Pinho, Marco C.

ABSTRACTSurgical biopsy of brainstem tumors carries a risk of neurological injury. We performed magnetic resonance spectroscopy (MRS) of 2‐hydroxyglutarate (2HG) and glycine in patients with brainstem tumors to assess the feasibility of detecting and quantifying 2HG in the brainstem to obviate the need for a diagnostic biopsy and to establish the clinical significance of glycine MRS in brainstem tumors in vivo. Twenty adult patients with radiographically identified presumed brainstem gliomas were prospectively enrolled in the study. Proton MRS was obtained at 3T with a protocol tailored for detection of 2HG and glycine (TE 97‐ms PRESS). Spectra were fit using LCModel software and in‐house basis signals of metabolites and lipids. The metabolite concentrations were quantified with reference to water and examined with respect to clinical outcomes, including postgadolinium MRI and overall survival time. MRS data from 19 patients were included in subsequent analysis, excluding suboptimal data from one patient. Tumors with elevated 2HG (> 1.9 mM, N = 8) and undetectable 2HG (< 0.3 mM, N = 11) were clearly distinguishable. Tumors with elevated glycine (> 1.5 mM, N = 4) showed rapid progression. Kaplan–Meier survival analyses with metabolite measures demonstrated that tumors with 2HG higher than 1.0 mM were significantly associated with a favorable prognosis (p = 0.01). In contrast, tumors with glycine higher than 2.5 mM showed a strong association with poor survival (p = 0.0005). The data confirm detection of 2HG in brainstem tumors at a concentration that is consistent with an IDH mutation and expected good prognosis, whereas elevated glycine in brainstem tumors portends rapid tumor progression and a worse prognosis.

Abstract Background The isocitrate dehydrogenase (IDH) mutation status is a key molecular marker in diagnosing and treating brain tumors. Currently, it is determined via invasive tissue biopsy. Recent advances in deep learning (DL) have offered promising non-invasive alternatives for determining IDH status. However, their clinical translation is hindered by a significant gap between DL predictions and their clinical applicability. The limited transparency of many DL-networks and inadequate evaluation metrics hinders trust and adoption, as clinicians require clear and validated insights for determining IDH status. These challenges highlight the need for robust validation and measures of predictive reliability to make DL-predictions clinically actionable. Methods We developed a unique approach for non-invasive prediction of IDH status using MRI. We combine a voxel-wise-segmentation network(MC-net) with Bayesian logistic regression (BLR) to provide an IDH status and estimate confidence scores. We utilized a comprehensive dataset of 2,481 glioma cases from eight institutions. Results Our framework(MC-net + BLR) demonstrated robust performance achieving 96.4% and 95.1% classification accuracies on diverse databases, with an AUC of 0.98. The BLR was implemented exclusively on held-out test data, ensuring that the derived confidence scores are independent of the training or validation phases. The derived confidence scores showed a low Brier score of 0.0125, highlighting its superior calibration and uncertainty quantification. Conclusion The developed framework provides an IDH status and a confidence score, offering clinicians an additional layer of assurance in prediction reliability. It bridges the gap between high-performing DL models and their clinical applicability by addressing the challenges in prediction reliability. Our framework is a significant advancement in non-invasive determination of IDH-status and confidence-informed therapeutic decision-making in neuro-oncology.

Abstract Glioblastoma is highly vascularized with increased perfusion and prognosis remains dismal, despite improved diagnosis and aggressive multimodal treatment. The current standard of care is maximal surgical resection when feasible, followed by adjuvant radiation with concurrent chemotherapy, temozolomide. However, treatment resistance and tumor recurrence pose a considerable challenge for stratifying patients for optimal treatment planning. Current radiological assessment of glioblastoma therapy response predominantly relies on morphological measures; that may not accurately capture early microscopic changes or therapeutic efficacy. We have developed and validated an advanced imaging method, Arterial Spin Labeling (ASL) MRI that enable noninvasive spatial assessment of the entire tumor. ASL-MRI uses water within the blood as an endogenous tracer and measures perfusion without the need for exogenous gadolinium-based contrast agent. The objective of this study was to investigate ASL measured quantitative perfusion after surgery to predict survival in glioblastoma patients undergoing the standard of care chemoradiation treatment. In this prospective, IRB-approved study, twenty-three adults with glioblastoma participated for longitudinal MRI. Progression-free survival (PFS) and overall survival (OS) were recorded for each patient. A baseline MRI scan was performed prior to chemoradiation treatment and after surgery utilizing a 3D ASL sequence. All structural images were co-registered to T1 post-contrast images. Tumor regions of interest (ROIs) such as enhancing tumor (ET), tumor core, whole tumor, and necrosis were manually segmented. Cerebral blood flow maps were calculated and mean perfusion values (MPV) within ET ROIs were extracted. Scatter plot of MPV and Kaplan-Meier graphs were generated using GraphPad Prism. Based on median survival of 420 days, patients were categorized into short (≤420 days) or long (&gt;420 days) survival. A threshold ASL measured perfusion value of 30 mL/100g/min distinguished survival groups; hyper-perfusion correlated with shorter OS, while hypo-perfusion linked to longer OS. The mean±SD of PFS and OS for patients with shorter survival were 119±42 days and 230±117 days respectively, while for longer survival were 624±285 and 726±210 days respectively, and the values were statistically different (P&lt;0.001for both). Kaplan-Meier curves showed significant OS and PFS differences (p&lt;0.015, Hazard Ratio=3.056). Our data indicate that ASL measured baseline perfusion, after surgery and before chemoradiation treatment can benefit in predicting survival and treatment stratification in glioblastoma patients. ASL measured perfusion can be performed during radiation treatment and could provide valuable information that can be incorporated into emerging treatment strategies such as adaptive radiation. Citation Format: Durga Udayakumar, Limin Zhou, Marco Pinho, Yiming Wang, Ben Wagner, Michael Youssef, Joseph Maldjian, Ananth Madhuranthakam. Noninvasive magnetic resonance imaging measured quantitative perfusion as predictive and prognostic biomarker in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7163.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

Background: We evaluated imaging features suggestive of neurodegeneration within the brainstem and upper cervical spinal cord (UCSC) in non-progressive multiple sclerosis (MS). Methods: Standardized 3-Tesla three-dimensional brain magnetic resonance imaging (MRI) studies were prospectively acquired. Rates of change in volume, surface texture, curvature were quantified at the pons and medulla-UCSC. Whole and regional brain volumes and T2-weighted lesion volumes were also quantified. Independent regression models were constructed to evaluate differences between those of Black or African ancestry (B/AA) and European ancestry (EA) with non-progressive MS. Results: 209 people with MS (pwMS) having at least two MRI studies, 29% possessing 3–6 timepoints, resulted in 487 scans for analysis. Median follow-up time between MRI timepoints was 1.33 (25th–75th percentile: 0.51–1.98) years. Of 183 non-progressive pwMS, 88 and 95 self-reported being B/AA and EA, respectively. Non-progressive pwMS demonstrated greater rates of decline in pontine volume ( p < 0.0001) in B/AA and in medulla-UCSC volume ( p < 0.0001) for EA pwMS. Longitudinal surface texture and curvature changes suggesting reduced tissue integrity were observed at the ventral medulla-UCSC ( p < 0.001), dorsal pons ( p < 0.0001) and dorsal medulla ( p < 0.0001) but not the ventral pons ( p = 0.92) between groups. Conclusions: Selectively vulnerable regions within the brainstem-UCSC may allow for more personalized approaches to disease surveillance and management.