Lu, Huan

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.

Abstract Aging increases the vulnerability of kidneys to injury and impairs their regenerative capacity. SIRT3 expression declines with aging and is associated with multiple age‐related pathologies. The expression profile and functional role of SIRT3 in renal aging remain unclear. Here, SIRT3 expression in aging kidneys is assessed and analyzed for its promoter methylation patterns using methylation‐specific PCR (MSP). It is found that aging exacerbates UUO‐induced renal fibrosis, associated with downregulated SIRT3 expression. Mechanistically, age‐related SIRT3 downregulation is mediated by hypermethylation of its promoter region. SIRT3 knockout exacerbated renal fibrosis in young mice subjected to UUO, whereas SIRT3 overexpression attenuated fibrosis in aged UUO mice. Integration of RNA‐seq and immunoprecipitation‐mass spectrometry (IP‐MS) analyses revealed that SIRT3 deficiency leads to hyperacetylation of GSK3β at lysine 15 (K15). This K15 hyperacetylation inhibited GSK3β activity, consequently stabilizing its substrate β‐catenin. Furthermore, self‐assembled PEG‐PCL‐PEG micelles are designed and synthesized to encapsulate hydrophobic honokiol (HKL). These micelles significantly enhanced the aqueous solubility and oral bioavailability of free HKL, maintained stable blood concentrations, and ultimately improved its anti‐fibrotic efficacy. These findings propose novel therapeutic strategies for managing renal fibrosis in the aging population and provide a foundation for developing new drugs and combination therapies.