Neuroradiology

Gorolay, Vineet Vijay; Niles, Naomi Natasha; Huo, Ya Ruth; Ahmadi, Navid; Hanneman, Kate; Thompson, Elizabeth; Chan, Michael Vinchill

doi: 10.1007/s00234-022-02941-wpmid: 35499636

PurposeEndoscopic biopsy is recommended for diagnosis of nasopharyngeal carcinoma (NPC). A proportion of lesions are hidden from endoscopic view but detected with magnetic resonance imaging (MRI). This systematic review and meta-analysis investigated the diagnostic performance of MRI for detection of NPC.MethodsAn electronic search of twelve databases and registries was performed. Studies were included if they compared the diagnostic accuracy of MRI to a reference standard (histopathology) in patients suspected of having NPC. The primary outcome was accuracy for detection of NPC. Random-effects models were used to pool outcomes for sensitivity, specificity, and positive and negative likelihood ratio (LR). Bias and applicability were assessed using the modified QUADAS-2 tool.ResultsNine studies were included involving 1736 patients of whom 337 were diagnosed with NPC. MRI demonstrated a pooled sensitivity of 98.1% (95% CI 95.2–99.3%), specificity of 91.7% (95% CI 88.3–94.2%), negative LR of 0.02 (95% CI 0.01–0.05), and positive LR of 11.9 (95% CI 8.35–16.81) for detection of NPC. Most studies were performed in regions where NPC is endemic, and there was a risk of selection bias due to inclusion of retrospective studies and one case–control study. There was limited reporting of study randomization strategy.ConclusionThis study demonstrates that MRI has a high pooled sensitivity, specificity, and negative predictive value for detection of NPC. MRI may be useful for lesion detection prior to endoscopic biopsy and aid the decision to avoid biopsy in patients with a low post-test probability of disease.

Nardi, Cosimo; Maraghelli, Davide; Pietragalla, Michele; Scola, Elisa; Locatello, Luca Giovanni; Maggiore, Giandomenico; Gallo, Oreste; Bartolucci, Maurizio

doi: 10.1007/s00234-022-02986-xpmid: 35657394

The sphenoid bone is an unpaired bone that contributes to the formation of the skull base. Despite the enormous progress in transnasal endoscopic visualisation, imaging techniques remain the cornerstones to characterise any pathological condition arising in this area. In the present review, we offer a bird’s-eye view of the developmental, inflammatory, and neoplastic alterations affecting the sphenoid body and clivus, with the aim to propose a practical diagnostic aid for radiologists based on clinico-epidemiological, computed tomography, and magnetic resonance imaging features.

Takao, Hidemasa; Amemiya, Shiori; Kato, Shimpei; Yamashita, Hiroshi; Sakamoto, Naoya; Abe, Osamu

doi: 10.1007/s00234-022-02902-3pmid: 35064786

PurposeThis study aims to develop a 2.5-dimensional (2.5D) deep-learning, object detection model for the automated detection of brain metastases, into which three consecutive slices were fed as the input for the prediction in the central slice, and to compare its performance with that of an ordinary 2-dimensional (2D) model.MethodsWe analyzed 696 brain metastases on 127 contrast-enhanced computed tomography (CT) scans from 127 patients with brain metastases. The scans were randomly divided into training (n = 79), validation (n = 18), and test (n = 30) datasets. Single-shot detector (SSD) models with a feature fusion module were constructed, trained, and compared using the lesion-based sensitivity, positive predictive value (PPV), and the number of false positives per patient at a confidence threshold of 50%.ResultsThe 2.5D SSD model had a significantly higher PPV (t test, p < 0.001) and a significantly smaller number of false positives (t test, p < 0.001). The sensitivities of the 2D and 2.5D models were 88.1% (95% confidence interval [CI], 86.6–89.6%) and 88.7% (95% CI, 87.3–90.1%), respectively. The corresponding PPVs were 39.0% (95% CI, 36.5–41.4%) and 58.9% (95% CI, 55.2–62.7%), respectively. The numbers of false positives per patient were 11.9 (95% CI, 10.7–13.2) and 4.9 (95% CI, 4.2–5.7), respectively.ConclusionOur results indicate that 2.5D deep-learning, object detection models, which use information about the continuity between adjacent slices, may reduce false positives and improve the performance of automated detection of brain metastases compared with ordinary 2D models.

Kathrani, Nihar; Chauhan, Richa Singh; Kotwal, Abhishek; Kulanthaivelu, Karthik; Bhat, Maya Dattatraya; Saini, Jitender; Prasad, Chandrajit; Chakrabarti, Dhritiman; Santosh, Vani; Uppar, Alok Mohan; Srinivas, Dwarakanath

doi: 10.1007/s00234-021-02857-x

Shin, Ilah; Park, Yae Won; Ahn, Sung Soo; Kim, Jinna; Chang, Jong Hee; Kim, Se Hoon; Lee, Seung-Koo

doi: 10.1007/s00234-021-02872-ypmid: 35112217

PurposePilocytic astrocytoma (PA) is rare in adults, and only limited knowledge on the clinical course and prognosis has been available. The combination of clinical information and comprehensive imaging parameters could be used for accurate prognostic stratification in adult PA patients. This study was conducted to predict the prognostic factors from clinical information and conventional magnetic resonance imaging (MRI) features in adult PAs.MethodsA total of 56 adult PA patients were enrolled in the institutional cohort. Clinical characteristics including age, sex, anaplastic PA, presence of neurofibromatosis type 1, Karnofsky performance status, extent of resection, and postoperative treatment were collected. MRI characteristics including major axis length, tumor location, presence of the typical ‘cystic mass with enhancing mural nodule appearance’, proportion of enhancing tumor, the proportion of edema, conspicuity of the nonenhancing margin, and presence of a cyst were evaluated. Univariable and multivariable Cox proportional hazard modeling were performed.ResultsThe 5-year progression-free survival (PFS) and overall survival (OS) rates were 83.9% and 91.l%, respectively. On univariable analysis, older age, larger proportion of edema, and poor definition of nonenhancing margin were predictors of shorter PFS and OS, respectively (all Ps < .05). On multivariable analysis, older age (hazard ratio [HR] = 1.04, P = .014; HR = 1.14, P = .030) and poor definition of nonenhancing margin (HR = 3.66, P = .027; HR = 24.30, P = .024) were independent variables for shorter PFS and OS, respectively.ConclusionAge and the margin of the nonenhancing part of the tumor may be useful biomarkers for predicting the outcome in adult PAs.

Batalov, A. I.; Afandiev, R. M.; Zakharova, N. E.; Pogosbekyan, E. L.; Shulgina, A. A.; Kobyakov, G. L.; Potapov, A. A.; Pronin, I. N.

doi: 10.1007/s00234-021-02888-4pmid: 35112216

PurposeThe aim of the study was to compare the parameters of blood flow in glioblastomas and primary central nervous system lymphomas (PCNSLs), measured by pseudo-continuous arterial spin labeling MRI (3D PCASL), and to determine the informativeness of this method in the differential diagnosis between these lesions.MethodsThe study included MRI data of 139 patients with PCNSL (n = 21) and glioblastomas (n = 118), performed in the Burdenko Neurosurgical Center. No patients received chemotherapy, hormone therapy, or radiation therapy prior to MRI. On the 3D PCASL perfusion map, the absolute and normalized values of tumor blood flow were calculated in the glioblastoma and PCNSL groups (maxTBFmean and nTBF).ResultsMaxTBFmean and nTBF in the glioblastoma group were significantly higher than those in the PCNSL group: 168.9 ml/100 g/min versus 65.6 and 9.3 versus 3.7, respectively (p < 0.001). Arterial spin labeling perfusion had high sensitivity (86% for maxTBFmean, 95% for nTBF) and specificity (77% for maxTBFmean, 73% for nTBF) in the differential diagnosis between PCNSL and glioblastomas. Blood flow thresholds were 98.9 ml/100 g/min using absolute blood flow values and 6.1 using normalized values, AUC > 0.88.ConclusionThe inclusion of 3D PCASL in the standard MRI protocol can increase the specificity of the differential diagnosis between glioblastomas and PCNSL.

Zhang, Xiuping; Gao, Qianqian; Chen, Kaidong; Wu, Qiuxiang; Chen, Bixue; Zeng, Shangyu; Fang, Xiangming

doi: 10.1007/s00234-022-02899-9pmid: 35083504

PurposeTo develop and validate a new nomogram utilizing non-contrast computed tomography (NCCT) signs and clinical factors for predicting hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH).MethodsHE was defined as > 6 mL or 33% increase in baseline hematoma volume. Multivariable logistic regression analysis was performed to identify the predictors of HE. The discriminatory performance of the proposed model was evaluated via receiver operation characteristic (ROC) analysis, and the predictive accuracy was assessed by a calibration curve. The nomogram was established by R programming language. The decision curve analysis and clinical impact curve were drawn according to the related risk factors.ResultsA total of 506 patients with spontaneous ICH were recruited in the development cohort, and 103 patients were registered as the external validation cohort. Among the development cohort, 132 (26.09%) experienced HE. Glasgow coma scale (GCS) (P < 0.001), neutrophil to lymphocyte ratio (NLR) (P < 0.001), blend sign (P < 0.001), swirl sign (P < 0.001), and hypodensities (P = 0.003) were significant predictors of HE, by which were used to establish the nomogram. The model demonstrated good performance with high area under the curve both in the development (AUC = 0.908; 95% confidence interval, 0.880–0.936) and the external validation (AUC = 0.844; 95% confidence interval, 0.760–0.908) cohort. The calibration curve illustrated a high accuracy for HE prediction.ConclusionThe nomogram derived from NCCT markers and clinical factors outperformed the NCCT signs-only model in predicting HE for patients with ICH, thus providing an effective and noninvasive tool for the risk stratification of HE.

Kaffenberger, Tina; Venkatraman, Vijay; Steward, Chris; Thijs, Vincent N.; Bernhardt, Julie; Desmond, Patricia M.; Campbell, Bruce C. V.; Yassi, Nawaf

doi: 10.1007/s00234-021-02875-9pmid: 35094103

PurposeDevelopment of a freely available stroke population–specific anatomical CT/MRI atlas with a reliable normalisation pipeline for clinical CT.MethodsBy reviewing CT scans in suspected stroke patients and filtering the AIBL MRI database, respectively, we collected 50 normal-for-age CT and MRI scans to build a standard-resolution CT template and a high-resolution MRI template. The latter was manually segmented into anatomical brain regions. We then developed and validated a MRI to CT registration pipeline to align the MRI atlas onto the CT template. Finally, we developed a CT-to-CT-normalisation pipeline and tested its reliability by calculating Dice coefficient (Dice) and Average Hausdorff Distance (AHD) for predefined areas in 100 CT scans from ischaemic stroke patients.ResultsThe resulting CT/MRI templates were age and sex matched to a general stroke population (median age 71.9 years (62.1–80.2), 60% male). Specifically, this accounts for relevant structural changes related to aging, which may affect registration. Applying the validated MRI to CT alignment (Dice > 0.78, Average Hausdorff Distance < 0.59 mm) resulted in our final CT-MRI atlas. The atlas has 52 manually segmented regions and covers the whole brain. The alignment of four cortical and subcortical brain regions with our CT-normalisation pipeline was reliable for small/medium/large infarct lesions (Dice coefficient > 0.5).ConclusionThe newly created CT-MRI brain atlas has the potential to standardise stroke lesion segmentation. Together with the automated normalisation pipeline, it allows analysis of existing and new datasets to improve prediction tools for stroke patients (free download at https://forms.office.com/r/v4t3sWfbKs).

Tandon, V.; Senthilvelan, S.; Sreedharan, S. E.; Kesavadas, C.; VT, Jissa; Sylaja, P. N.

doi: 10.1007/s00234-021-02891-9pmid: 35112218

PurposeHigh-resolution MR vessel wall imaging (HRVWI) can characterise vessel wall pathology affecting intracranial circulation and helps in differentiating intracranial vasculopathies. The aim was to differentiate intracranial pathologies involving middle cerebral artery (MCA) in patients with ischemic stroke and characterise the high-risk plaques in intracranial atherosclerotic disease (ICAD) using HRVWI.MethodsPatients with ischemic stroke with isolated MCA disease with ≥ 50% luminal narrowing by vascular imaging were enrolled within 2 weeks of onset and underwent high-resolution (3 T) intracranial vessel wall imaging (VWI). The pattern of vessel wall thickening, high signal on T1-weighted images, juxtaluminal hyperintensity, pattern and grade of enhancement were studied. The TOAST classification before and after HRVWI and the correlation of the recurrence of ischemic events at 3 months with imaging characteristics were analysed.ResultsOf the 36 patients, the mean age was 49.53 ± 15.61 years. After luminal imaging, by TOAST classification, 12 of 36 patients had stroke of undetermined aetiology. After vessel wall imaging, lesions in MCA were analysed. Of them, 23 patients had ICAD, 8 had vasculitis, and 2 had partially occlusive thrombus in MCA. The ability of HRVWI to bring a change in diagnosis was significant (p = 0.031). Of the 23 patients with ICAD, 12 patients had recurrent strokes within 3 months. The presence of grade 2 contrast enhancement (p = 0.02) and type 2 wall thickening (p = 0.03) showed a statistically significant association with recurrent ischemic events.ConclusionHigh-resolution MRVWI can help in identifying the aetiology of stroke. The HRVWI characteristics in ICAD can help in risk stratification.

Fasen, Bram A. C. M.; Berendsen, Ralph C. M.; Kwee, Robert M.

doi: 10.1007/s00234-022-02912-1pmid: 35137270

PurposeTo evaluate the diagnostic performance of AI software in diagnosing intracranial arterial occlusions in the proximal anterior circulation at CT angiography (CTA) and to compare it to manual reading performed in clinical practice.MethodsPatients with acute ischemic stroke underwent CTA to detect arterial occlusion in the proximal anterior circulation. Retrospective review of CTA scans by two neuroradiologists served as reference standard. Sensitivity and specificity of AI software (StrokeViewer) were compared to those of manual reading using the McNemar test. The proportions of correctly detected occlusions in the distal internal carotid artery and/or M1 segment of the middle cerebral artery (large vessel occlusion [LVO]) and in the M2 segment of the middle cerebral artery (medium vessel occlusion [MeVO]) were calculated.ResultsOf the 474 patients, 75 (15.8%) had an arterial occlusion in the proximal anterior circulation according to the reference standard. Sensitivity of StrokeViewer software was not significantly different compared to that of manual reading (77.3% vs. 78.7%, P = 1.000). Specificity of StrokeViewer software was significantly lower than that of manual reading (88.5% vs. 100%, P < 0.001). StrokeViewer software correctly identified 40 of 42 LVOs (95.2%) and 18 of 33 MeVOs (54.5%). StrokeViewer software detected 8 of 16 (50%) intracranial arterial occlusions which were missed by manual reading.ConclusionThe current AI software detected intracranial arterial occlusion with moderate sensitivity and fairly high specificity. The AI software may detect additional occlusions which are missed by manual reading. As such, the use of AI software may be of value in clinical stroke care.