分子影像学杂志2025，Vol.48Issue(8)：946-951,6.DOI:10.12122/j.issn.1674-4500.2025.08.04
肝癌患者术前Ki-67指数预测:基于小样本的磁共振T2WI图像的影像组学特征结合多种机器学习模型
Prediction of preoperative Ki-67 index in hepatocellular carcinoma patients:a small-sample-based integration of T2WI radiomic features with multiple machine learning algorithms
摘要
Abstract
Objective To compare the differences in Ki-67 expression levels among newly diagnosed hepatocellular carcinoma(HCC)patients using machine learning methods and to investigate their role in preoperative prediction of immunohistochemical characteristics.Methods This dual-center study collected MRI data and immunohistochemical Ki-67 expression profiles from 59 newly diagnosed HCC patients at the 910th Hospital of Joint Logistics Support Force and Quanzhou First Hospital from November 2023 to October 2024.The cohort included 52 males and 7 females[age 32-55(44.71±6.639)years],stratified into high-expression(Ki-67≥20%,n=37)and low-expression(Ki-67<20%,n=22)groups based on a 20%threshold.All patients underwent preoperative contrast-enhanced MRI(3.0T scanners:Siemens Skyra and GE Discovery 750)with T2WI.Tumor regions of interest were manually delineated using 3D Slicer,and 1,198 radiomic features(shape,first-order statistics,texture,and wavelet transforms)were extracted via the OnekeyAI platform.Synthetic minority oversampling technique addressed class imbalance,followed by rigorous preprocessing(missing value imputation,outlier detection,and data standardization).Eight machine learning models,including logistic regression,support vector machine,K-nearest neighbors,Random forest,extremely randomized trees(ERT),XGBoost,LightGBM,and multilayer perceptron,were implemented for Ki-67 classification.Results Random forest,ERT and XGBoost demonstrated superior performance during training,with XGBoost achieving the highest AUC(0.914),followed by Random forest(0.911)and ERT(0.833).In testing,these models maintained robust generalization capabilities,yielding AUCs of 0.741(Random forest),0.750(ERT),and 0.777(XGBoost),respectively.Conclusion This study demonstrates that a small-sample-based integration of T2WI radiomic features with machine learning algorithms enables effective preoperative prediction of Ki-67 proliferation index in HCC patients.关键词
Ki-67/肝癌/机器学习/ROC曲线/双中心研究Key words
Ki-67/liver cancer/machine learning/ROC curve/multi-center study引用本文复制引用
黄莹,张乾营,刘旭红,韩晓兵,林涛,何桂凤,黄艺峰,丁碧娇,张永辉,王新达..肝癌患者术前Ki-67指数预测:基于小样本的磁共振T2WI图像的影像组学特征结合多种机器学习模型[J].分子影像学杂志,2025,48(8):946-951,6.基金项目
福建省科技计划项目(2024Y9455) （2024Y9455）
泉州市科技计划项目(2024NY057) （2024NY057）
第910医院院级课题项目(910YK202307) （910YK202307）
