卵巢癌
医学
回顾性队列研究
队列
癌症
肿瘤科
内科学
医学物理学
人工智能
妇科
计算机科学
作者
Guangyao Cai,Fangjun Huang,Yue Gao,Xiao Li,Jianhua Chi,Jincheng Xie,Linghong Zhou,Yanling Feng,He Huang,Ting Deng,Yun Zhou,Chuyao Zhang,Xiaolin Luo,Xing Xie,Qinglei Gao,Xin Zhen,Jihong Liu
标识
DOI:10.1016/s2589-7500(23)00245-5
摘要
Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer.
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