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Semi‐solid MT and APTw CEST‐MRI predict clinical outcome of patients with glioma early after radiotherapy

Semi‐solid MT and APTw CEST‐MRI predict clinical outcome of patients with glioma early after... INTRODUCTIONDiffuse gliomas have an incidence of 5.2 / 100000 per year and are the second most common class of primary brain tumors over all.1 Therapy involves maximal surgical resection followed by radiotherapy or concurrent radio/chemotherapy and adjuvant chemotherapy, depending on World Health Organization (WHO) grade.2 Under current clinical standard of care, life expectancy depends on histopathologic subtype, tumor size, completeness of resection and patient age.3–6 Time to treatment failure amounts to 1.4 y for glioblastoma (GBM), 4.5 y for astrocytomas with oncogenic mutations of the isocitrate dehydrogenase isotype 1 or 2 (IDH‐mutant) and 9.8 y for IDH‐mutant oligodendrogliomas.6 Treatment‐related brain damage and tumor progression can equally result in blood–brain‐barrier damage and increasing gliosis, making differentiation on MRI challenging.7 Misdiagnosis of either can result in severe consequences for the patient, as they require different therapeutic strategies. Growing awareness to pseudoprogression (PP), occurring in up to 30 % of all cases following radiation, has led to the introduction of response assessment in neuro‐oncology (RANO) criteria in 2010.8,9 However, RANO requires longitudinal follow‐up as well as integration of image‐based and clinical parameters, making correct response assessment challenging. For this reason, innovative MRI modalities such as diffusion‐ and perfusion‐weighted imaging, PET‐MRI and MRI spectroscopy have been evaluated for their clinical value in patients with glioma before and after therapy over the past two decades. This has led to the incorporation of some of these techniques into clinical routine.10,11CEST MRI is an innovative and contrast agent‐free MRI approach, for which several groups have demonstrated great potential for therapy response assessment and prognostication in patients with glioma undergoing radio‐ and chemotherapy.12–19 However, CEST contrasts are heavily dependent on the metrics used for their extraction from the Z‐spectrum and the applied magnetic field strengths.20,21 There is a fast growing body of evidence in the literature about the prognostic capacities of asymmetry‐based, relaxation‐compensated and Lorentzian‐fit‐based CEST contrasts of the amide proton transfer (APT) and semi‐solid magnetization transfer (ssMT), that is for predicting therapy response at different field strengths of 1.5, 3, and 7 T.16–19,22–24 Furthermore, several studies have demonstrated the diagnostic capacities of asymmetry‐based APT‐weighted (APTwasym) CEST imaging for differentiation of treatment‐related changes and glioma progression, as early as 3 mo after the completion of radiotherapy.12–14 However, results from cross‐sectional prospective clinical studies, comparing the prognostic and diagnostic potential of the APTwasym, as well as relaxation‐compensated (MTRRexAPT and MTRrexMT) and Lorentzian‐fit‐based (PeakAreaAPT and MTconst) CEST contrasts of the APT and ssMT early after the completion of radiotherapy in a larger cohort of patients with glioma have not been reported before. Therefore, the purpose of this study was to compare the potential of the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst for response assessment and prediction of progression‐free survival (PFS) in patients with glioma in a prospective clinical trial at the first follow‐up 4 – 6 wk after the completion of radiotherapy.METHODSStudy designThe local institutional review board committee approved this prospective study. Written informed consent was obtained from each participant prior to study inclusion.From September 2018 until December 2021 72 study participants (61 with initial disease and 11 with relapsing/progressive disease) being treated for diffuse glioma at the department of radiation‐oncology of the University Hospital Heidelberg were prospectively enrolled. MRI scans were acquired at the first follow‐up 4 – 6 wk after the completion of radiotherapy. Inclusion criteria were minimum age of 18 y, Karnofsky‐Performance‐Score of 50 or higher and legal capacity to consent to study inclusion. Eleven participants had been excluded from analysis due to heavy motion artifacts (two participants), incomplete data sets (seven participants), large perioperative infarction (one participant), and missing clinical follow‐up data at data cut off on January 26, 2022 (one participant) (Figure 1).1FIGUREFlow chart of the study. Seventy‐two participants were prospectively included in the study and underwent CEST MRI 4 – 6 wk following completion of radiotherapy. Eleven participants had to be excluded from evaluation due to the given reasons. Association of CEST contrast values with therapy response assessment at the first follow‐up was tested by Mann Whitney‐U‐test and ROC analysis in all 61 evaluable study participants (all participants, n = 61) and 42 participants with residual contrast enhancement on MRI (participants with CE, n = 42). Gliomas with midline locations were excluded for PFS assessment, since these tumors are known to be associated with an especially poor prognosis (participants with CE, n = 37). CE, contrast enhancement; KPI, Karnovsky Performance Score; PFS, progression‐free survival; ROC, receiver operating characteristic.Assessment of therapy response at the first follow‐up 4 – 6 wk after the completion of radiotherapy and PFS were performed according to the revised RANO criteria based on clinical data and MRI with a median follow‐up of 9.2 mo (range 1.6 – 40.47 mo).9,25 Pseudoprogression was distinguished from progressive disease based on follow‐up. PFS was assessed as the time between the start of radiotherapy and disease progression. Final decisions were made in consensus by two radiologists with 5 and 10 y of experience in neuroimaging (N.K. and D.P.). To account for prescribed antiangiogenic drugs, potential adaptions of therapy regimens and clinical status changes, assessment results were matched with institutional interdisciplinary tumor‐board decisions.HistologyHistology after biopsy or surgical resection was available for 60 out of 61 study participants. Histology of one participant being treated for newly diagnosed glioma with midline location could not be secured due to poor clinical status. Clinical routine work‐up included determination of IDH‐, ATRX‐, LOH1p19q‐, H3K27M‐, and MGMT‐ status and glioma classification was performed according to WHO 2016 criteria. For detailed information on tumor histopathologies of the study cohort, please see Table 1.1TABLEClinical characteristics of all evaluated 61 study participants who received CEST MRI in the first follow‐up (FU) 4 – 6 wk after completion of radiotherapyCharacteristicNumber (n)PercentageAge at diagnosisMean 59 ± 1661SexMale3557%Female2643%Glioma locationHemispheric5592%Midline68%Treatment forInitial disease5387%Relapsing disease813%TherapyRadiation1423%Chemoradiation4777%Debulking surgery3862%RANOProgressive disease1931%Stable disease3456%Pseudoprogression813%Residual contrast enhancement on MRIYes4269%No1931%DiagnosisGBM4472%Gliosarcoma23%Midline glioma (H3K27M)23%Astrocytoma813%Oligodendroglioma47%Not secured12%WHO gradeII610%III35%IV5184%N/A12%IDH statusIDHwt4675%IDHmut1220%N/A35%MGMT promotor methylationYes3354%No2033%N/A813%Note: “Therapy” indicates whether participants received radiotherapy only or chemoradiation, and states the number of participants that underwent debulking surgery prior to radiotherapy. “Diagnosis” and “WHO grade” describe histopathological subtypes and tumor grades according to the 2016 WHO criteria for primary central nervous system tumors.Abbreviations: GBM, glioblastoma; H3K27M, subsitution of lysine residue 27 on histone 3 for methionine (oncogenic histone variant); IDHmut, IDH‐mutant; IDHwt, IDH‐wildtype; RANO, response assessment in neuro‐oncology; WHO, World Health Organization.Image acquisition and postprocessingAll images were acquired on a 3 T whole‐body MR scanner (MAGNETOM Prisma; Siemens Healthineers) with a 64‐channel receive head/neck coil and an integrated transmit body coil. Processing of CEST data was performed using customized scripts in Matlab® (Mathworks) version 2019b.Lorentzian‐fit based and relaxation‐compensated CEST imaging of the APT and ssMTThe image‐readout parameters (matrix = 128 × 104 × 16, resolution = 1.7 × 1.7 × 3 mm3) of the 3D spiral‐centric‐reordered gradient‐echo acquisition sequence (i.e., snapshot CEST26,27) and the CEST presaturation, originally from Zaiss et al.,27 were set up as described in detail by Goerke et al.21Presaturation was performed with a mean amplitude of B1 = flip angle/(γ·tp) = 0.6 and 0.9 μT respectively. For both CEST scans, 57 unequally distributed frequency offsets between ±250 ppm and two M0 at −300 ppm for normalization were acquired, resulting in a total measurement time of 7:36 min per CEST scan.The WASABI28 (3:41 min) approach, which provides B0 and B1 maps, was used with the same image readout and a similar presaturation with an equally distributed sampling of 31 offsets between ±2 ppm.For postprocessing, the CEST and WASABI data were coregistered using a rigid registration algorithm as described by.29 Subsequently, all Z‐spectra were B0 corrected by shifting them along Δω according to the B0‐map. Finally, the Z‐spectra were denoised using a principle component analysis based algorithm.30Two different Lorentzian‐fit based evaluation models were applied to isolate the signal contributions.The first model (MTRRexAPT and MTRRexMT) is additionally compensated for spillover of the direct water saturation and MT influences. These contrasts are referred to as “relaxation compensated” in the following. This model is based on a four‐pool Lorentzian‐fit between ±250 ppm around the water offset (0 ppm: direct water saturation, 3.5 ppm: APT, −3.5 ppm: rNOE, and −2.5 ppm: ssMT).21 The MTRRex contrasts of the APT and ssMT were calculated by MTRRex=1Z−1Zref$$ \mathrm{MT}{\mathrm{R}}_{\mathrm{R}\mathrm{ex}}=\frac{1}{Z}-\frac{1}{Z_{\mathrm{ref}}} $$. Additional B1‐correction was performed with the two‐point “contrast‐correction” method as proposed by Windschuh et al.31The second data fit approach (PeakAreaAPT and MTconst) was adapted from Mehrabian et al.,16 and only uses the region between ±6 ppm to fit a four‐pool Lorentzian (APT, AMINE, rNOE, DS) and a constant ssMT to the data of the CEST measurement at 0.6 μT:S(Δ)=1−MT+∑i=14Ai1+Δ−Δ0i0.5wi2$$ \boldsymbol{S}\left(\boldsymbol{\Delta} \right)=\mathbf{1}-\left(\mathbf{MT}+{\sum}_{\boldsymbol{i}=\mathbf{1}}^{\mathbf{4}}\frac{{\boldsymbol{A}}_{\boldsymbol{i}}}{\mathbf{1}+{\left(\frac{\boldsymbol{\Delta} -{\boldsymbol{\Delta}}_{\mathbf{0i}}}{\mathbf{0.5}{\boldsymbol{w}}_{\boldsymbol{i}}}\right)}^{\mathbf{2}}}\right) $$With Ai,Δ0i,wi=[amplitude,center frequency,width]$$ \left[{\boldsymbol{A}}_{\boldsymbol{i}},{\boldsymbol{\Delta}}_{\mathbf{0i}},{\boldsymbol{w}}_{\boldsymbol{i}}\right]=\left[\mathrm{amplitude},\mathrm{center}\ \mathrm{frequency},\mathrm{width}\right] $$. Afterward, the constant ssMT offset (MTconst) and the area under curve of the Lorentzian describing the APT‐peak (PeakArea APT) were calculated. The PeakAreaAPT and MTconst are collectively being referred to as Lorentzian‐fit‐based contrasts in the following. A representative fitted Z‐spectrum and residuals of both models can be found in Figure S1. The displayed Z‐spectrum is located in the CE‐region of the participant with progressive disease shown in Figure 2.2FIGUREThe figure shows anatomical images and contrast maps of two exemplary study participants with progressive disease (PD–top) and pseudoprogression (PP–bottom). Depicted from left to right in the top rows are: T1w post‐contrast (T1CE), APTwasym, MTRRexAPT, and MTRRexMT. Bottom rows show from left to right: T2w‐FLAIR, PeakAreaAPT, and MTconst. Asterisks (*) are indicating contrast‐enhancing tumor tissue (CE). Arrows (→) are indicating glioma‐associated edema (ED–hyperintense in T2w‐FLAIR). In this case the MTRRexAPT demonstrated markedly increased contrast values confined to CE in the participant with PD, whilst no such contrast pattern can be seen in the participant with PP. Visual correlation of the APTwasym, MTRRexMT, PeakAreaAPT, and MTconst contrast values with CE and ED were less clear. PeakAreaAPT demonstrated very noisy contrast maps.Asymmetry‐based CEST imaging of the APTwasymAPTwasym imaging was realized with the identical pulse sequence and image readout as the CEST measurement in the previous section. Presaturation was achieved according to Zhou J, et al.32 in line with recent consensus and recommendation guidelines33 employing four rectangular RF pulses with a B1 of 2 μT, pulse length (tp) of 0.2 s and 95% duty cycle (tsat = 0.83 s). Sixteen offsets were acquired at ±4 (1), ±3.75 (2), ±3.5 (2), ±3.25 (2), and ±3 (1) ppm. Additionally, an M0 at −300 ppm was acquired for normalization purposes.Similar coregistration and B0‐correction approaches were used as described in the previous section. Afterward, the APTwasym=Z(−3.5ppm)−Z(3.5ppm)$$ \mathrm{APT}{\mathrm{w}}_{\mathrm{asym}}=Z\left(-3.5\ \mathrm{ppm}\right)-Z\left(3.5\ \mathrm{ppm}\right) $$ contrast was calculated from the corrected data.Quantitative T1 mappingGiven that the investigated CEST contrasts are influenced by T1 to varying degrees, we also assessed the association of T1 with the RANO assessment results and PFS. Quantitative mapping of the longitudinal relaxation time of water (T1) was performed by the application of a saturation recovery sequence21 with recovery times trec$$ \left({t}_{\mathrm{rec}}\right) $$ of 0.1, 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, 3.5, 5.0, 7.5, and 10.0 s and Mztrec=M0+Mz(0)−M0⋅e−trec/T1$$ {M}_z\left({t}_{\mathrm{rec}}\right)={M}_0+\left({M}_z(0)-{M}_0\right)\cdot {e}^{-{t}_{\mathrm{rec}}/\mathrm{T}1} $$.Tumor segmentations3D segmentations of contrast‐enhancing tumor (CE) volumes, whole tumor (WT) volumes, and normal appearing white matter (NAWM) were performed by a radiologist with 5 y of experience in neuroimaging (N.K.) on contrast‐enhanced T1w and T2w fluid‐attenuated inversion recovery (T2w‐FLAIR) images with a custom‐made segmentation tool in Matlab® (Mathworks) version 2019b. Segmentations were performed in all evaluable study participants (all participants, n = 61) and in one sub‐cohort encompassing only participants with residual contrast enhancement on MRI (participants with CE, n = 42) (Figure 1). The WT volume in the study participants with residual contrast enhancement encompassed CE plus peritumoral T2w‐FLAIR hyperintense signal alterations (WT [participants with CE]). In the entire study sample, the WT volumes encompassed additional 19 cases that contained only peritumoral T2w‐FLAIR hyperintense signal alterations (WT [all participants]), given the absence of residual contrast enhancement in these patients (please see Table 1). Relevant surgically induced changes (i.e., hemorrhage) were excluded by visual correlation with T1w and T2w*$$ {\mathrm{T}}_{2\mathrm{w}}^{\ast } $$ images.Statistical analysesReceiver operating characteristic (ROC) analyses and Mann–Whitney‐U‐test were performed to test for quantitative differences of CEST contrast values between participants with stable and progressive disease, as well as pseudoprogressive and progressive disease. Kaplan–Meier curves and logrank‐tests were used for analyses of PFS in association with mean CEST contrast values per ROI.In six participants, tumors showed involvement of midline structures as thalamus, midbrain, or brainstem. Diffuse midline gliomas are common in pediatric patients, but rare in adults.34 Since glioma midline location is known to be associated with worse clinical outcome, midline gliomas were additionally excluded for PFS analysis (participants with CE, n = 37)35 (Figure 1).All statistical analyses were performed using in‐house software in Matlab® (Mathworks) version 2019b.RESULTSDemographics of study participantsSeventy‐two study participants (mean age 59 ± 16 y; 43 male) underwent CEST MRI at the first follow‐up 4 – 6 wk after the completion of radiotherapy.Out of 61 evaluable participants (35 male, 26 female; mean age 59 ± 16 y) 19 had progressive disease (PD) and 8 had pseudoprogression (PP). Overall, 42 participants were diagnosed as having stable disease (SD), which included the eight participants with PP. Median PFS was 5.3 mo (range 1.5 – 27.1). Detailed characteristics of study participants are provided in Table 1.MTconst showed a stronger association with early response assessment according to RANO compared to PeakAreaAPT and MTRRexMTContrast maps of the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst for two study participants, one with PD, and one with PP, are illustrated in Figure 2.When comparing participants with PD and SD, the MTRRexMT (participants with CE: CE 0.41 ± 0.05 vs. 0.36 ± 0.07, p = 0.02; WT [participants with CE] 0.49 ± 0.05 vs. 0.45 ± 0.06, p = 0.05) and MTconst (all participants: WT [all participants] 0.49 ± 0.05 vs. 0.46 ± 0.07, p = 0.01; participants with CE: CE 0.41 ± 0.05 vs. 0.36 ± 0.07, p < 0.01, WT [participants with CE] 0.49 ± 0.05 vs. 0.45 ± 0.06, p < 0.01) showed higher contrast values for tumor tissue in participants with PD, whereas the PeakAreaAPT showed lower contrast values for participants with PD (participants with CE: CE 0.24 ± 0.10 vs. 0.33 ± 0.13, p = 0.02; WT [participants with CE] 0.23 ± 0.12 vs. 0.31 ± 0.13, p = 0.04). When comparing participants with PD and PP, the MTconst was the only contrast that showed significant differences, with higher contrast values for tumor tissue in participants with PD (participants with CE: CE 0.18 ± 0.02 vs. 0.16 ± 0.03, p = 0.03; WT [participants with CE] 0.18 ± 0.02 vs. 0.15 ± 0.03, p = 0.05) (Figure 3).3FIGUREViolin plots of CEST contrast values for study participants with residual contrast enhancement on MRI (n = 42). Top row shows from left to right graphs for APTwasym, MTRRexAPT and MTRRexMT. Bottom row shows graphs for PeakAreaAPT (left) and MTconst (right). Graphs show absolute CEST contrast values of contrast‐enhancing tumor volume (CE), whole tumor volume (WT), and normal appearing white matter (NAWM). Red columns show cases with progressive disease (PD, n = 19), green columns with stable disease (SD, n = 23) and blue columns with pseudoprogression (PP, n = 8). Green columns (SD) also include cases with PP, given the fact that PP is formally counted as SD according to the response assessment in neuro‐oncology (RANO) criteria. Only MTconst showed for CE and WT significantly different contrast values for cases with PD compared to SD (CE p < 0.01, WT p < 0.01) and PP (CE p = 0.03, WT p = 0.05). When comparing study participants with PD and SD only, the MTRRexMT showed higher contrast values for CE (p = 0.02) and WT (p = 0.05) in participants with PD, whereas the PeakAreaAPT showed higher contrast values for participants with SD (CE p = 0.02, WT p = 0.04).ROC analyses yielded matching results. When comparing participants with PD and SD, areas under the curve (AUC) for the MTRRexMT were 0.71 for CE (p = 0.02) and 0.68 for WT (participants with CE) (p = 0.05) in participants with residual contrast enhancement on MRI (participants with CE). In the same sub‐cohort AUCs for the PeakAreaAPT were 0.71 (p = 0.02) for CE and 0.69 for WT (participants with CE) (p = 0.04). For the MTconst AUCs were 0.79 (p < 0.01) for CE and 0.78 (p < 0.01) for WT (participants with CE) in participants with residual contrast enhancement on MRI and 0.72 (p = 0.01) for WT (all participants) in all evaluable participants (all participants). When testing for differences between participants with PD and PP the MTconst yielded AUCs of 0.79 (p = 0.02) for CE and 0.75 (p = 0.05) for WT (participants with CE) in participants with residual contrast enhancement on MRI. All other contrasts did not enable distinction between participants with PD and PP. Furthermore, the APTwasym and MTRRexAPT were not associated with RANO assessment at the first follow‐up. ROC curves testing for differences in mean contrast values between participants with PD and SD for all CEST contrasts, as well as differences between participants with PD and PP for the MTconst are shown in Figures 4 and 5.4FIGUREReceiver operating characteristic (ROC) curves testing for capacities of the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst to differentiate between study participants with progressive disease (PD) and stable disease (SD) according to the response assessment in neuro‐oncology (RANO) criteria. Participants with pseudoprogression (PP) are included in SD, given the fact that PP is formally counted as SD according to the response assessment in neuro‐oncology (RANO) criteria. Given are area under the curve (AUC) values for whole tumor (WT [all participants], light blue) volume in all study participants (n = 61), as well as contrast‐enhancing tumor volumes (CE, red) and WT (WT [participants with CE], dark blue) in participants with residual contrast enhancement on MRI (n = 42). In all participants, MTconst was the only contrast that could differentiate PD and SD (WT [all participants], p = 0.01). After stratification for participants with residual contrast enhancement on MRI MTRRexMT (CE p = 0.02, WT [participants with CE] p = 0.05), PeakAreaAPT (CE p = 0.02, WT [participants with CE] p = 0.04), and MTconst (CE p < 0.01, WT [participants with CE] p < 0.01) could differentiate between participants with PD and SD.5FIGUREReceiver operating characteristic curves testing for the capacity of the MTconst to differentiate between participants with progressive disease (PD, n = 19) and pseudo‐progression (PP, n = 8) according to response assessment in neuro oncology criteria in participants with residual contrast enhancement on MRI (n = 42). AUCs were significant for contrast‐enhancing tumor volume (CE, p = 0.02, red) and whole tumor volume (WT [participants with CE], p = 0.05, blue).APTwasym, PeakAreaAPT, and MTconst signal intensities are associated with PFSIn Kaplan–Meier analyses, the APTwasym, PeakAreaAPT, and MTconst showed significant association with PFS. Participants with APTwasym and MTconst contrast values above the median of the (sub‐)cohort showed shorter time to progression compared to participants with contrast values below the median. For the APTwasym, PFS was 127 versus 267 days (HR = 2.47, p = 0.01) for WT (all participants) in all evaluable participants (all participants) and 71 versus 174 days (HR = 2.63, p = 0.02) for CE after stratification for participants with residual contrast enhancement on MRI (participants with CE). For the MTconst PFS was 68 versus 172 days (HR = 3.04, p = 0.01) for CE and 62 versus 186 days (HR = 4.75, p < 0.01) for WT (participants with CE) in participants with residual contrast enhancement on MRI. On the contrary, for the PeakAreaAPT participants with contrast values above the median showed longer PFS with 173 versus 68 days (HR = 0.39, p = 0.03) for CE and a similar trend (148 vs. 70 days; HR = 0.48, p = 0.08) for WT (participants with CE) in participants with residual contrast enhancement on MRI. The MTRRexAPT and MTRRexMT did not show any association with PFS (Figure 6).6FIGUREKaplan–Meier analyses testing for the association of progression‐free survival (PFS) with the APTwasym contrast values of whole tumor volume (WT) in all study participants (all participants, number at risk n = 61), as well as the association of PFS with the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst contrast values for contrast‐enhancing tumor volume (CE) and WT in participants with residual contrast enhancement on MRI (participants with CE, number at risk n = 37). The APTwasym was the only contrast that was associated with PFS in all study participants (top row first column, HR = 2.47, p = 0.01) with shorter PFS of participants showing higher contrast values for WT (dark red). After stratification for participants with residual contrast enhancement on MRI, the APTwasym (bottom row right column, HR = 2.63, p = 0.02), PeakAreaAPT (bottom row middle column, HR = 0.39, p = 0.03), and MTconst (bottom row left column, HR = 3.04, p = 0.01) were associated with PFS, with APTwasym and MTconst showing higher contrast values for CE (light red) and WT (dark red) in participants with shorter survival. The PeakAreakAPT showed higher contrast values in participants with longer survival. For PFS analyses, participants with midline glioma location (n = 5) where additionally excluded from the subgroup of participants with residual contrast enhancement on MRI.T1 is weakly associated with RANO assessment but not with PFSWhen comparing participants with PD and SD, the AUC for T1 for the WT (all participants) volumes was 0.66 (p = 0.047) (Figure S2). However, there was no association of T1 with the RANO assessment results after the stratification for presence of residual contrast enhancement on MRI (Figure S2). Furthermore, there were no significant differences of T1 between participants with PD and SD (Figure S3) and no significant association of T1 with PFS (Figure S4).DISCUSSIONSeveral groups have demonstrated the potential of CEST MRI for therapy response assessment in diffuse gliomas. However, so far comparative cross‐sectional results of asymmetry‐based (APTw), relaxation‐compensated (MTRRexAPT and MTRRexMT), and Lorentzian‐fit‐based (PeakAreaAPT and MTconst) contrasts of the APT and ssMT at the first follow‐up 4 – 6 wk after the completion of radiotherapy at 3 T have not been reported before. In this prospective clinical study, we demonstrated that the MTconst (AUC = 0.79, p < 0.01) showed higher association with RANO response assessment than the PeakAreaAPT (AUC = 0.71, p = 0.02) and the MTRRexMT (AUC = 0.71, p = 0.02), and enabled differentiation of study participants with pseudoprogression (n = 8) from those with disease progression (AUC = 0.79, p = 0.02) at the first follow‐up after the completion of radiotherapy. Furthermore, the MTconst (HR = 3.04, p = 0.01), PeakAreaAPT (HR = 0.39, p = 0.03), and APTwasym (HR = 2.63, p = 0.02) were associated with PFS. The MTRRexAPT was not associated with therapy response or PFS, as assessed by RANO criteria.In prior non‐cross sectional clinical studies with 30 – 65 patients, Liu et al., Ma et al., and Park et al. have demonstrated that APTwasym contrast values were associated with RANO assessment at 3 T and enabled differentiation of glioma progression and radiation necrosis as early as 3 mo after the completion of radiotherapy.12–14 Others have reported in prospective clinical studies at 1.5, 3, and 7 T with 19 – 71 patients that the APTwasym contrast was associated with PFS before treatment onset, whilst the prognostic performance of this technique rapidly decreased in patients after the start of therapy.12–14,16,22–24 We only observed an association of the APTwasym with PFS but not with therapy response at the first follow‐up, as assessed by RANO criteria.In prospective clinical studies at 7 T with 20 and 26 patients with newly diagnosed gliomas, Regnery et al. and Paech et al. reported that the MTRRexAPT could predict therapy response and survival, with higher contrast values for patients with early disease progression.19,23 However, Meissner et al. did not find any association of MTRRexAPT contrast values with treatment response after the start of radiotherapy.18 Mirroring the findings by Meissner et al., we also did not find any association of MTRRexAPT contrast values with therapy response and PFS at the first follow‐up after the completion of radiotherapy at 3 T. For the MTRRexMT, we only observed an association with therapy response but not with PFS. To our knowledge, this is the first report on the association of the MTRRexMT with clinical outcomes in patients with glioma following radiotherapy.In a prospective clinical study with 19 patients, Mehrabian et al. have demonstrated that MTconst could prognosticate therapy response, whereas MTconst after the start of therapy and PeakAreaAPT did not show any association with clinical outcome.16 In our study with 61 evaluable participants, MTconst and PeakAreaAPT demonstrated the highest association with therapy response and PFS. Furthermore, the MTconst could also differentiate between participants assessed as having progressive and pseudoprogressive disease.Generally, for the interpretation of these study findings it is essential to consider well‐known diagnostic limitations of the RANO criteria. RANO compares volumetric changes of contrast enhancement on T1w and abnormal T2w‐FLAIR signal hyperintensities on consecutive MRI exams under consideration of clinical status and application of antiangiogenic drugs. However, in clinical practice blood–brain‐barrier disruption and edema caused by disease progression or radiotherapy are in most cases not easily distinguishable and frequently occur to variable degrees simultaneously. Furthermore, other factors such as therapeutically induced granulation tissue and inflammatory processes might also contribute to respective changes on MRI.7–9,25 Regarding CEST, the Lorentzian‐fit‐based PeakAreaAPT and MTconst contrasts have several other contributions, such as T1, spill‐over, and unwanted ssMT in case of the PeakAreaAPT.16,36 The asymmetry‐based APTw includes, in addition to those, contributions from the relayed nuclear Overhauser effect (rNOE).32,36 In relaxation‐compensated CEST contrasts the APT and ssMT peaks are corrected for spill‐over effects, even though also these contrasts have certain T1 contributions.21,36 In this regard, it can be speculated that the superior performance of the Lorentzian‐fit‐based contrasts in this study might be explained by a summation of several biomolecular changes which influence T1 relaxation, spill‐over, and CEST effects as APT, rNOE, and ssMT to varying degrees. T1 is heavily influenced by the water content of examined volumes. Therefore, the higher MTconst values in tumor tissue compared to NAWM, which is reflected by higher T1 (Figure S3), might be linked to the tumor‐associated disruption of the blood brain barrier and resulting edema. However, the fact that in this study only a weak association of T1 with therapy response according to RANO but not with PFS was observed underscores the added clinical value of the investigated Lorentzian‐fit‐based CEST contrasts. Nevertheless, the finding that the MTRRexMT values in tumor volumes were lower compared to NAWM, which is in line with previously published results for contrasts of the ssMT,22,37 indicates that the investigated Lorentzian‐fit‐based contrasts have stronger remaining T1 contributions compared to the investigated relaxation‐compensated contrasts.This study has several limitations. Since true disease progression is secured by histology only in a fraction of cases, relying on RANO assessment for determination of clinical endpoints is a major weakness of this study due to well‐known shortcomings of the RANO criteria. Therefore, we additionally included PFS analysis and long‐term follow‐up (median 9.2 mo) to enable best possible ground truth assessment in the clinical routine setting. Furthermore, the included study participants had different types of diffuse gliomas which were resected to varying extents and treated with varying types and doses of radiation. To overcome such limitations, future studies should aim for even larger patient cohorts (e.g., in multicenter trials) with longitudinal CEST MRI data acquisition. In this context, leading experts in the field have recently published consensus recommendations for APTwasym CEST imaging in neuro‐oncologic applications in order to provide a rationale for optimized APTwasym imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods.33CONCLUSIONSIn patients with glioma at the first follow‐up 4 – 6 wk after the completion of radiotherapy, MTconst, PeakAreaAPT, and APTwasym can predict clinical outcomes by means of PFS, whereas MTconst can distinguish pseudoprogression from disease progression, as assessed according to RANO criteria. These findings indicate that Lorentzian‐fit‐based and asymmetry‐based CEST metrics of the APT and ssMT early after the completion of radiotherapy might have synergistic potential for supporting clinical decision making in patients with glioma.ACKNOWLEDGMENTSThis study was funded by the German Research Foundation (Grant No. 445704496). We very much appreciate the careful work of all study nurses and radiographers who have enabled this work. Namely Jessica Engelhart, Vanessa Peregovic, Jennifer Reimold, Heike Streib‐Retzbach, Regula Gnirs, Cora Weyrich, Marek Wolski, Marina Schuricht, Anne‐Mareike Rau, Susanne Metzner and Sina Kammer. Open Access funding enabled and organized by Projekt DEAL.REFERENCESOstrom QT, Cioffi G, Waite K, Kruchko C, Barnholtz‐Sloan JS. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2014–2018. Neuro Oncol. 2021;23:iii1‐iii105. doi:10.1093/neuonc/noab200Weller M, van den Bent M, Preusser M, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol. 2021;18:170‐186. doi:10.1038/s41571‐020‐00447‐zStupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352:987‐996. doi:10.1056/NEJMoa043330Verhaak RG, Hoadley KA, Purdom E, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17:98‐110. doi:10.1016/j.ccr.2009.12.020Suzuki H, Aoki K, Chiba K, et al. Mutational landscape and clonal architecture in grade II and III gliomas. Nat Genet. 2015;47:458‐468. doi:10.1038/ng.3273Wick W, Roth P, Hartmann C, et al. Long‐term analysis of the NOA‐04 randomized phase III trial of sequential radiochemotherapy of anaplastic glioma with PCV or temozolomide. Neuro Oncol. 2016;18:1529‐1537. doi:10.1093/neuonc/now133Zikou A, Sioka C, Alexiou GA, Fotopoulos A, Voulgaris S, Argyropoulou MI. Radiation necrosis, pseudoprogression, pseudoresponse, and tumor recurrence: imaging challenges for the evaluation of treated gliomas. Contrast Media Mol Imaging. 2018;2018:6828396. doi:10.1155/2018/6828396Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high‐grade gliomas: response assessment in neuro‐oncology working group. J Clin Oncol. 2010;28:1963‐1972. doi:10.1200/jco.2009.26.3541Chukwueke UN, Wen PY. Use of the response assessment in neuro‐oncology (RANO) criteria in clinical trials and clinical practice. CNS Oncologia. 2019;8:CNS28. doi:10.2217/cns‐2018‐0007Henriksen OM, Del Mar A‐TM, Figueiredo P, et al. High‐grade glioma treatment response monitoring biomarkers: a position statement on the evidence supporting the use of advanced MRI techniques in the clinic, and the latest bench‐to‐bedside developments. Part 1: perfusion and diffusion techniques. Front Oncologia. 2022;12:810263. doi:10.3389/fonc.2022.810263Booth TC, Wiegers EC, Warnert EAH, et al. High‐grade glioma treatment response monitoring biomarkers: a position statement on the evidence supporting the use of advanced MRI techniques in the clinic, and the latest bench‐to‐bedside developments. Part 2: spectroscopy, chemical exchange saturation, multiparametric imaging, and radiomics. Front Oncologia. 2021;11:811425. doi:10.3389/fonc.2021.811425Liu J, Li C, Chen Y, et al. Diagnostic performance of multiparametric MRI in the evaluation of treatment response in glioma patients at 3T. J Magn Reson Imaging. 2020;51:1154‐1161. doi:10.1002/jmri.26900Ma B, Blakeley JO, Hong X, et al. Applying amide proton transfer‐weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas. J Magn Reson Imaging. 2016;44:456‐462. doi:10.1002/jmri.25159Park KJ, Kim HS, Park JE, Shim WH, Kim SJ, Smith SA. Added value of amide proton transfer imaging to conventional and perfusion MR imaging for evaluating the treatment response of newly diagnosed glioblastoma. Eur Radiol. 2016;26:4390‐4403. doi:10.1007/s00330‐016‐4261‐2Park YW, Ahn SS, Kim EH, et al. Differentiation of recurrent diffuse glioma from treatment‐induced change using amide proton transfer imaging: incremental value to diffusion and perfusion parameters. Neuroradiology. 2021;63:363‐372. doi:10.1007/s00234‐020‐02542‐5Mehrabian H, Myrehaug S, Soliman H, Sahgal A, Stanisz GJ. Evaluation of glioblastoma response to therapy with chemical exchange saturation transfer. Int J Radiat Oncol Biol Phys. 2018;101:713‐723. doi:10.1016/j.ijrobp.2018.03.057Mehrabian H, Myrehaug S, Soliman H, Sahgal A, Stanisz GJ. Quantitative magnetization transfer in monitoring glioblastoma (GBM) response to therapy. Sci Rep. 2018;8:2475. doi:10.1038/s41598‐018‐20624‐6Meissner JE, Korzowski A, Regnery S, et al. Early response assessment of glioma patients to definitive chemoradiotherapy using chemical exchange saturation transfer imaging at 7T. J Magn Reson Imaging. 2019;50:1268‐1277. doi:10.1002/jmri.26702Paech D, Dreher C, Regnery S, et al. Relaxation‐compensated amide proton transfer (APT) MRI signal intensity is associated with survival and progression in high‐grade glioma patients. Eur Radiol. 2019;29:4957‐4967. doi:10.1007/s00330‐019‐06066‐2Wu B, Warnock G, Zaiss M, et al. An overview of CEST MRI for non‐MR physicists. EJNMMI Phys. 2016;3:19. doi:10.1186/s40658‐016‐0155‐2Goerke S, Soehngen Y, Deshmane A, et al. Relaxation‐compensated APT and rNOE CEST‐MRI of human brain tumors at 3T. Magn Reson Med. 2019;82:622‐632. doi:10.1002/mrm.27751Chan RW, Chen H, Myrehaug S, et al. Quantitative CEST and MT at 1.5T for monitoring treatment response in glioblastoma: early and late tumor progression during chemoradiation. J Neurooncol. 2021;151:267‐278. doi:10.1007/s11060‐020‐03661‐yRegnery S, Adeberg S, Dreher C, et al. Chemical exchange saturation transfer MRI serves as predictor of early progression in glioblastoma patients. Oncotarget. 2018;9:28772‐28783. doi:10.18632/oncotarget.25594Joo B, Han K, Ahn SS, et al. Amide proton transfer imaging might predict survival and IDH mutation status in high‐grade glioma. Eur Radiol. 2019;29:6643‐6652. doi:10.1007/s00330‐019‐06203‐xWen PY, Chang SM, Van den Bent MJ, Vogelbaum MA, Macdonald DR, Lee EQJJCO. Response assessment in neuro‐oncology clinical trials. J Clin Oncol. 2017;35:2439‐2449.Deshmane A, Zaiss M, Lindig T, et al. 3D gradient echo snapshot CEST MRI with low power saturation for human studies at 3T. Magn Reson Med. 2019;81:2412‐2423. doi:10.1002/mrm.27569Zaiss M, Ehses P, Scheffler K. Snapshot‐CEST: optimizing spiral‐centric‐reordered gradient echo acquisition for fast and robust 3D CEST MRI at 9.4 T. NMR Biomed. 2018;31:e3879. doi:10.1002/nbm.3879Schuenke P, Windschuh J, Roeloffs V, Ladd ME, Bachert P, Zaiss M. Simultaneous mapping of water shift and B1 (WASABI)‐application to field‐inhomogeneity correction of CEST MRI data. Magn Reson Med. 2017;77:571‐580. doi:10.1002/mrm.26133Breitling J, Korzowski A, Kempa N, et al. Motion correction for three‐dimensional chemical exchange saturation transfer imaging without direct water saturation artifacts. NMR Biomed. 2022;35:e4720. doi:10.1002/nbm.4720Breitling J, Deshmane A, Goerke S, et al. Adaptive denoising for chemical exchange saturation transfer MR imaging. NMR Biomed. 2019;32:e4133. doi:10.1002/nbm.4133Windschuh J, Zaiss M, Meissner JE, et al. Correction of B1‐inhomogeneities for relaxation‐compensated CEST imaging at 7T. NMR Biomed. 2015;28:529‐537. doi:10.1002/nbm.3283Zhou J, Heo HY, Knutsson L, van Zijl PCM, Jiang S. APT‐weighted MRI: techniques, current neuro applications, and challenging issues. J Magn Reson Imaging. 2019;50:347‐364. doi:10.1002/jmri.26645Zhou J, Zaiss M, Knutsson L, et al. Review and consensus recommendations on clinical APT‐weighted imaging approaches at 3T: application to brain tumors. 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Eur Radiol. 2022;32:101‐111. doi:10.1007/s00330‐021‐08175‐3 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Magnetic Resonance in Medicine Wiley

Semi‐solid MT and APTw CEST‐MRI predict clinical outcome of patients with glioma early after radiotherapy

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Wiley
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© 2023 International Society for Magnetic Resonance in Medicine
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1522-2594
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10.1002/mrm.29746
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Abstract

INTRODUCTIONDiffuse gliomas have an incidence of 5.2 / 100000 per year and are the second most common class of primary brain tumors over all.1 Therapy involves maximal surgical resection followed by radiotherapy or concurrent radio/chemotherapy and adjuvant chemotherapy, depending on World Health Organization (WHO) grade.2 Under current clinical standard of care, life expectancy depends on histopathologic subtype, tumor size, completeness of resection and patient age.3–6 Time to treatment failure amounts to 1.4 y for glioblastoma (GBM), 4.5 y for astrocytomas with oncogenic mutations of the isocitrate dehydrogenase isotype 1 or 2 (IDH‐mutant) and 9.8 y for IDH‐mutant oligodendrogliomas.6 Treatment‐related brain damage and tumor progression can equally result in blood–brain‐barrier damage and increasing gliosis, making differentiation on MRI challenging.7 Misdiagnosis of either can result in severe consequences for the patient, as they require different therapeutic strategies. Growing awareness to pseudoprogression (PP), occurring in up to 30 % of all cases following radiation, has led to the introduction of response assessment in neuro‐oncology (RANO) criteria in 2010.8,9 However, RANO requires longitudinal follow‐up as well as integration of image‐based and clinical parameters, making correct response assessment challenging. For this reason, innovative MRI modalities such as diffusion‐ and perfusion‐weighted imaging, PET‐MRI and MRI spectroscopy have been evaluated for their clinical value in patients with glioma before and after therapy over the past two decades. This has led to the incorporation of some of these techniques into clinical routine.10,11CEST MRI is an innovative and contrast agent‐free MRI approach, for which several groups have demonstrated great potential for therapy response assessment and prognostication in patients with glioma undergoing radio‐ and chemotherapy.12–19 However, CEST contrasts are heavily dependent on the metrics used for their extraction from the Z‐spectrum and the applied magnetic field strengths.20,21 There is a fast growing body of evidence in the literature about the prognostic capacities of asymmetry‐based, relaxation‐compensated and Lorentzian‐fit‐based CEST contrasts of the amide proton transfer (APT) and semi‐solid magnetization transfer (ssMT), that is for predicting therapy response at different field strengths of 1.5, 3, and 7 T.16–19,22–24 Furthermore, several studies have demonstrated the diagnostic capacities of asymmetry‐based APT‐weighted (APTwasym) CEST imaging for differentiation of treatment‐related changes and glioma progression, as early as 3 mo after the completion of radiotherapy.12–14 However, results from cross‐sectional prospective clinical studies, comparing the prognostic and diagnostic potential of the APTwasym, as well as relaxation‐compensated (MTRRexAPT and MTRrexMT) and Lorentzian‐fit‐based (PeakAreaAPT and MTconst) CEST contrasts of the APT and ssMT early after the completion of radiotherapy in a larger cohort of patients with glioma have not been reported before. Therefore, the purpose of this study was to compare the potential of the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst for response assessment and prediction of progression‐free survival (PFS) in patients with glioma in a prospective clinical trial at the first follow‐up 4 – 6 wk after the completion of radiotherapy.METHODSStudy designThe local institutional review board committee approved this prospective study. Written informed consent was obtained from each participant prior to study inclusion.From September 2018 until December 2021 72 study participants (61 with initial disease and 11 with relapsing/progressive disease) being treated for diffuse glioma at the department of radiation‐oncology of the University Hospital Heidelberg were prospectively enrolled. MRI scans were acquired at the first follow‐up 4 – 6 wk after the completion of radiotherapy. Inclusion criteria were minimum age of 18 y, Karnofsky‐Performance‐Score of 50 or higher and legal capacity to consent to study inclusion. Eleven participants had been excluded from analysis due to heavy motion artifacts (two participants), incomplete data sets (seven participants), large perioperative infarction (one participant), and missing clinical follow‐up data at data cut off on January 26, 2022 (one participant) (Figure 1).1FIGUREFlow chart of the study. Seventy‐two participants were prospectively included in the study and underwent CEST MRI 4 – 6 wk following completion of radiotherapy. Eleven participants had to be excluded from evaluation due to the given reasons. Association of CEST contrast values with therapy response assessment at the first follow‐up was tested by Mann Whitney‐U‐test and ROC analysis in all 61 evaluable study participants (all participants, n = 61) and 42 participants with residual contrast enhancement on MRI (participants with CE, n = 42). Gliomas with midline locations were excluded for PFS assessment, since these tumors are known to be associated with an especially poor prognosis (participants with CE, n = 37). CE, contrast enhancement; KPI, Karnovsky Performance Score; PFS, progression‐free survival; ROC, receiver operating characteristic.Assessment of therapy response at the first follow‐up 4 – 6 wk after the completion of radiotherapy and PFS were performed according to the revised RANO criteria based on clinical data and MRI with a median follow‐up of 9.2 mo (range 1.6 – 40.47 mo).9,25 Pseudoprogression was distinguished from progressive disease based on follow‐up. PFS was assessed as the time between the start of radiotherapy and disease progression. Final decisions were made in consensus by two radiologists with 5 and 10 y of experience in neuroimaging (N.K. and D.P.). To account for prescribed antiangiogenic drugs, potential adaptions of therapy regimens and clinical status changes, assessment results were matched with institutional interdisciplinary tumor‐board decisions.HistologyHistology after biopsy or surgical resection was available for 60 out of 61 study participants. Histology of one participant being treated for newly diagnosed glioma with midline location could not be secured due to poor clinical status. Clinical routine work‐up included determination of IDH‐, ATRX‐, LOH1p19q‐, H3K27M‐, and MGMT‐ status and glioma classification was performed according to WHO 2016 criteria. For detailed information on tumor histopathologies of the study cohort, please see Table 1.1TABLEClinical characteristics of all evaluated 61 study participants who received CEST MRI in the first follow‐up (FU) 4 – 6 wk after completion of radiotherapyCharacteristicNumber (n)PercentageAge at diagnosisMean 59 ± 1661SexMale3557%Female2643%Glioma locationHemispheric5592%Midline68%Treatment forInitial disease5387%Relapsing disease813%TherapyRadiation1423%Chemoradiation4777%Debulking surgery3862%RANOProgressive disease1931%Stable disease3456%Pseudoprogression813%Residual contrast enhancement on MRIYes4269%No1931%DiagnosisGBM4472%Gliosarcoma23%Midline glioma (H3K27M)23%Astrocytoma813%Oligodendroglioma47%Not secured12%WHO gradeII610%III35%IV5184%N/A12%IDH statusIDHwt4675%IDHmut1220%N/A35%MGMT promotor methylationYes3354%No2033%N/A813%Note: “Therapy” indicates whether participants received radiotherapy only or chemoradiation, and states the number of participants that underwent debulking surgery prior to radiotherapy. “Diagnosis” and “WHO grade” describe histopathological subtypes and tumor grades according to the 2016 WHO criteria for primary central nervous system tumors.Abbreviations: GBM, glioblastoma; H3K27M, subsitution of lysine residue 27 on histone 3 for methionine (oncogenic histone variant); IDHmut, IDH‐mutant; IDHwt, IDH‐wildtype; RANO, response assessment in neuro‐oncology; WHO, World Health Organization.Image acquisition and postprocessingAll images were acquired on a 3 T whole‐body MR scanner (MAGNETOM Prisma; Siemens Healthineers) with a 64‐channel receive head/neck coil and an integrated transmit body coil. Processing of CEST data was performed using customized scripts in Matlab® (Mathworks) version 2019b.Lorentzian‐fit based and relaxation‐compensated CEST imaging of the APT and ssMTThe image‐readout parameters (matrix = 128 × 104 × 16, resolution = 1.7 × 1.7 × 3 mm3) of the 3D spiral‐centric‐reordered gradient‐echo acquisition sequence (i.e., snapshot CEST26,27) and the CEST presaturation, originally from Zaiss et al.,27 were set up as described in detail by Goerke et al.21Presaturation was performed with a mean amplitude of B1 = flip angle/(γ·tp) = 0.6 and 0.9 μT respectively. For both CEST scans, 57 unequally distributed frequency offsets between ±250 ppm and two M0 at −300 ppm for normalization were acquired, resulting in a total measurement time of 7:36 min per CEST scan.The WASABI28 (3:41 min) approach, which provides B0 and B1 maps, was used with the same image readout and a similar presaturation with an equally distributed sampling of 31 offsets between ±2 ppm.For postprocessing, the CEST and WASABI data were coregistered using a rigid registration algorithm as described by.29 Subsequently, all Z‐spectra were B0 corrected by shifting them along Δω according to the B0‐map. Finally, the Z‐spectra were denoised using a principle component analysis based algorithm.30Two different Lorentzian‐fit based evaluation models were applied to isolate the signal contributions.The first model (MTRRexAPT and MTRRexMT) is additionally compensated for spillover of the direct water saturation and MT influences. These contrasts are referred to as “relaxation compensated” in the following. This model is based on a four‐pool Lorentzian‐fit between ±250 ppm around the water offset (0 ppm: direct water saturation, 3.5 ppm: APT, −3.5 ppm: rNOE, and −2.5 ppm: ssMT).21 The MTRRex contrasts of the APT and ssMT were calculated by MTRRex=1Z−1Zref$$ \mathrm{MT}{\mathrm{R}}_{\mathrm{R}\mathrm{ex}}=\frac{1}{Z}-\frac{1}{Z_{\mathrm{ref}}} $$. Additional B1‐correction was performed with the two‐point “contrast‐correction” method as proposed by Windschuh et al.31The second data fit approach (PeakAreaAPT and MTconst) was adapted from Mehrabian et al.,16 and only uses the region between ±6 ppm to fit a four‐pool Lorentzian (APT, AMINE, rNOE, DS) and a constant ssMT to the data of the CEST measurement at 0.6 μT:S(Δ)=1−MT+∑i=14Ai1+Δ−Δ0i0.5wi2$$ \boldsymbol{S}\left(\boldsymbol{\Delta} \right)=\mathbf{1}-\left(\mathbf{MT}+{\sum}_{\boldsymbol{i}=\mathbf{1}}^{\mathbf{4}}\frac{{\boldsymbol{A}}_{\boldsymbol{i}}}{\mathbf{1}+{\left(\frac{\boldsymbol{\Delta} -{\boldsymbol{\Delta}}_{\mathbf{0i}}}{\mathbf{0.5}{\boldsymbol{w}}_{\boldsymbol{i}}}\right)}^{\mathbf{2}}}\right) $$With Ai,Δ0i,wi=[amplitude,center frequency,width]$$ \left[{\boldsymbol{A}}_{\boldsymbol{i}},{\boldsymbol{\Delta}}_{\mathbf{0i}},{\boldsymbol{w}}_{\boldsymbol{i}}\right]=\left[\mathrm{amplitude},\mathrm{center}\ \mathrm{frequency},\mathrm{width}\right] $$. Afterward, the constant ssMT offset (MTconst) and the area under curve of the Lorentzian describing the APT‐peak (PeakArea APT) were calculated. The PeakAreaAPT and MTconst are collectively being referred to as Lorentzian‐fit‐based contrasts in the following. A representative fitted Z‐spectrum and residuals of both models can be found in Figure S1. The displayed Z‐spectrum is located in the CE‐region of the participant with progressive disease shown in Figure 2.2FIGUREThe figure shows anatomical images and contrast maps of two exemplary study participants with progressive disease (PD–top) and pseudoprogression (PP–bottom). Depicted from left to right in the top rows are: T1w post‐contrast (T1CE), APTwasym, MTRRexAPT, and MTRRexMT. Bottom rows show from left to right: T2w‐FLAIR, PeakAreaAPT, and MTconst. Asterisks (*) are indicating contrast‐enhancing tumor tissue (CE). Arrows (→) are indicating glioma‐associated edema (ED–hyperintense in T2w‐FLAIR). In this case the MTRRexAPT demonstrated markedly increased contrast values confined to CE in the participant with PD, whilst no such contrast pattern can be seen in the participant with PP. Visual correlation of the APTwasym, MTRRexMT, PeakAreaAPT, and MTconst contrast values with CE and ED were less clear. PeakAreaAPT demonstrated very noisy contrast maps.Asymmetry‐based CEST imaging of the APTwasymAPTwasym imaging was realized with the identical pulse sequence and image readout as the CEST measurement in the previous section. Presaturation was achieved according to Zhou J, et al.32 in line with recent consensus and recommendation guidelines33 employing four rectangular RF pulses with a B1 of 2 μT, pulse length (tp) of 0.2 s and 95% duty cycle (tsat = 0.83 s). Sixteen offsets were acquired at ±4 (1), ±3.75 (2), ±3.5 (2), ±3.25 (2), and ±3 (1) ppm. Additionally, an M0 at −300 ppm was acquired for normalization purposes.Similar coregistration and B0‐correction approaches were used as described in the previous section. Afterward, the APTwasym=Z(−3.5ppm)−Z(3.5ppm)$$ \mathrm{APT}{\mathrm{w}}_{\mathrm{asym}}=Z\left(-3.5\ \mathrm{ppm}\right)-Z\left(3.5\ \mathrm{ppm}\right) $$ contrast was calculated from the corrected data.Quantitative T1 mappingGiven that the investigated CEST contrasts are influenced by T1 to varying degrees, we also assessed the association of T1 with the RANO assessment results and PFS. Quantitative mapping of the longitudinal relaxation time of water (T1) was performed by the application of a saturation recovery sequence21 with recovery times trec$$ \left({t}_{\mathrm{rec}}\right) $$ of 0.1, 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, 3.5, 5.0, 7.5, and 10.0 s and Mztrec=M0+Mz(0)−M0⋅e−trec/T1$$ {M}_z\left({t}_{\mathrm{rec}}\right)={M}_0+\left({M}_z(0)-{M}_0\right)\cdot {e}^{-{t}_{\mathrm{rec}}/\mathrm{T}1} $$.Tumor segmentations3D segmentations of contrast‐enhancing tumor (CE) volumes, whole tumor (WT) volumes, and normal appearing white matter (NAWM) were performed by a radiologist with 5 y of experience in neuroimaging (N.K.) on contrast‐enhanced T1w and T2w fluid‐attenuated inversion recovery (T2w‐FLAIR) images with a custom‐made segmentation tool in Matlab® (Mathworks) version 2019b. Segmentations were performed in all evaluable study participants (all participants, n = 61) and in one sub‐cohort encompassing only participants with residual contrast enhancement on MRI (participants with CE, n = 42) (Figure 1). The WT volume in the study participants with residual contrast enhancement encompassed CE plus peritumoral T2w‐FLAIR hyperintense signal alterations (WT [participants with CE]). In the entire study sample, the WT volumes encompassed additional 19 cases that contained only peritumoral T2w‐FLAIR hyperintense signal alterations (WT [all participants]), given the absence of residual contrast enhancement in these patients (please see Table 1). Relevant surgically induced changes (i.e., hemorrhage) were excluded by visual correlation with T1w and T2w*$$ {\mathrm{T}}_{2\mathrm{w}}^{\ast } $$ images.Statistical analysesReceiver operating characteristic (ROC) analyses and Mann–Whitney‐U‐test were performed to test for quantitative differences of CEST contrast values between participants with stable and progressive disease, as well as pseudoprogressive and progressive disease. Kaplan–Meier curves and logrank‐tests were used for analyses of PFS in association with mean CEST contrast values per ROI.In six participants, tumors showed involvement of midline structures as thalamus, midbrain, or brainstem. Diffuse midline gliomas are common in pediatric patients, but rare in adults.34 Since glioma midline location is known to be associated with worse clinical outcome, midline gliomas were additionally excluded for PFS analysis (participants with CE, n = 37)35 (Figure 1).All statistical analyses were performed using in‐house software in Matlab® (Mathworks) version 2019b.RESULTSDemographics of study participantsSeventy‐two study participants (mean age 59 ± 16 y; 43 male) underwent CEST MRI at the first follow‐up 4 – 6 wk after the completion of radiotherapy.Out of 61 evaluable participants (35 male, 26 female; mean age 59 ± 16 y) 19 had progressive disease (PD) and 8 had pseudoprogression (PP). Overall, 42 participants were diagnosed as having stable disease (SD), which included the eight participants with PP. Median PFS was 5.3 mo (range 1.5 – 27.1). Detailed characteristics of study participants are provided in Table 1.MTconst showed a stronger association with early response assessment according to RANO compared to PeakAreaAPT and MTRRexMTContrast maps of the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst for two study participants, one with PD, and one with PP, are illustrated in Figure 2.When comparing participants with PD and SD, the MTRRexMT (participants with CE: CE 0.41 ± 0.05 vs. 0.36 ± 0.07, p = 0.02; WT [participants with CE] 0.49 ± 0.05 vs. 0.45 ± 0.06, p = 0.05) and MTconst (all participants: WT [all participants] 0.49 ± 0.05 vs. 0.46 ± 0.07, p = 0.01; participants with CE: CE 0.41 ± 0.05 vs. 0.36 ± 0.07, p < 0.01, WT [participants with CE] 0.49 ± 0.05 vs. 0.45 ± 0.06, p < 0.01) showed higher contrast values for tumor tissue in participants with PD, whereas the PeakAreaAPT showed lower contrast values for participants with PD (participants with CE: CE 0.24 ± 0.10 vs. 0.33 ± 0.13, p = 0.02; WT [participants with CE] 0.23 ± 0.12 vs. 0.31 ± 0.13, p = 0.04). When comparing participants with PD and PP, the MTconst was the only contrast that showed significant differences, with higher contrast values for tumor tissue in participants with PD (participants with CE: CE 0.18 ± 0.02 vs. 0.16 ± 0.03, p = 0.03; WT [participants with CE] 0.18 ± 0.02 vs. 0.15 ± 0.03, p = 0.05) (Figure 3).3FIGUREViolin plots of CEST contrast values for study participants with residual contrast enhancement on MRI (n = 42). Top row shows from left to right graphs for APTwasym, MTRRexAPT and MTRRexMT. Bottom row shows graphs for PeakAreaAPT (left) and MTconst (right). Graphs show absolute CEST contrast values of contrast‐enhancing tumor volume (CE), whole tumor volume (WT), and normal appearing white matter (NAWM). Red columns show cases with progressive disease (PD, n = 19), green columns with stable disease (SD, n = 23) and blue columns with pseudoprogression (PP, n = 8). Green columns (SD) also include cases with PP, given the fact that PP is formally counted as SD according to the response assessment in neuro‐oncology (RANO) criteria. Only MTconst showed for CE and WT significantly different contrast values for cases with PD compared to SD (CE p < 0.01, WT p < 0.01) and PP (CE p = 0.03, WT p = 0.05). When comparing study participants with PD and SD only, the MTRRexMT showed higher contrast values for CE (p = 0.02) and WT (p = 0.05) in participants with PD, whereas the PeakAreaAPT showed higher contrast values for participants with SD (CE p = 0.02, WT p = 0.04).ROC analyses yielded matching results. When comparing participants with PD and SD, areas under the curve (AUC) for the MTRRexMT were 0.71 for CE (p = 0.02) and 0.68 for WT (participants with CE) (p = 0.05) in participants with residual contrast enhancement on MRI (participants with CE). In the same sub‐cohort AUCs for the PeakAreaAPT were 0.71 (p = 0.02) for CE and 0.69 for WT (participants with CE) (p = 0.04). For the MTconst AUCs were 0.79 (p < 0.01) for CE and 0.78 (p < 0.01) for WT (participants with CE) in participants with residual contrast enhancement on MRI and 0.72 (p = 0.01) for WT (all participants) in all evaluable participants (all participants). When testing for differences between participants with PD and PP the MTconst yielded AUCs of 0.79 (p = 0.02) for CE and 0.75 (p = 0.05) for WT (participants with CE) in participants with residual contrast enhancement on MRI. All other contrasts did not enable distinction between participants with PD and PP. Furthermore, the APTwasym and MTRRexAPT were not associated with RANO assessment at the first follow‐up. ROC curves testing for differences in mean contrast values between participants with PD and SD for all CEST contrasts, as well as differences between participants with PD and PP for the MTconst are shown in Figures 4 and 5.4FIGUREReceiver operating characteristic (ROC) curves testing for capacities of the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst to differentiate between study participants with progressive disease (PD) and stable disease (SD) according to the response assessment in neuro‐oncology (RANO) criteria. Participants with pseudoprogression (PP) are included in SD, given the fact that PP is formally counted as SD according to the response assessment in neuro‐oncology (RANO) criteria. Given are area under the curve (AUC) values for whole tumor (WT [all participants], light blue) volume in all study participants (n = 61), as well as contrast‐enhancing tumor volumes (CE, red) and WT (WT [participants with CE], dark blue) in participants with residual contrast enhancement on MRI (n = 42). In all participants, MTconst was the only contrast that could differentiate PD and SD (WT [all participants], p = 0.01). After stratification for participants with residual contrast enhancement on MRI MTRRexMT (CE p = 0.02, WT [participants with CE] p = 0.05), PeakAreaAPT (CE p = 0.02, WT [participants with CE] p = 0.04), and MTconst (CE p < 0.01, WT [participants with CE] p < 0.01) could differentiate between participants with PD and SD.5FIGUREReceiver operating characteristic curves testing for the capacity of the MTconst to differentiate between participants with progressive disease (PD, n = 19) and pseudo‐progression (PP, n = 8) according to response assessment in neuro oncology criteria in participants with residual contrast enhancement on MRI (n = 42). AUCs were significant for contrast‐enhancing tumor volume (CE, p = 0.02, red) and whole tumor volume (WT [participants with CE], p = 0.05, blue).APTwasym, PeakAreaAPT, and MTconst signal intensities are associated with PFSIn Kaplan–Meier analyses, the APTwasym, PeakAreaAPT, and MTconst showed significant association with PFS. Participants with APTwasym and MTconst contrast values above the median of the (sub‐)cohort showed shorter time to progression compared to participants with contrast values below the median. For the APTwasym, PFS was 127 versus 267 days (HR = 2.47, p = 0.01) for WT (all participants) in all evaluable participants (all participants) and 71 versus 174 days (HR = 2.63, p = 0.02) for CE after stratification for participants with residual contrast enhancement on MRI (participants with CE). For the MTconst PFS was 68 versus 172 days (HR = 3.04, p = 0.01) for CE and 62 versus 186 days (HR = 4.75, p < 0.01) for WT (participants with CE) in participants with residual contrast enhancement on MRI. On the contrary, for the PeakAreaAPT participants with contrast values above the median showed longer PFS with 173 versus 68 days (HR = 0.39, p = 0.03) for CE and a similar trend (148 vs. 70 days; HR = 0.48, p = 0.08) for WT (participants with CE) in participants with residual contrast enhancement on MRI. The MTRRexAPT and MTRRexMT did not show any association with PFS (Figure 6).6FIGUREKaplan–Meier analyses testing for the association of progression‐free survival (PFS) with the APTwasym contrast values of whole tumor volume (WT) in all study participants (all participants, number at risk n = 61), as well as the association of PFS with the APTwasym, MTRRexAPT, MTRRexMT, PeakAreaAPT, and MTconst contrast values for contrast‐enhancing tumor volume (CE) and WT in participants with residual contrast enhancement on MRI (participants with CE, number at risk n = 37). The APTwasym was the only contrast that was associated with PFS in all study participants (top row first column, HR = 2.47, p = 0.01) with shorter PFS of participants showing higher contrast values for WT (dark red). After stratification for participants with residual contrast enhancement on MRI, the APTwasym (bottom row right column, HR = 2.63, p = 0.02), PeakAreaAPT (bottom row middle column, HR = 0.39, p = 0.03), and MTconst (bottom row left column, HR = 3.04, p = 0.01) were associated with PFS, with APTwasym and MTconst showing higher contrast values for CE (light red) and WT (dark red) in participants with shorter survival. The PeakAreakAPT showed higher contrast values in participants with longer survival. For PFS analyses, participants with midline glioma location (n = 5) where additionally excluded from the subgroup of participants with residual contrast enhancement on MRI.T1 is weakly associated with RANO assessment but not with PFSWhen comparing participants with PD and SD, the AUC for T1 for the WT (all participants) volumes was 0.66 (p = 0.047) (Figure S2). However, there was no association of T1 with the RANO assessment results after the stratification for presence of residual contrast enhancement on MRI (Figure S2). Furthermore, there were no significant differences of T1 between participants with PD and SD (Figure S3) and no significant association of T1 with PFS (Figure S4).DISCUSSIONSeveral groups have demonstrated the potential of CEST MRI for therapy response assessment in diffuse gliomas. However, so far comparative cross‐sectional results of asymmetry‐based (APTw), relaxation‐compensated (MTRRexAPT and MTRRexMT), and Lorentzian‐fit‐based (PeakAreaAPT and MTconst) contrasts of the APT and ssMT at the first follow‐up 4 – 6 wk after the completion of radiotherapy at 3 T have not been reported before. In this prospective clinical study, we demonstrated that the MTconst (AUC = 0.79, p < 0.01) showed higher association with RANO response assessment than the PeakAreaAPT (AUC = 0.71, p = 0.02) and the MTRRexMT (AUC = 0.71, p = 0.02), and enabled differentiation of study participants with pseudoprogression (n = 8) from those with disease progression (AUC = 0.79, p = 0.02) at the first follow‐up after the completion of radiotherapy. Furthermore, the MTconst (HR = 3.04, p = 0.01), PeakAreaAPT (HR = 0.39, p = 0.03), and APTwasym (HR = 2.63, p = 0.02) were associated with PFS. The MTRRexAPT was not associated with therapy response or PFS, as assessed by RANO criteria.In prior non‐cross sectional clinical studies with 30 – 65 patients, Liu et al., Ma et al., and Park et al. have demonstrated that APTwasym contrast values were associated with RANO assessment at 3 T and enabled differentiation of glioma progression and radiation necrosis as early as 3 mo after the completion of radiotherapy.12–14 Others have reported in prospective clinical studies at 1.5, 3, and 7 T with 19 – 71 patients that the APTwasym contrast was associated with PFS before treatment onset, whilst the prognostic performance of this technique rapidly decreased in patients after the start of therapy.12–14,16,22–24 We only observed an association of the APTwasym with PFS but not with therapy response at the first follow‐up, as assessed by RANO criteria.In prospective clinical studies at 7 T with 20 and 26 patients with newly diagnosed gliomas, Regnery et al. and Paech et al. reported that the MTRRexAPT could predict therapy response and survival, with higher contrast values for patients with early disease progression.19,23 However, Meissner et al. did not find any association of MTRRexAPT contrast values with treatment response after the start of radiotherapy.18 Mirroring the findings by Meissner et al., we also did not find any association of MTRRexAPT contrast values with therapy response and PFS at the first follow‐up after the completion of radiotherapy at 3 T. For the MTRRexMT, we only observed an association with therapy response but not with PFS. To our knowledge, this is the first report on the association of the MTRRexMT with clinical outcomes in patients with glioma following radiotherapy.In a prospective clinical study with 19 patients, Mehrabian et al. have demonstrated that MTconst could prognosticate therapy response, whereas MTconst after the start of therapy and PeakAreaAPT did not show any association with clinical outcome.16 In our study with 61 evaluable participants, MTconst and PeakAreaAPT demonstrated the highest association with therapy response and PFS. Furthermore, the MTconst could also differentiate between participants assessed as having progressive and pseudoprogressive disease.Generally, for the interpretation of these study findings it is essential to consider well‐known diagnostic limitations of the RANO criteria. RANO compares volumetric changes of contrast enhancement on T1w and abnormal T2w‐FLAIR signal hyperintensities on consecutive MRI exams under consideration of clinical status and application of antiangiogenic drugs. However, in clinical practice blood–brain‐barrier disruption and edema caused by disease progression or radiotherapy are in most cases not easily distinguishable and frequently occur to variable degrees simultaneously. Furthermore, other factors such as therapeutically induced granulation tissue and inflammatory processes might also contribute to respective changes on MRI.7–9,25 Regarding CEST, the Lorentzian‐fit‐based PeakAreaAPT and MTconst contrasts have several other contributions, such as T1, spill‐over, and unwanted ssMT in case of the PeakAreaAPT.16,36 The asymmetry‐based APTw includes, in addition to those, contributions from the relayed nuclear Overhauser effect (rNOE).32,36 In relaxation‐compensated CEST contrasts the APT and ssMT peaks are corrected for spill‐over effects, even though also these contrasts have certain T1 contributions.21,36 In this regard, it can be speculated that the superior performance of the Lorentzian‐fit‐based contrasts in this study might be explained by a summation of several biomolecular changes which influence T1 relaxation, spill‐over, and CEST effects as APT, rNOE, and ssMT to varying degrees. T1 is heavily influenced by the water content of examined volumes. Therefore, the higher MTconst values in tumor tissue compared to NAWM, which is reflected by higher T1 (Figure S3), might be linked to the tumor‐associated disruption of the blood brain barrier and resulting edema. However, the fact that in this study only a weak association of T1 with therapy response according to RANO but not with PFS was observed underscores the added clinical value of the investigated Lorentzian‐fit‐based CEST contrasts. Nevertheless, the finding that the MTRRexMT values in tumor volumes were lower compared to NAWM, which is in line with previously published results for contrasts of the ssMT,22,37 indicates that the investigated Lorentzian‐fit‐based contrasts have stronger remaining T1 contributions compared to the investigated relaxation‐compensated contrasts.This study has several limitations. Since true disease progression is secured by histology only in a fraction of cases, relying on RANO assessment for determination of clinical endpoints is a major weakness of this study due to well‐known shortcomings of the RANO criteria. Therefore, we additionally included PFS analysis and long‐term follow‐up (median 9.2 mo) to enable best possible ground truth assessment in the clinical routine setting. Furthermore, the included study participants had different types of diffuse gliomas which were resected to varying extents and treated with varying types and doses of radiation. To overcome such limitations, future studies should aim for even larger patient cohorts (e.g., in multicenter trials) with longitudinal CEST MRI data acquisition. In this context, leading experts in the field have recently published consensus recommendations for APTwasym CEST imaging in neuro‐oncologic applications in order to provide a rationale for optimized APTwasym imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods.33CONCLUSIONSIn patients with glioma at the first follow‐up 4 – 6 wk after the completion of radiotherapy, MTconst, PeakAreaAPT, and APTwasym can predict clinical outcomes by means of PFS, whereas MTconst can distinguish pseudoprogression from disease progression, as assessed according to RANO criteria. These findings indicate that Lorentzian‐fit‐based and asymmetry‐based CEST metrics of the APT and ssMT early after the completion of radiotherapy might have synergistic potential for supporting clinical decision making in patients with glioma.ACKNOWLEDGMENTSThis study was funded by the German Research Foundation (Grant No. 445704496). 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Journal

Magnetic Resonance in Medicine – Wiley

Published: Oct 1, 2023

Keywords: amide proton transfer imaging; chemical exchange saturation transfer; glioma; magnetic resonance imaging; radiotherapy; semisolid magnetization transfer imaging

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