Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation

Abstract Purpose This study aims to develop an effective machine learning (ML)-based predictive model for the recurrence of borderline ovarian tumor (BOT), and provide the guidelines of accurate clinical diagnosis and precise treatment for patients. Method A total of 660 patients diagnosed with BOT...

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Auteurs principaux:	Liheng Yan, Qiulin Ye, Baole Shi, Juanjuan Liu, Yuexin Hu, Ouxuan Liu, Xiao Li, Bei Lin, Yue Qi
Format:	Artigo
Langue:	Inglês
Publié:	Springer 2025-05-01
Collection:	Journal of Cancer Research and Clinical Oncology
Sujets:	Borderline ovarian tumor Recurrence prediction Machine learning Random forest Predictive modeling Fertility-sparing surgery
Accès en ligne:	https://doi.org/10.1007/s00432-025-06221-x
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