Madhuranthakam, Ananth J.

ABSTRACT Background In chronic liver disease, fat and fibroinflammatory changes often coexist. However, their biomarkers, proton density fat fraction (PDFF) and T 1 , are typically assessed separately. Their reproducibility under mutual confounding remains unclear. Purpose To assess multicenter, multi‐vendor reproducibility of confounder‐corrected chemical shift‐encoded (CSE)‐MRI‐based PDFF mapping and MOLLI‐based T 1 mapping using a combined PDFF‐T 1 phantom. Study Type Prospective phantom study. Phantom Commercial PDFF‐T 1 Phantom (Model 725) with varying PDFF (0%–30%) and T 1 (200–1400 ms) values. Field Strength/Sequence 1.5 T and 3 T multi‐echo, three‐dimensional spoiled‐gradient‐echo (SGRE) sequence for PDFF mapping, and MOLLI sequence (5(3)3 acquisition scheme) using two‐dimensional SGRE readouts for T 1 mapping across four centers and vendors. Assessment PDFF and T 1 maps were acquired using standardized protocols. PDFF maps were reconstructed locally, while T 1 maps were generated using a centralized algorithm. All maps were quantitatively analyzed by a single radiologist using standardized region‐of‐interest placement. Phantom temporal stability was assessed at one center across five sessions over 9 months (baseline, retest, 1 week, 6 and 9 months). Statistical Tests Intraclass correlation coefficients (ICC), reproducibility coefficients (RDC), and linear regression analysis were used. A p value < 0.05 was considered statistically significant. Results PDFF showed overall excellent reproducibility (ICC = 0.987, RDC = 3.7%), with increased variability at higher T 1 values (RDC up to 7.9% at T 1 = 1400 ms). T 1 mapping showed good reproducibility in the absence of fat (RDC 16–161 ms at PDFF = 0%), but moderate to poor reproducibility in the presence of fat, with RDC increasing up to 1553 ms at PDFF 30%. Temporal stability was excellent ICC ≥ 0.998 for both PDFF and T 1 , and RDC of 1.1%–1.3% for PDFF and 52–57 ms for T 1 . Data Conclusion This phantom study demonstrated high reproducibility of PDFF, whereas T 1 reproducibility deteriorated at higher fat and T 1 levels, underscoring the need for fat‐corrected T 1 mapping for reliable assessment of fibroinflammatory changes. Evidence Level N/A. Technical Efficacy Stage 1.

Abstract Renal tumors are highly heterogeneous, ranging from benign lesions and indolent renal cell carcinoma (RCC) to highly aggressive phenotypes. Accurately determining tumor aggressiveness before treatment remains a major clinical challenge. Conventional MR imaging lacks sensitivity to microarchitectural differences underlying tumor biology, and biopsy is limited by sampling error and tumor heterogeneity. Radiomics offers a noninvasive approach to extract high dimensional features that may capture biologically relevant patterns. In this IRB-approved retrospective study, we evaluated MR-based radiomics combined with machine learning (ML) to differentiate indolent from aggressive tumors. Pre-operative clinically acquired MR images obtained from eighty-two RCC patients (aggressive, n = 56; indolent, n = 26) who underwent nephrectomy were included for the analysis. Regions of interest (ROIs) of the entire tumor were manually drawn using 3D Slicer on the morphological T2-weighted images and four phases (pre-contrast [PRE], corticomedullary [CM], nephrographic [NG], and delayed [DEL]) from the contrast-enhanced MR images, normalized, and used for radiomic feature extraction (PyRadiomics, v3.1.0). Independently, tumors were grouped into aggressive and indolent phenotypes based on the established histological classification of Mayo Clinic. Statistical analysis used two-sided t-test. Features selected based on mutual information were used for ML classification (Random Under-Sampling Boosting (RUSBoost)) and performance assessed using accuracy, sensitivity, precision, specificity, F1-score, and area under the curve (AUC). Of 107 radiomics features extracted, the following numbers showed statistically significant differences between aggressive and indolent tumors (p &lt; 0.05): CM phase (n = 19), NG phase (n = 24), DEL phase (n = 22), and T2-weighted images (n = 29). Several features consistently differed among contrast-enhanced images and included first-order (length, surface area, volume), GLCM (correlation, IDM, IMC1, IMC2), GLDM (dependence entropy, dependence non-uniformity, dependence variance), GLRLM (gray-level non-uniformity, run entropy, run-length non-uniformity), GLSZM (high gray-level emphasis, zone entropy), and NGTDM (coarseness). Using the 15 features with highest mutual information across 50 patients (aggressive, n = 31; indolent, n = 19), a five-fold cross-validation RUSBoost classifier achieved accuracy 0.81, sensitivity 0.84, precision 0.87, specificity 0.77, F1-score 0.85, and AUC 0.80 for identifying the aggressive phenotype. MRI radiomics features show potential to differentiate aggressive from indolent renal tumors. ML-based classification using these features demonstrates promising accuracy. Further optimization and validation in larger cohorts, may help guide individualized care, including consideration of active surveillance for indolent renal tumors. Citation Format: Huan Lu, Salim Rukhsar, Xueqing Yin, Garima Suman, Ashish Khandelwal, Ananth J. Madhuranthakam, Durga Udayakumar. MRI radiomic features to differentiate aggressive and indolent renal tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2142.

This cohort study evaluates the use of arterial spin labeling magnetic resonance imaging (ASL MRI) to quantify placental perfusion in pregnant individuals with a fetus with congenital heart disease.

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.