CITV may have received lowered SRS dose due to dose constraints related to single-fraction treatment.33 Alternatively, the tumors with differing CITV may reflect an underlying biology that is not captured by LITV. For example, the sizes and size distribution of the intracranial metastases may serve as imaging proxy for the biology of the primary cancer. For instance, the natural history and therapeutic responses of primary tumors that give rise to BM consisting of 1 dominant lesion and 3 smaller lesions may be different from those that give rise to 4 similarly sized lesions. These hypotheses are not mutually exclusive and may both contribute to the observed results. Limitations There are several limitations to our study. First, our study is subject to all biases inherent in retrospective analysis. To mitigate these biases, we validated our observation using an independent cohort of patients.34 Second, the SIR model was developed without consideration for tumor histology.17,35 While examining CITV and LITV in this model afford direct comparison of these variables in an established prognostic scale,17 we were unable to examine the effects of histology using an SIR-based framework for our augmented model. Future studies to examine the impact of histology on the prognostic value of CITV are warranted. Third, we rely partly on statistical measures (namely NRI and IDI) which carry inherent limitations.36,37 While NRI and IDI metrics allow for direct quantifiable comparison of the 2 different prognostic scales at hand in this study (SIR and CITV-modified SIR), both measures require a binary outcome in order to compare the SIR to our CITV-modified SIR. That said, the results of our multivariable models (using survival as a continuous variable) and AIC calculations highlight the prognostic superiority of CITV over LITV. Finally, our study fails to consider the survival impact of molecularly targeted therapy, immunotherapy, or the genetic mutation profile of the tumor. To the extent, survival expectations are increasingly modified by these agents, future studies should be devoted to study the interaction between CITV and these agents. CONCLUSION In 2 independent patient cohorts totaling ∼6000 patients, CITV consistently outperforms LITV as a prognostic variable when applied in the context of the SIR model. Disclosures The project described was partially supported by the National Institutes of Health (NIH), Grant 1TL1TR001443. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. There are no other financial disclosures relevant to this manuscript. The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. REFERENCES 1. Patchell RA. The management of brain metastases. Cancer Treat Rev . 2003; 29( 6): 533- 540. Google Scholar CrossRef Search ADS PubMed  2. Eichler AF, Loeffler JS. Multidisciplinary management of brain metastases. Oncologist . 2007; 12( 7): 884- 898. Google Scholar CrossRef Search ADS PubMed  3. Gaspar LE, Mehta MP, Patchell RA et al.   The role of whole brain radiation therapy in the management of newly diagnosed brain metastases: a systematic review and evidence-based clinical practice guideline. J Neurooncol . 2010; 96( 1): 17- 32. Google Scholar CrossRef Search ADS PubMed  4. Gondi V, Wolfgang TA, Mehta MP. Why avoid the hippocampus? A comprehensive review. Radiother Oncol . 2010; 97( 3): 370- 376. Google Scholar CrossRef Search ADS PubMed  5. 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Int J Radiat Oncol Biol Phys . 1999; 43( 3): 549- 558. Google Scholar CrossRef Search ADS PubMed  9. Halasz LM, Weeks JC, Neville BA, Taback N, Punglia RS. Use of stereotactic radiosurgery for brain metastases from non-small cell lung cancer in the United States. Int J Radiat Oncol Biol Phys . 2013; 85( 2): e109- e116. Google Scholar CrossRef Search ADS PubMed  10. Preusser M, Capper D, Ilhan-Mutlu A et al.   Brain metastases: pathobiology and emerging targeted therapies. Acta Neuropathol . 2012; 123( 2): 205- 222. Google Scholar CrossRef Search ADS PubMed  11. Cohen JV, Kluger HM. Systemic immunotherapy for the treatment of brain metastases. Front Oncol . 2016; 6: 49. doi:10.3389/fonc.2016.00049. Google Scholar CrossRef Search ADS PubMed  12. Nussbaum ES, Djalilian HR, Cho KH, Hall WA. Brain metastases: histology, multiplicity, surgery, and survival. Cancer . 1996; 78( 8): 1781- 1788. Google Scholar CrossRef Search ADS PubMed  13. Langer CJ, Mehta MP. Current management of brain metastases, with a focus on systemic options. J Clin Oncol . 2005; 23( 25): 6207- 6219. Google Scholar CrossRef Search ADS PubMed  14. Pollock BE, Brown PD, Foote RL, Stafford SL, Schomberg PJ. Properly selected patients with multiple brain metastases may benefit from aggressive treatment of their intracranial disease. [Review] [32 refs]. J Neurooncol . 2003; 61( 1): 73- 80. Google Scholar CrossRef Search ADS PubMed  15. Gaspar L, Scott C, Rotman M et al.   Recursive partitioning analysis (RPA) of prognostic factors in three Radiation Therapy Oncology Group (RTOG) brain metastases trials. Int J Radiat Oncol Biol Phys . 1997; 37( 4): 745- 751. Google Scholar CrossRef Search ADS PubMed  16. Sperduto PW, Berkey B, Gaspar LE, Mehta M, Curran W. A new prognostic index and comparison to three other indices for patients with brain metastases: an analysis of 1,960 patients in the RTOG database. Int J Radiat Oncol Biol Phys . 2008; 70( 2): 510- 514. Google Scholar CrossRef Search ADS PubMed  17. Weltman E, Salvajoli JV, De Oliveira Borges SR, Toledo R, Anselmo R, Wajsbrot DB. Score index for stereotactic radiosurgery of brain metastases. J Radiosurgery . 1998; 1( 2): 89- 97. Google Scholar CrossRef Search ADS   18. Marcus LP, Marshall D, Hirshman BR et al.   Cumulative intracranial tumor volume (CITV) enhances the prognostic value of the lung-specific graded prognostic assessment (GPA) model. Neurosurgery . 2016; 79( 2): 246- 252. doi:10.1227/NEU.0000000000001123. CrossRef Search ADS PubMed  19. Gonda DD, Kim TE, Goetsch SJ et al.   Prognostic factors for stereotactic radiosurgery-treated patients with cerebral metastasis: implications on randomised control trial design and inter-institutional collaboration. Eur J Cancer . 2014; 50( 6): 1148- 1158. Google Scholar CrossRef Search ADS PubMed  20. Marshall DC, Marcus LP, Kim TE et al.   Management patterns of patients with cerebral metastases who underwent multiple stereotactic radiosurgeries. J Neurooncol . 2016. doi:10.1007/s11060-016-2084-2. 21. Obuchowski NA, Lieber ML, Wians FH. ROC curves in clinical chemistry: uses, misuses, and possible solutions. Clin Chem . 2004; 50( 7): 1118- 1125. Google Scholar CrossRef Search ADS PubMed  22. Serizawa T, Higuchi Y, Nagano O et al.   A new grading system focusing on neurological outcomes for brain metastases treated with stereotactic radiosurgery: the modified basic score for brain metastases. J Neurosurg . 2014; 121( suppl): 35- 43. Google Scholar PubMed  23. Nabors LB, Portnow J, Ammirati M et al.   Central nervous system cancers, version 2.2014: featured updates to the NCCN guidelines. J Natl Compr Canc Netw . 2014; 12( 11): 1517- 1523. Google Scholar CrossRef Search ADS PubMed  24. Pan H, Cerviño LI, Pawlicki T et al.   Frameless, real-time, surface imaging-guided radiosurgery: clinical outcomes for brain metastases. Neurosurgery . 2012; 71( 4): 844- 851. Google Scholar CrossRef Search ADS PubMed  25. Kaal EC, Niël CG, Vecht CJ. Therapeutic management of brain metastasis. Lancet Neurol . 2005; 4( 5): 289- 298. doi:10.1016/S1474-4422(05)70072-7. Google Scholar CrossRef Search ADS PubMed  26. Burnham KP, Anderson DR, eds. Model Selection and Multimodel Inference . New York, NY: Springer; 2004. doi:10.1007/b97636. Google Scholar CrossRef Search ADS   27. Rava P, Leonard K, Sioshansi S et al.   Survival among patients with 10 or more brain metastases treated with stereotactic radiosurgery. J Neurosurg . 2013; 119( 2): 1- 6. Google Scholar CrossRef Search ADS   28. Joshi R, Johnson MD, Maitz A, Marvin KS, Olson RE, Grills IS. Utility of graded prognostic assessment in evaluation of patients with brainstem metastases treated with radiosurgery. Clin Neurol Neurosurg . 2016; 147: 30- 33. doi:10.1016/j.clineuro.2016.05.001. Google Scholar CrossRef Search ADS PubMed  29. Trifiletti DM, Lee C-C, Kano H et al.   Stereotactic radiosurgery for brainstem metastases: an international cooperative study to define response and toxicity. Int J Radiat Oncol Biol Phys . 2016; 96( 2): 280- 288. Google Scholar CrossRef Search ADS PubMed  30. Pencina MJ, D’Agostino RB, Pencina KM, Janssens AC, Greenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol . 2012; 176( 6): 473- 481. Google Scholar CrossRef Search ADS PubMed  31. Cooney MT, Dudina A, De Bacquer D et al.   How much does HDL cholesterol add to risk estimation? A report from the SCORE investigators. Eur J Cardiovasc Prev Rehabil . 2009; 16( 3): 304- 314. Google Scholar CrossRef Search ADS PubMed  32. Shiau CY, Sneed PK, Shu HKG et al.   Radiosurgery for brain metastases: relationship of dose and pattern of enhancement to local control. Int J Radiat Oncol Biol Phys . 1997; 37( 2): 375- 383. Google Scholar CrossRef Search ADS PubMed  33. Kim YJ, Cho KH, Kim JY et al.   Single-dose versus fractionated stereotactic radiotherapy for brain metastases. Int J Radiat Oncol Biol Phys . 2011; 81( 2): 483- 489. Google Scholar CrossRef Search ADS PubMed  34. Toll DB, Janssen KJM, Vergouwe Y, Moons KGM. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol . 2008; 61( 11): 1085- 1094. Google Scholar CrossRef Search ADS PubMed  35. Sperduto PW, Chao ST, Sneed PK et al.   Diagnosis-specific prognostic factors, indexes, and treatment outcomes for patients with newly diagnosed brain metastases: a multi-institutional analysis of 4,259 patients. Int J Radiat Oncol Biol Phys . 2010; 77( 3): 655- 661. Google Scholar CrossRef Search ADS PubMed  36. Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS. Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology . 2014; 25( 1): 114- 121. Google Scholar CrossRef Search ADS PubMed  37. Hilden J, Gerds TA. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med . 2014; 33( 19): 3405- 3414. Google Scholar CrossRef Search ADS PubMed  Supplemental digital content is available for this article at www.neurosurgery-online.com. Copyright © 2017 by the Congress of Neurological Surgeons http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neurosurgery Oxford University Press

Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest Intracranial Tumor Volume for Stereotactic Radiosurgery-Treated Brain Metastasis Patients

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Oxford University Press
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Copyright © 2017 by the Congress of Neurological Surgeons
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0148-396X
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1524-4040
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10.1093/neuros/nyx225
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Abstract

Abstract BACKGROUND Two intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV). OBJECTIVE To determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components—LITV—with CITV. METHODS We compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients. RESULTS In both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model. CONCLUSION After accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients. Brain metastases, Cumulative intracranial tumor volume, Net reclassification improvement, Prognostic model, Radiosurgery ABBREVIATIONS ABBREVIATIONS AIC Akaike Information Criterion BM brain metastases CI confidence interval CITV cumulative intracranial tumor volume IDI integrated discrimination improvement KPS Karnofsky Performance Score LITV largest intracranial tumor NIH National Institutes of Health ROC receiver operating characteristic SIR scored index for radiosurgery SRS stereotactic radiosurgery WBRT whole brain radiation therapy NRI net reclassification improvement Recent estimates suggest that 25% to 45% of the cancer patient population will develop brain metastases (BM).1 Because most chemotherapeutic agents are unable to penetrate the blood–brain barrier, surgery and radiation are the mainstay treatments for BM.2 For surgically inaccessible BMs or small BMs without associated neurological deficits, radiation is the preferred treatment. Radiation delivery can be achieved by use of whole brain radiation therapy (WBRT),3 hippocampal sparing WBRT,4 and stereotactic radiosurgery (SRS).5 Because of potential deleterious neurocognitive effects associated with WBRT,6 SRS is increasingly utilized for BM patients with a limited number of metastases.7,8 Based on the Surveillance Epidemiology and End Results multistate cancer registry, SRS as treatment for BM has more than tripled in the past decade.9 While recent studies show increased survival for a subset of patients with BMs after the introduction of molecularly targeted therapies10 and immunotherapies,11 the survival expectation for patients with BM as a whole remains poor.12 With an expected median survival of 2 to 7 mo for all BM patients,2,13 a major challenge in clinical management involves weighing the inherent risks of potential intervention relative to survival expectation.14 In this context, several prognostic scales have been developed for patients afflicted with BM.15-17 While the specifics of these scales differ, they generally involve the same clinical variables, including age, Karnofsky Performance Score (KPS), extracranial disease status, histology, and the number of metastases.15-17 Intracranial tumor volume is an important prognostic factor for SRS-treated BM patients.17-20 The first prognostic scale to acknowledge the importance of intracranial tumor volume is termed Scored Index for Radiosurgery (SIR). SIR included 5 prognostic factors, including age, KPS, extracranial disease status, number of brain lesions, and volume of the largest intracranial tumor (LITV).17 Our previous studies suggested that the cumulative intracranial tumor volume (CITV), or the sum volume of all intracranial metastases, may also serve as a critical prognostic factor for SRS-treated BM patients.18-20 Here, we utilized the framework established by the SIR to test the prognostic performance of CITV relative to LITV. METHODS Study Cohorts All data collection was performed under Institutional Review Board approved retrospective review, which included a waiver of informed consent. The first dataset consists of 3061 consecutive BM patients treated by TS (1998-2014). This study population is termed the “TS cohort”. A posthoc sample size analysis of the TS cohort receiver operating characteristic (ROC) curves indicated that a minimum of 2055 patients would be necessary for a power of 0.8021 to detect the effect size seen in the cohort. To ensure our validation cohort was sufficiently powered to detect a difference of similar magnitude, we combined the cohorts of patients treated by MH and CC (1994-2014) to a resulting data set of 2793 patients. This study population is termed the “MC” cohort. Details of these cohorts have been previously published.18-20,22 In brief, every patient had a primary tumor confirmed outside of the central nervous system and underwent SRS without craniotomy. All patients were referred for SRS by a radiation or medical oncologist as well as a neurosurgeon. A multidisciplinary tumor board reviewed each of the SRS candidates. Patients who were recommended and underwent surgical resection were not included in the present analysis. For the purposes of our analyses, patients who underwent multiple radiosurgery treatments (n = 273—4.7% of all analyzed patients) were labeled as new patients, based on previously published findings that survival is comparable between patients receiving a singular round of SRS vs those receiving multiple SRS treatments.20 Decisions to pursue SRS vs WBRT were personalized based on the patient's general medical status as well as the status of their oncologic disease. In general, the clinical practice pattern of the 3 treating surgeons conform to generally accepted guidelines, which include reserving WBRT for those with prohibitively large tumor volumes, those unable to follow up with surveillance MRIs, those with miliary disease, and those receiving radiation for the sole purpose of palliation.23-25 Patient baseline characteristics for each data set are available in Table 1. SRS was performed as previously described.18 TABLE 1. Demographics and Clinical Characteristic of Study Patientsa   TS cohort  MC cohort  Number of patients  3061  2793  Socio-demographics       Age (mean, SD)  64.5 ± 11.1  63.2 ± 12.0   Sex (% male)  59.2%  58.9%  Patient properties       KPS (mean, SD)  85.5 ± 14.4  85.0 ± 13.4   Number of metastases (mean, SD)  6.6 ± 9.0  5.7 ± 8.9   Active extracranial disease (%)  68.6%  85.6%   Largest lesion (cc; mean, SD)  5.3 ± 6.8  6.4 ± 8.9   CITV (cc; mean, SD)  8.3 ± 10.2  8.7 ± 11.8  Type of cancer (%)       Breast  11.0%  11.9%   Gastrointestinal  12.7%  9.1%   Lung  65.5%  61.6%   Urogenital  6.2%  4.8%   Other  4.6%  12.6%  Survival       Overall survival (years) (mean, SD)  1.1 ± 1.4  0.9 ± 1.4   Patients with minimum 1 yr survival  33.4%  27.6%    TS cohort  MC cohort  Number of patients  3061  2793  Socio-demographics       Age (mean, SD)  64.5 ± 11.1  63.2 ± 12.0   Sex (% male)  59.2%  58.9%  Patient properties       KPS (mean, SD)  85.5 ± 14.4  85.0 ± 13.4   Number of metastases (mean, SD)  6.6 ± 9.0  5.7 ± 8.9   Active extracranial disease (%)  68.6%  85.6%   Largest lesion (cc; mean, SD)  5.3 ± 6.8  6.4 ± 8.9   CITV (cc; mean, SD)  8.3 ± 10.2  8.7 ± 11.8  Type of cancer (%)       Breast  11.0%  11.9%   Gastrointestinal  12.7%  9.1%   Lung  65.5%  61.6%   Urogenital  6.2%  4.8%   Other  4.6%  12.6%  Survival       Overall survival (years) (mean, SD)  1.1 ± 1.4  0.9 ± 1.4   Patients with minimum 1 yr survival  33.4%  27.6%  acc, cubic centimeters; CITV, cumulative intracranial tumor volume; KPS, Karnofsky performance score; SD, standard deviation. View Large Radiosurgery Parameters and Overall Survival In both cohorts, in-house electronic medical record data were gathered, including age, KPS, extracranial disease status, number of BM, CITV, LITV, and overall survival. Only patients for whom each of these variables were known were analyzed; patients with any missing data (515 of 6369, or 8.1%) were excluded for the purposes of our analyses. We defined overall survival as the time (in months) from SRS to time of death. LITV was defined as the largest contiguous lesion present on the pre-SRS (T1-weighted postcontrast image) as calculated by the Elekta's Gamma Plan software. As previously published,18 CITV was defined as the sum volume of all treated lesions, such that in patients with a single lesion, the CITV and LITV values were identical. To facilitate comparison of the prognostic value of LITV and CITV, we set out to compare the sensitivity and specificity of 1-yr survival prognostication by SIR relative to a model where LITV was replaced with CITV (termed CITV-modified SIR). The SIR scale (termed SIR model) used in this study is as originally proposed17 and included age, KPS, number of metastases, extracranial disease status, and LITV (see Table 2). We used the optimized cut-offs of LITV (<5, 5-13, and >13 cc) as previously reported by Weltman et al.17 For the CITV-modified SIR model, all variables of SIR were included except that LITV was replaced with CITV. The optimized CITV cut-points (<3.5, 3.5-13, and >13 cc) were derived using methods similar to those used by Weltman et al17 for LITV (see Table 2). Median survival time (in months) for each of the point groupings is shown in Table 3. TABLE 2. Scoring Components for SIR- and CITV-Modified SIR Modelsa   SIR model  CITV-modified SIR model    0 points  1 point  2 points  0 points  1 point  2 points  Age  ≥60 yr  51-59  ≤50 yr  ≥60 yr  51-59  ≤50 yr  KPS  ≤50  60-70  ≥80  ≤50  60-70  ≥80  Number of lesions  ≥3  2  1  ≥3  2  1  Extracranial disease status  PD  PR  CR  PD  PR  CR  Largest lesion size  <5 cc  5-13 cc  >13 cc  –  –  –  CITV  –  –  –  <3.5 cc  3.5-13 cc  >13 cc    SIR model  CITV-modified SIR model    0 points  1 point  2 points  0 points  1 point  2 points  Age  ≥60 yr  51-59  ≤50 yr  ≥60 yr  51-59  ≤50 yr  KPS  ≤50  60-70  ≥80  ≤50  60-70  ≥80  Number of lesions  ≥3  2  1  ≥3  2  1  Extracranial disease status  PD  PR  CR  PD  PR  CR  Largest lesion size  <5 cc  5-13 cc  >13 cc  –  –  –  CITV  –  –  –  <3.5 cc  3.5-13 cc  >13 cc  aKPS, Karnofsky performance score; PD, progressive disease; PR, partial remission; CR, complete remission; cc, cubic centimeters; CITV, cumulative intracranial tumor volume; SIR, scored index for radiosurgery. View Large TABLE 3. Median Survival Time (in months) of SIR- and CITV-Modified SIR Point Groupingsa Point total  0  1  2  3  4  5  6  7  8  9  10  SIR model  2.7  2.2  3.4  4.3  7.0  7.7  9.5  13.6  21.6  23.3  42.0  CITV-SIR model  2.5  2.6  3.8  5.1  7.3  8.4  10.6  15.3  21.2  27.4  42.0  Point total  0  1  2  3  4  5  6  7  8  9  10  SIR model  2.7  2.2  3.4  4.3  7.0  7.7  9.5  13.6  21.6  23.3  42.0  CITV-SIR model  2.5  2.6  3.8  5.1  7.3  8.4  10.6  15.3  21.2  27.4  42.0  aSIR, scored index for radiosurgery; CITV, cumulative intracranial tumor volume. View Large Statistical Methods In order to justify direct comparison of CITV to LITV within the SIR model, we first needed to identify CITV as an independent predictor of survival. This was achieved by first performing a series of univariable analyses comparing the proportions and distributions of each SIR factor (ie, age, KPS, extracranial disease status, number of BM, LITV) across CITV groupings, to determine the extent of variability amongst SIR factors within distinct CITV groupings. Continuous factors were compared using ANOVA tests, while categorical factors were compared using chi-square tests. We next performed both a multivariable linear regression analysis and Cox proportional analysis to examine the predictive significance of CITV for patient survival (in months) after accounting for the remaining SIR factors (age, KPS, extracranial disease status, and number of BM). For relative comparison, the same set of multivariable analyses were performed examining the predictive capacity of LITV (ie, using the original SIR model factors). To more thoroughly compare the prognostic significance of CITV to LITV, a final set of multivariable analyses (linear regression and Cox proportional hazards) were performed including both CITV and LITV as predictors against the outcome of continuous survival; these models were examined to determine which variables retained prognostic significance upon the inclusion of both CITV and LITV. For all multivariable models, the Akaike Information Criterion (AIC) was calculated, such that the goodness of fit could be compared between models which included CITV vs LITV. In brief, the AIC allows for the quantification of a model's goodness of fit while at the same time penalizing models that are over-fit and would limit the external validity of the model to new data (ie, other patients not contained within the dataset). When comparing 2 models, a lower AIC indicates an improved goodness of fit; a reduction in AIC of at least 2 is necessary for one model to be deemed superior to the other.26 Finally, to directly compare the prognostic value of our CITV-modified SIR model to the traditional SIR model, we relied on the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices, as well as the calculated change in area under the ROC curve (ΔAUC) to determine the extent of prognostic improvement afforded by the replacement of LITV with CITV within the SIR model. In each case, patient survival was defined according to the discrete time point of 12 mo. This time point was chosen in part due to its prevalent usage in the literature.27-29 Calculating ΔAUC allowed for comparison of true positive and false positive rates for a given test as a whole. Calculating NRI and IDI helps quantify the number of correctly reassigned cases (ie, false negative to true negative, false positive to true positive) after implementing a change in the model such as the replacement of LITV with CITV. Taken together, all 3 values provided complementary information as to the relative improvement in a prognostic model which may result from a change in one (or more) of its variables.30 Statistical comparisons were performed with R version 3.2.3 and the predictABEL package for ΔAUC, NRI, and IDI calculations. R’s plotROC package was used to plot the ROC curves. All P < .05 were considered statistically significant. RESULTS Comparisons of LITV and CITV Detailed descriptions of the 2 study cohorts (TS and MC) have been previously published.18-20,22 Pertinent demographic and clinical variables pertaining to these cohorts are shown in Table 1. Both cohorts showed comparable demographics, with similar distribution in age (mean age of 64.5 and 63.2) and sex (59.2% and 58.9% male). The distribution in cancer types was also similar, with a predominance of lung cancer patients in both cohorts (65.5% and 61.6% for TS and MC cohorts, respectively). We first characterized relative distribution of LITV and CITV for each SRS-treated patient in the TS cohort. We found that LITV and CITV differed significantly for the majority of the patients. The CITV was at least 50% greater than the LITV in ∼80% of the patients (Figure 1A). CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV. These results suggest that the majority of the BM patients treated harbored multiple intracranial lesions. Moreover, the sum volume of the intracranial lesions (CITV) differed significantly from that of the largest tumor volume (8.33 vs 5.33 cc, P < .001). FIGURE 1. View largeDownload slide Discrepancy between CITV and LITV in the TS cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. FIGURE 1. View largeDownload slide Discrepancy between CITV and LITV in the TS cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. Prognostic Value of CITV The results of our univariable analyses are shown in Table 4. As shown in this table, KPS, extracranial disease status, number of lesions, and LITV were all found to differ for patients according to their CITV point grouping (ie, <3.5, 3.5-13, >13 cc). Only age was found to not significantly differ according to CITV point grouping. TABLE 4. Univariable Analysis of Each SIR Factor as Distributed Across CITV Groupings (<3.5, 3-13, >13)a   CITV grouping      >13cc  3.5-13cc  <3.5cc  Statistical significance  Age in years (mean, SD)  64.3 ± 12.0  64.8 ± 11.1  64.3 ± 10.8  P = .843  KPS (mean, SD)  77.2 ± 15.0  84.2 ± 14.5  90.8 ± 11.7  P < .001  Number of metastases (mean, SD)  7.9 ± 10.3  6.7 ± 9.3  5.7 ± 8.0  P < .001  Extracranial disease status (col %)           • PD  65.9%  65.7%  72.4%  P < .001   • PR  8.0%  12.2%  13.4%     • CR  26.1%  22.1%  14.2%    LITV in cc (mean, SD)  14.0 ± 9.5  5.4 ± 2.9  1.0 ± 0.8  P < .001    CITV grouping      >13cc  3.5-13cc  <3.5cc  Statistical significance  Age in years (mean, SD)  64.3 ± 12.0  64.8 ± 11.1  64.3 ± 10.8  P = .843  KPS (mean, SD)  77.2 ± 15.0  84.2 ± 14.5  90.8 ± 11.7  P < .001  Number of metastases (mean, SD)  7.9 ± 10.3  6.7 ± 9.3  5.7 ± 8.0  P < .001  Extracranial disease status (col %)           • PD  65.9%  65.7%  72.4%  P < .001   • PR  8.0%  12.2%  13.4%     • CR  26.1%  22.1%  14.2%    LITV in cc (mean, SD)  14.0 ± 9.5  5.4 ± 2.9  1.0 ± 0.8  P < .001  aCITV, cumulative intracranial tumor volume; cc, cubic centimeters; SD, standard deviation; KPS, Karnofsky performance score; PD, progressive disease; PR, partial remission; CR, complete remission; LITV, largest intracranial tumor volume. View Large Following our univariable analyses, we performed a series of multivariable analyses to determine the degree of prognostic significance of CITV for patient survival in the context of covariates from the SIR model. The results of our first multivariable analysis—a linear regression analysis, using the outcome of patient survival defined in months alive—are shown in Table 5. As shown in this table, CITV was found to be significantly predictive of patient survival (in months) after accounting for patient age, KPS, number of metastases, and extracranial disease status. The AIC calculated for this was lower than that of a similar model in which LITV is used in place of CITV (25501 vs 25511), indicating an improved goodness of fit over the original SIR model. Finally, a third model, in which CITV and LITV were both included, found that CITV retained its prognostic significance (P < .001), while LITV dropped out of the model as a prognostic factor (P = .173). TABLE 5. Multivariable Analysis of Survival (in months), Demonstrating Statistical Significance of CITV as Prognostic Factor After Correcting for SIR Variables in a Point-Based SIR Modela   Outcome = survival in months    CITV-modified SIR  SIR model  SIR model + CITV    Estimate  Statistical  Estimate  Statistical  Estimate  Statistical    coefficient  significance  coefficient  significance  coefficient  significance  Age grouping (per point increase)  0.91  P = .029  0.85  P = .029  0.91  P = .030  KPS grouping (per point increase)  5.1  P < .001  5.31  P < .001  5.02  P < .001  Number of metastases grouping (per point increase)  2.34  P < .001  2.69  P < .001  2.45  P < .001  Systemic disease status grouping (per point increase)  6.98  P < .001  6.97  P < .001  6.99  P < .001  LITV grouping (per point increase)    2.375  P < .001  0.85  P = .173  CITV grouping (per point increase)  2.44  P < .001    1.89  P < .001  Akaike Information Criterion (AIC)  25501  25511  25501    Outcome = survival in months    CITV-modified SIR  SIR model  SIR model + CITV    Estimate  Statistical  Estimate  Statistical  Estimate  Statistical    coefficient  significance  coefficient  significance  coefficient  significance  Age grouping (per point increase)  0.91  P = .029  0.85  P = .029  0.91  P = .030  KPS grouping (per point increase)  5.1  P < .001  5.31  P < .001  5.02  P < .001  Number of metastases grouping (per point increase)  2.34  P < .001  2.69  P < .001  2.45  P < .001  Systemic disease status grouping (per point increase)  6.98  P < .001  6.97  P < .001  6.99  P < .001  LITV grouping (per point increase)    2.375  P < .001  0.85  P = .173  CITV grouping (per point increase)  2.44  P < .001    1.89  P < .001  Akaike Information Criterion (AIC)  25501  25511  25501  aCITV, cumulative intracranial tumor volume; SIR, scored index for radiosurgery; KPS, Karnofsky performance score; LITV, largest intracranial tumor volume. View Large We performed a second multivariable analysis using a Cox proportional hazards model. The results of this analysis again showed that CITV remained a significant prognostic factor for survival after accounting for patient age, KPS, number of metastases, and extracranial disease status (Table 6). The AIC for our CITV model was lower than that of SIR (42428 vs 42445), indicating an improved goodness of fit over the original SIR model. Finally, when examining both CITV and LITV together in the same Cox model, only CITV retained its prognostic significance (P < .001), while LITV became statistically insignificant (P = .393). TABLE 6. Cox Proportional Hazards Analysis Demonstrating Prognostic Superiority of CITV Over LITV Within the SIR Modela   Outcome = death (survival time in months)    CITV-modified SIR  SIR model  SIR model + CITV    Hazard  Statistical  Hazard  Statistical  Hazard  Statistical    ratio  significance  ratio  significance  ratio  significance  Age grouping (per point increase)  0.915  P = .001  0.917  P = .001  0.914  P = .001  KPS grouping (per point increase)  0.572  P < .001  0.559  P < .001  0.574  P < .001  Number of metastases grouping (per point increase)  0.872  P < .001  0.854  P < .001  0.869  P < .001  Systemic disease status grouping (per point increase)  0.681  P < .001  0.684  P < .001  0.681  P < .001  LITV grouping (per point increase)    0.854  P < .001  0.967  P = .393  CITV grouping (per point increase)  0.842  P < .001    0.860  P < .001  Akaike Information Criterion (AIC)  42428  42445  42429    Outcome = death (survival time in months)    CITV-modified SIR  SIR model  SIR model + CITV    Hazard  Statistical  Hazard  Statistical  Hazard  Statistical    ratio  significance  ratio  significance  ratio  significance  Age grouping (per point increase)  0.915  P = .001  0.917  P = .001  0.914  P = .001  KPS grouping (per point increase)  0.572  P < .001  0.559  P < .001  0.574  P < .001  Number of metastases grouping (per point increase)  0.872  P < .001  0.854  P < .001  0.869  P < .001  Systemic disease status grouping (per point increase)  0.681  P < .001  0.684  P < .001  0.681  P < .001  LITV grouping (per point increase)    0.854  P < .001  0.967  P = .393  CITV grouping (per point increase)  0.842  P < .001    0.860  P < .001  Akaike Information Criterion (AIC)  42428  42445  42429  aCITV, cumulative intracranial tumor volume; SIR, scored index for radiosurgery; KPS, Karnofsky performance score; LITV, largest intracranial tumor volume. View Large Taken together, these results suggest that CITV allows for finer survival discrimination within each value of LITV. To confirm this hypothesis, we generated an ROC for LITV and CITV as prognostic factors for 1 yr survival after SRS treatment in a univariable analysis (Figure 1B). Consistent with our hypothesis, the AUC for CITV (red) was greater than that of LITV (black), suggesting that CITV was a superior prognostic variable. Prognostic Value of LITV Relative to CITV in the SIR Model Finally, in order to directly compare the prognostic utility of CITV to LITV within the SIR model, we determined the prognostic value of the SIR in the TS cohort and compared this value to a model where LITV was replaced with CITV using the aforementioned statistical measures: NRI > 0 and IDI. For both measures, the prognostic value of the SIR model was significantly improved when LITV was replaced with CITV. The NRI > 0 was 0.2574 (95% confidence interval [CI] 0.1890-0.3257, P < .001) when comparing the SIR model where LITV was replaced with CITV (termed CITV-modified SIR model) relative to conventional SIR model (Table 7). IDI revealed an improvement of 0.0088 (95% CI 0.0057-0.0119, P < .001) when comparing the CITV-modified SIR model relative to the conventional model. These results both support the notion that CITV is a superior prognostic factor relative to LITV. TABLE 7. Prognostic Performance of the SIR and CITV-Modified SIR Models for TS and MC Cohortsa   TS cohort Value  95% CI  ΔAUC  0.0098  P = .001  Continuous NRI > 0  0.2574  0.1890-0.3257 (P < .001)  IDI score  0.0088  0.0057-0.0119 (P < .001)    MC cohort Value  95% CI  ΔAUC  0.0093  P = .007  Continuous NRI > 0  0.2604  0.1796-0.3411 (P < .001)  IDI score  0.0051  0.0030-0.0073 (P < .001)    TS cohort Value  95% CI  ΔAUC  0.0098  P = .001  Continuous NRI > 0  0.2574  0.1890-0.3257 (P < .001)  IDI score  0.0088  0.0057-0.0119 (P < .001)    MC cohort Value  95% CI  ΔAUC  0.0093  P = .007  Continuous NRI > 0  0.2604  0.1796-0.3411 (P < .001)  IDI score  0.0051  0.0030-0.0073 (P < .001)  aΔAUC, area under curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement. View Large Result Validation in an Independent Cohort To validate our findings, we analyzed LITV and CITV in an independent cohort of 2793 patients treated by MH and CC (termed MC cohort). These analyses showed results highly comparable to those observed in the TS cohort. As was previously seen in the TS cohort, the CITV was at least 50% greater than the LITV in ∼80% of the patients in the MC cohort (Figure 2A). Similarly, CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each LITV category. Univariable analyses (Table A, Supplemental Digital Content) showed that, consistent with our TS cohort, univariable analyses of our MC cohort found that KPS, number of metastases, extracranial disease status, and LITV all differed for patients according to their CITV point grouping (ie, <3.5, 3.5-13, and >13 cc). CITV was better associated with survival than LITV in our multivariable analyses (results are shown in Tables B and C, Supplementary Digital Content). Further, our CITV-based ROC curve showed a greater AUC relative to our LITV-based ROC (Figure 2B). When LITV in the SIR was replaced with CITV, NRI > 0 showed an improvement of 0.2604 (95% CI 0.1796-0.3411, P < .001), and IDI showed an improvement of 0.0051 (95% CI 0.0030-0.0073, P < .001; Table 7). These results validate the findings observed in the TS cohort and support the superior prognostic value of CITV relative to LITV for SRS-treated brain metastasis patients. FIGURE 2. View largeDownload slide Discrepancy between CITV and LITV in the MC cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. Results were derived from the TS cohort. FIGURE 2. View largeDownload slide Discrepancy between CITV and LITV in the MC cohort. A, Histogram showing the number of patients (y-axis) with a given largest lesion size (x-axis). Relative increase of CITV to largest lesion was color coded such that increasing intensity reflects greater discrepancy between CITV and LITV. CITV was as high as 400% of LITV for 10% to 30% of the SRS-treated BM patients for each category of LITV (shown in 1 cc increments on the x-axis). B, ROC curve showing increased AUC for CITV relative to LITV for prognosticating 1-yr survival. Results were derived from the TS cohort. DISCUSSION Relevance of Findings We previously demonstrated the importance of CITV as a prognostic factor for SRS-treated BM patients18-20. Here we compared CITV and LITV17 in terms of their prognostic significance through a variety of statistical models, all of which demonstrate a superiority of CITV over LITV for predicting patient survival within the framework of the SIR model. Admittedly, the improvement in survival prognostication when comparing CITV and LITV was modest. However, this modest effect is to be expected since CITV includes LITV. The fundamental question is to what extent the tumor volume not accounted for by the LITV contributes to prognostication. Our study suggests these tumor volumes contribute to survival prognostication in a statistically significant albeit modest manner. To the best of our knowledge, this study is the first to address this issue. Our analysis indicates that ∼80% of the patients with comparable LITV exhibited significantly different CITV, suggesting that CITV offers a more granular stratification of the SRS-treated BM population. When LITV in the SIR scale was replaced with CITV, sensitivity and specificity of predicting 1-yr survival was significantly improved in 2 independent cohorts of ∼6000 SRS-treated BM patients. Impressively, the gain in prognostication, as measured by NRI > 0 in both cohorts, exceeds the Cohen criteria of significance11,18,30 and was on par with that observed for High-Density Lipoprotein (HDL) as a prognostic factor for myocardial infarction.31 In aggregate, these results suggest that the CITV-modified SIR model offers superior prognostic value for SRS-treated BM patients relative to the SIR model with LITV, a finding further supported by the failure of prior studies to find a significant prognostic value for LITV.16,31 Potential Explanations There are several potential explanations for the superior prognostic value of CITV relative to LITV. Clinically, the differing CITV in patients with comparable LITV may influence the maximal dose of radiation dose delivered, thereby influencing the likelihood of local control.32 For instance, in 2 patients with identical LITV, the patient with significantly higher CITV may have received lowered SRS dose due to dose constraints related to single-fraction treatment.33 Alternatively, the tumors with differing CITV may reflect an underlying biology that is not captured by LITV. For example, the sizes and size distribution of the intracranial metastases may serve as imaging proxy for the biology of the primary cancer. For instance, the natural history and therapeutic responses of primary tumors that give rise to BM consisting of 1 dominant lesion and 3 smaller lesions may be different from those that give rise to 4 similarly sized lesions. These hypotheses are not mutually exclusive and may both contribute to the observed results. Limitations There are several limitations to our study. First, our study is subject to all biases inherent in retrospective analysis. To mitigate these biases, we validated our observation using an independent cohort of patients.34 Second, the SIR model was developed without consideration for tumor histology.17,35 While examining CITV and LITV in this model afford direct comparison of these variables in an established prognostic scale,17 we were unable to examine the effects of histology using an SIR-based framework for our augmented model. Future studies to examine the impact of histology on the prognostic value of CITV are warranted. Third, we rely partly on statistical measures (namely NRI and IDI) which carry inherent limitations.36,37 While NRI and IDI metrics allow for direct quantifiable comparison of the 2 different prognostic scales at hand in this study (SIR and CITV-modified SIR), both measures require a binary outcome in order to compare the SIR to our CITV-modified SIR. That said, the results of our multivariable models (using survival as a continuous variable) and AIC calculations highlight the prognostic superiority of CITV over LITV. Finally, our study fails to consider the survival impact of molecularly targeted therapy, immunotherapy, or the genetic mutation profile of the tumor. To the extent, survival expectations are increasingly modified by these agents, future studies should be devoted to study the interaction between CITV and these agents. CONCLUSION In 2 independent patient cohorts totaling ∼6000 patients, CITV consistently outperforms LITV as a prognostic variable when applied in the context of the SIR model. Disclosures The project described was partially supported by the National Institutes of Health (NIH), Grant 1TL1TR001443. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. There are no other financial disclosures relevant to this manuscript. 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NeurosurgeryOxford University Press

Published: Apr 1, 2018

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