{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:54:27Z","timestamp":1790747667252,"version":"4.1.0"},"reference-count":35,"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. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Postoperative lower-extremity deep vein thrombosis (DVT) is a common and potentially fatal complication after radical surgery for gynecologic malignancies, yet population-specific risk tools remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 500 consecutive patients who underwent radical surgery for cervical, endometrial, or ovarian cancer (January 2022-January 2025). The cohort was randomly divided into a training set (n=351, 70%) and a validation set (n=149, 30%) at a 7:3 ratio. Independent predictors were identified using logistic regression, and 10 machine-learning algorithms were trained and compared. Model discrimination, calibration, and clinical utility were evaluated, and the optimal model was interpreted using SHAP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DVT occurred in 116 patients (23.2%). Five variables were independently associated with DVT: age, Caprini score, lymphocyte count, preoperative D-dimer, and neutrophil-to-lymphocyte ratio (NLR).Across candidate models, CatBoost achieved the optimal overall performance on the validation set, yielding an AUC of 0.816 (95% CI: 0.738\u20130.881), with sensitivity of 0.824 (95% CI: 0.700\u20131.000) and specificity of 0.730 (95% CI: 0.515\u20130.876), and SHAP analysis ranked the predictors by mean absolute SHAP value in the following order: lymphocyte count, Caprini score, age, preoperative D-dimer, and neutrophil-to-lymphocyte ratio (NLR).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An interpretable CatBoost-based model enables individualized postoperative DVT risk stratification after gynecologic cancer surgery and may support targeted, evidence-based thromboprophylaxis and perioperative nursing management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1813354","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:54:44Z","timestamp":1790744084000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable machine-learning model for postoperative lower-extremity deep vein thrombosis after gynecologic cancer surgery"],"prefix":"10.3389","volume":"16","author":[{"given":"Yanhong","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Interventional Therapy I, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","place":["Jinan, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congcong","family":"Shi","sequence":"additional","affiliation":[{"name":"Depression Disorder Diagnosis and Treatment Center, Shandong Mental Health 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