Imaging‐Based Prediction of Ki‐67 Expression in Hepatocellular Carcinoma: A Retrospective Study

作者：Chiyu Cai, Liancai Wang, Lianyuan Tao, Hengli Zhu, Yongnian Ren, Deyu Li, Dongxiao Li · 发表于：Cancer Medicine · 年份：2025 · DOI：10.1002/cam4.70562 · 被引用次数：9 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、Pancreatic and Hepatic Oncology Research

AIM: This study aims to develop a non-invasive, preoperative predictive model for Ki-67 expression in HCC patients using enhanced computed tomography (CT) and clinical indicators to improve patient outcomes. METHODS: This retrospective study analyzed 595 post-curative hepatectomy HCC patients. Patients were categorized into high (> 20%) and low (≤ 20%) Ki-67 expression groups based on cellular proliferation levels. Radiomic features were extracted from enhanced CT scans and combined with clinical parameters to develop a predictive model for Ki-67 expression. RESULTS: Key clinical factors impacting Ki-67 expression in HCC included alpha-fetoprotein (AFP), non-smooth tumor margin, ill-defined pseudo-capsule, and peritumoral star node. From 1441 initially extracted radiomic features, 16 key features were selected using Lasso regression. These features were used to develop a radiomics model, which, when combined with clinical data, yielded an integrated predictive model with high accuracy. The combined model achieved an area under the curve (AUC) of 0.854 in the training group and 0.839 in the validation group. A nomogram based on this model was constructed, and its predictive accuracy was validated through calibration curves and decision curve analysis. A risk scorecard model was also constructed as a practical tool for clinicians to assess the risk level of high Ki-67 expression, facilitating personalized treatment planning. Survival analysis demonstrated significant difference...