Udayakumar, Durga

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 < 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.

Introduction: Placental dysfunction is an emerging contributor to neonatal morbidity and mortality in pregnancies complicated by fetal congenital heart disease (CHD). Previous work in mouse models of CHD and examination of human placental pathology indicates that the maternal-fetal interface, specifically the syncytiotrophoblast layer, can be dysmorphic in fetal CHD-affected pregnancies. We hypothesized that gene expression in a subset of placentas affected by fetal CHD would show differences in genes required for syncytiotrophoblast differentiation and function. Methods: Placentas from pregnancies affected by fetal CHD were sampled and stored as formalin-fixed paraffin embedded blocks per pathology core standard protocol. A subset of fetal CHD cases affected by left ventricular outflow tract obstruction (LVOTO) without genetic diagnoses or major extracardiac anomalies were selected for transcriptomic analysis. Results: Placentas affected by fetal LVOTO (n=20) demonstrated markedly reduced expression of genes associated with terminally differentiated syncytiotrophoblasts compared to controls (n=10). Specifically, aromatase (p<0.0001), placental growth hormone (p<0.0001), and multiple chorionic gonadotropin subunits and pregnancy specific glycoproteins were downregulated. Transcription factor GCM1, a master regulator of trophoblast differentiation, was also downregulated (p<0.0001). Syncytin-1, a product of syncytiotrophoblasts that facilitates their formation from cytotrophoblasts, was downregulated (p<0.0001) and syncytin-1 receptor expressed on cytotrophoblasts was upregulated (p<0.0001) in the LVOTO affected placentas. Abundance of cytotrophoblasts did not differ between groups, which showed similar expression of cytotrophoblast markers including E-cadherin and cytokeratins. Conclusions: Dysregulation of the heart-placenta axis in fetal CHD may be a result of altered syncytiotrophoblast differentiation, resulting in decreased functional surface area at the maternal-fetal interface.

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 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.