Non‐Invasive Tumor Grade Evaluation in Von Hippel– Lindau‐Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging‐Based Study

作者：Aryan Zahergivar, Pouria Yazdian Anari, Neil Mendhiratta, Nathan Lay, Shiva M. Singh, Fatemeh Dehghani Firouzabadi, Aditi Chaurasia, Mahshid Golagha, Fatemeh Homayounieh, Rabindra Gautam, Stephanie A. Harmon, Evrim Türkbey, Maria J. Merino, Elizabeth C. Jones, Mark W. Ball, Barış Türkbey, W. Marston Linehan, Ashkan A. Malayeri · 发表于：Journal of Magnetic Resonance Imaging · 年份：2024 · DOI：10.1002/jmri.29222 · 被引用次数：6 · 研究领域：Renal cell carcinoma treatment、Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis

BACKGROUND: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. STUDY TYPE: Retrospective analysis of a prospectively maintained cohort. POPULATION: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. FIELD STRENGTH AND SEQUENCES: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. ASSESSMENT: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. STATISTICAL TESTS: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of re...