{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T19:52:25Z","timestamp":1788551545891,"version":"build-2803163510"},"reference-count":19,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel\u2013Lindau (VHL)\u2010associated ccRCC, aiming to improve non\u2010invasive diagnosis and personalized treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective analysis of a prospectively maintained cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>One hundred and thirty\u2010six patients, 84 (61%) males and 52 (39%) females with pathology\u2010proven ccRCC with a mean age of 52.8\u2009\u00b1\u200912.7 from 2010 to 2023.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength and Sequences<\/jats:title><jats:p>1.5 and 3\u2009T MRIs. Segmentations were performed on the <jats:styled-content style=\"fixed-case\">T1<\/jats:styled-content>\u2010weighted 3\u2010minute delayed sequence and then registered on pre\u2010contrast, <jats:styled-content style=\"fixed-case\">T1<\/jats:styled-content>\u2010weighted arterial and venous sequences.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>A total of 404 lesions, 345 low\u2010grade tumors, and 59 high\u2010grade tumors were segmented using ITK\u2010SNAP on a T1\u2010weighted 3\u2010minute delayed sequence of MRI. Radiomics features were extracted from pre\u2010contrast, T1\u2010weighted arterial, venous, and delayed post\u2010contrast 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The best model displayed an accuracy of 0.79. For low\u2010grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high\u2010grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>Radiomics analysis shows promise in classifying pathology grades non\u2010invasively for patients with VHL\u2010associated ccRCC, potentially leading to better diagnosis and personalized treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>1<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29222","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T11:24:27Z","timestamp":1706786667000},"page":"1076-1081","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["<scp>Non\u2010Invasive<\/scp> Tumor Grade Evaluation in Von Hippel\u2013<scp>Lindau\u2010Associated<\/scp> Clear Cell Renal Cell Carcinoma: A Magnetic Resonance <scp>Imaging\u2010Based<\/scp> Study"],"prefix":"10.1002","volume":"60","author":[{"given":"Aryan","family":"Zahergivar","sequence":"first","affiliation":[{"name":"Radiology and Imaging Sciences Clinical Center, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pouria","family":"Yazdian Anari","sequence":"additional","affiliation":[{"name":"Radiology and Imaging Sciences Clinical Center, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil","family":"Mendhiratta","sequence":"additional","affiliation":[{"name":"Urology Oncology Branch National Cancer Institutes, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Lay","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Resource National Cancer Institute, National 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