{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:55:52Z","timestamp":1790751352785,"version":"4.1.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a non-invasive prediction model combining renal CT radiomics features and clinical markers for assessing long-term renal prognosis at 1, 3, and 5 years in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective cohort of 262 AAV patients from Peking Union Medical College Hospital (2015\u20132020) was analyzed. Radiomics features (morphological, first-order, and texture) were extracted from segmented renal parenchyma on abdominopelvic CT. Clinical markers included demographics, serum creatinine, complement component 3 (C3), and other laboratory indicators. Renal volume, serum creatinine, and complement component 3 (C3) were pre-specified as predictors based on\n                      <jats:italic>a priori<\/jats:italic>\n                      biological rationale, and XGBoost was employed for model construction. Performance was evaluated via ROC-AUC (receiver operating characteristic\/area under the curve), F1 score, decision curve analysis, and calibration curves. SHAP (SHapley Additive exPlanations) analysis assessed feature contributions, and Kaplan\u2013Meier curves with Bonferroni-corrected log-rank tests were used for survival stratification.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The mean follow-up was 40.8 months; 59 patients (23%) developed ESRD (56 within 3 years) and 29 (11%) died. The combined model (renal volume\u2009+\u2009sCr\u2009+\u2009C3) achieved an AUC of 0.93 for 3-year ESRD prediction (clinical-only model: 0.91), with renal volume independently associated with ESRD (Wald\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.004). For the 3-year composite outcome, the combined model achieved an AUC of 0.84 (clinical-only: 0.81) with significantly improved reclassification (IDI\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.018; category-free NRI\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.024). In pre-specified median-based Kaplan\u2013Meier analyses, reduced renal volume (\u2264 130.6\u2005mL), elevated serum creatinine (&amp;gt; 177.5 \u03bcmol\/L) and lower C3 (\u2264 0.97\u2005g\/L) were associated with poorer 3-year ESRD-free survival (log-rank\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2264\u20090.002).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integrated radiomics-clinical model provides accurate, non-invasive risk stratification for long-term renal outcomes in AAV, facilitating personalized treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1915391","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:03:34Z","timestamp":1790748214000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomics-Based prediction of renal prognosis in ANCA-associated vasculitis"],"prefix":"10.3389","volume":"8","author":[{"given":"Zuoxian","family":"Hou","sequence":"first","affiliation":[{"name":"Department of Nephrology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","place":["Beijing, China"]},{"name":"Beijing Key Laboratory of Molecular Pathology Diagnosis and Translational Research on Rare and Complex Urinary System Diseases, Peking Union Medical College Hospital","place":["Beijing, China"]},{"name":"Center for Digital Medicine and Artificial Intelligence, National Infrastructures for Translational Medicine, Peking Union Medical College Hospital, Peking Union 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