广东医学2025，Vol.46Issue(6)：839-844,6.DOI:10.13820/j.cnki.gdyx.20250917
基于影像组学特征预测肾上腺皮质癌Ki-67的人工智能模型的研究
Artificial intelligence model based on radiomic features from preoperative CT for predicting Ki-67 expression in adrenocortical carcinoma
摘要
Abstract
Objective To establish an artificial intelligence(AI)model based on radiomic features extracted from preoperative non-contrast CT images to predict Ki-67 expression levels in adrenocortical carcinoma(ACC).Meth-ods A total of 93 patients diagnosed pathologically with primary ACC and who underwent adrenalectomy were retrospec-tively included from the First Affiliated Hospital of Sun Yat-sen University,Sun Yat-sen University Cancer Center,the First Affiliated Hospital of the University of Science and Technology of China,and the TCIA database between July 2010 and September 2023.Patients were divided into internal training and external validation cohorts.A Ki-67 index＞10％was defined as high Ki-67.Radiomic features were extracted and selected,and five AI algorithms were applied to build predictive models.Model performance was evaluated using the area under the receiver operating characteristic curve(AUC).Results There were no significant differences in clinical baseline characteristics such as age and tumor size a-mong patients.After feature selection,six radiomic features were retained for model development.In the internal training cohort,the gradient boosting algorithm achieved the highest AUC(0.94),with an accuracy of 0.87,sensitivity of 0.89,and specificity of 0.84.In the external validation cohort,the random forest algorithm showed the best performance,with an AUC of 0.86,accuracy of 0.87,and sensitivity of 0.88.Conclusion Radiomic features from preoperative non-con-trast CT can effectively predict Ki-67 expression in adrenocortical carcinoma,offering a potential non-invasive biomark-er for clinical decision-making.关键词
肾上腺皮质癌/影像组学/Ki-67/人工智能/多中心回顾性研究Key words
adrenocortical carcinoma/radiomics/Ki-67/artificial intelligence/multicenter retrospective study分类
医药卫生引用本文复制引用
巢堙尧,邓一术,陈贤达,马楠,朱洪章,余锴霖,柯宗潘,肖峻,郭胜杰..基于影像组学特征预测肾上腺皮质癌Ki-67的人工智能模型的研究[J].广东医学,2025,46(6):839-844,6.基金项目
广东省医学科研基金项目(202405161235209275) （202405161235209275）
广东省基础与应用基础研究基金资助项目(24202104030000320) （24202104030000320）
