兰州大学学报（医学版）2026，Vol.52Issue(2)：8-15,8.DOI:10.13885/j.issn.2097-681X.M20252151
卵巢癌转移灶的智能识别与结构化报告填充:一项多中心研究
Intelligent identification of ovarian cancer metastases and a structured population report:a multicenter study
摘要
Abstract
Objective To explore application models of artificial intelligence for precise localization and evaluation of ovarian cancer metastases.Methods A total of 273 contrast-enhanced abdominal-pelvic com-puted tomography(CT)scans from patients with ovarian cancer metastases across three centers were includ-ed.Radiologists annotated 174 subdiaphragmatic metastases and 516 perihepatic metastases,who were ran-domly divided into training(n=561)and test(n=129)sets.A deep convolutional network-based binary classification model for distinguishing subdiaphragmatic/perihepatic locations was constructed,and its accura-cy,sensitivity,specificity,precision,F1-score,and area under the curve(AUC)were calculated.Using four-phase contrast-enhanced CT images and surgical pathology data,the structured reports were filled in and their performanc eevaluated.Results The subphrenic/perihepatic location differentiation model achieved an AUC of 0.78,with an accuracy of 0.721,sensitivity of 0.417,specificity of 0.839,precision of 0.500,and an F1-score of 0.455.In the structured report population,the classification model for perihepatic metastasis location performed best,attaining an AUC of 0.83,accuracy of 0.753,sensitivity of 0.804,specificity of 0.702,preci-sion of 0.725,and an F1-score of 0.763.The recognition capabilities of models for other features require fur-ther improvement.Conclusion This work establishes a novel clinical assistance workflow—"automated image analysis,key feature extraction,and structured report population"—offering a practical framework for optimiz-ing diagnostic processes and enhancing reporting standardization.关键词
卵巢癌/腹膜转移/计算机体层成像/人工智能/结构化报告Key words
ovarian cancer/peritoneal metastasis/computed tomography/artificial intelligence/structured re-porting分类
医药卫生引用本文复制引用
赵佳,任静,黄梦琳,丛福泽,王芳,吴哲,何泳蓝,薛华丹..卵巢癌转移灶的智能识别与结构化报告填充:一项多中心研究[J].兰州大学学报（医学版）,2026,52(2):8-15,8.基金项目
中国医学科学院医学与健康科技创新工程项目(2024-I2M-C&T-B-032) （2024-I2M-C&T-B-032）
中央高水平医院临床科研业务费(2025-PUMCH-A-023) （2025-PUMCH-A-023）
