Zhu Zi-Wei, Wu Jun, Guo Yang, Ren Qiong-Yuan, Li Dong-Ning, Li Ze-Yu, Han Lei
China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110000, Liaoning Province, China.
Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China.
World J Gastrointest Oncol. 2025 May 15;17(5):104172. doi: 10.4251/wjgo.v17.i5.104172.
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumours of the digestive system worldwide. The expression of Ki-67 is crucial for the diagnosis, treatment, and prognostic evaluation of HCC. AIM: To construct a machine learning model for the preoperative evaluation of Ki-67 expression in HCC and to assist in clinical decision-making. METHODS: This study included 164 pathologically confirmed HCC patients. Radiomic features were extracted from the computed tomography images reconstructed by superresolution of the intratumoral and peritumoral regions. Features were selected the intraclass correlation coefficient, tests, Pearson correlation coefficients and least absolute shrinkage and selection operator regression methods, and models were constructed various machine learning methods. The best model was selected, and the radiomics score (Radscore) was calculated. A nomogram incorporating the Radscore and clinical risk factors was constructed. The predictive performance of each model was evaluated receiver operating characteristic (ROC) curves and calibration curves, and decision curve analysis was used to assess the clinical benefits. RESULTS: In total, 164 HCC patients, namely, 104 patients with high Ki-67 expression and 60 with low Ki-67 expression, were included. Compared with the models in which only intratumoral or peritumoral features were used, the fusion model in which intratumoral and peritumoral features were combined demonstrated stronger predictive ability. Moreover, the clinical-radiomics model including the Radscore and clinical features had higher predictive performance than did the fusion model (area under the ROC curve = 0.848 0.780 in the training group, area under the ROC curve = 0.830 0.760 in the validation group). The calibration curve showed good consistency between the predicted probability and the actual probability, and the decision curve further confirmed its clinical benefit. CONCLUSION: A machine learning model based on the radiomic features of the intratumoral and peritumoral regions on superresolution computed tomography in conjunction with clinical factors can accurately evaluate Ki-67 expression. The model provides valuable assistance in selecting treatment strategies for HCC patients and contributes to research on neoadjuvant therapy for liver cancer.
背景：肝细胞癌（HCC）是全球最常见的消化系统恶性肿瘤之一。Ki-67的表达对HCC的诊断、治疗及预后评估至关重要。 目的：构建用于术前评估HCC中Ki-67表达的机器学习模型，以辅助临床决策。 方法：本研究纳入164例经病理确诊的HCC患者。从肿瘤内和肿瘤周围区域超分辨率重建的计算机断层扫描图像中提取放射组学特征。通过组内相关系数、检验、Pearson相关系数和最小绝对收缩和选择算子回归方法选择特征，并采用多种机器学习方法构建模型。选择最佳模型并计算放射组学评分（Radscore）。构建包含Radscore和临床危险因素的列线图。通过受试者操作特征（ROC）曲线和校准曲线评估各模型的预测性能，并采用决策曲线分析评估临床获益。 结果：共纳入164例HCC患者，其中104例Ki-67高表达患者，60例Ki-67低表达患者。与仅使用肿瘤内或肿瘤周围特征的模型相比，结合肿瘤内和肿瘤周围特征的融合模型具有更强的预测能力。此外，包含Radscore和临床特征的临床-放射组学模型比融合模型具有更高的预测性能（训练组ROC曲线下面积=0.848对0.780，验证组ROC曲线下面积=0.830对0.760）。校准曲线显示预测概率与实际概率之间具有良好的一致性，决策曲线进一步证实了其临床获益。 结论：基于超分辨率计算机断层扫描的肿瘤内和肿瘤周围区域放射组学特征结合临床因素的机器学习模型可准确评估Ki-67表达。该模型为HCC患者选择治疗策略提供了有价值的帮助，并有助于肝癌新辅助治疗的研究。
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