文献类型：期刊文献

中文题名：基于术前CT影像组学模型预测透明细胞肾细胞癌患者的Ki-67表达

英文题名：Preoperative CT radiomics-based model for predicting Ki-67 expression in clear cell renal cell carcinoma patients

作者：杨志军[1,2];何涵[1];张云峰[3];王佳[3];张文博[3];周逢海[1,2]

第一作者：杨志军

机构：[1]兰州大学第一临床医学院,兰州730000;[2]甘肃省人民医院泌尿外科,兰州730000;[3]甘肃中医药大学第一临床医学院,兰州730000

第一机构：兰州大学第一临床医学院,兰州730000

年份：2024

卷号：49

期号：11

起止页码：1722

中文期刊名：中南大学学报(医学版)

外文期刊名：Journal of Central South University :Medical Science

收录：;北大核心:【北大核心2023】；CSCD:【CSCD2023_2024】；

基金：甘肃省自然科学基金(22JR5RA650);甘肃省人民医院院内科研基金(23GSSYD-12)。

语种：中文

中文关键词：透明细胞肾细胞癌;Ki-67;计算机断层扫描;影像组学;预后

外文关键词：clear cell renal cell carcinoma;Ki-67;computed tomography;radiomics;prognosis

摘要：目的:透明细胞肾细胞癌(clear cell renal cell carcinoma,ccRCC)是肾细胞癌(renal cell carcinoma,RCC)最常见的亚型,制订个体化的治疗方案对于改善患者预后有重要意义。本研究开发并验证基于术前计算机断层扫描(computer tomography,CT)影像组学的预测ccRCC患者Ki-67表达的模型,以指导其临床治疗和预后预测。方法:回顾性分析2018年1月至2023年11月在甘肃省人民医院接受手术治疗的214例经术后病理确诊为ccRCC的患者。根据术后免疫组织化学染色结果将患者分为Ki-67高表达组(n=123)和Ki-67低表达组(n=91),并以7?3的比例随机分为训练集(n=149)和验证集(n=65)。收集患者术前泌尿系统增强CT图像和临床资料,首先挑选出5 mm动脉期CT图像经过前期处理后,使用ITK-SNAP 3.8软件全手动逐层勾画感兴趣区(region of interest,ROI);然后利用FeAture Explorer(FAE)包提取原始影像组学特征,通过最小绝对收缩与选择算子(least absolute shrinkage and selection operator,LASSO)算法对提取到的特征进行降维及筛选;最终筛选出最优特征组合。基于这些特征分别利用逻辑回归(logistic regression,LR)、多层感知器(multilayer perceptron,MLP)和支持向量机(support vector machine,SVM)分类器构建预测模型;绘制受试者操作特征(receiver operating characteristic,ROC)曲线并计算曲线下面积(area under the curve,AUC),同时采用决策曲线分析(decision curve analysis,DCA)、校准曲线进行模型评价。结果:利用FAE工具包从5 mm动脉期CT图像提取到107种原始影像组学特征,使用LASSO算法最终筛选出21个与患者Ki-67表达密切相关的影像组学特征,分别基于LR、MLP、SVM分类器构建模型,3种模型在训练集和验证集中的AUC分别为LR模型0.904(95%CI 0.852~0.956)、0.818(95%CI 0.710~0.926),MLP模型0.859(95%CI 0.794~0.923)、0.823(95%CI 0.716~0.929),SVM模型0.917(95%CI 0.865~0.969)、0.857(95%CI 0.760~0.953)。DCA表明模型具有较好的临床净收益。校准曲线表明预测模型具有良好的精确性。结论:本研究建立了基于CT影像组学的ccRCC患者Ki-67表达预测模型,有助于指导临床医师制订ccRCC患者的治疗方案,并预测患者的预后。
Objective:Clear cell renal cell carcinoma(ccRCC)is the most common subtype of renal cell carcinoma(RCC),and developing personalized treatment strategies is crucial for improving patient prognosis.This study aims to develop and validate a preoperative computer tomography(CT)radiomics-based predictive model to estimate Ki-67 expression in ccRCC patients,thereby assisting in clinical treatment decisions and prognosis prediction.Methods:A retrospective analysis was conducted on 214 ccRCC patients who underwent surgical treatment at Gansu Provincial Hospital between January 2018 and November 2023.Patients were classified into high Ki-67 expression(n=123)and low Ki-67 expression(n=91)groups based on postoperative immunohistochemical staining results.The dataset was randomly divided in a 7?3 ratio into a training set(n=149)and a validation set(n=65).Preoperative contrast-enhanced urinary CT images and clinical data were collected.After preprocessing,5 mm arterial-phase CT images were manually segmented layer by layer to delineate the region of interest(ROI)using ITK-SNAP 3.8 software.Radiomic features were then extracted using the FeAture Explorer(FAE)package.Dimensionality reduction and feature selection were performed using the least absolute shrinkage and selection operator(LASSO)algorithm,yielding the optimal feature set.Three classification models were constructed using logistic regression(LR),multilayer perceptron(MLP),and support vector machine(SVM).The receiver operating characteristic(ROC)curve,area under the curve(AUC),decision curve analysis(DCA),and calibration curves were used for model evaluation.Results:A total of 107 radiomic features were extracted from 5 mm arterial-phase CT images,and twenty-one features significantly associated with Ki-67 expression were selected using the LASSO algorithm.Predictive models were developed using LR,MLP,and SVM classifiers.In the training and validation sets,the AUC values for each model were 0.904(95%CI 0.852 to 0.956)and 0.818(95%CI 0.710 to 0.926)for the LR model,0.859(95%CI 0.794 to 0.923)and 0.823(95%CI 0.716 to 0.929)for the MLP model,and 0.917(95%CI 0.865 to 0.969)and 0.857(95%CI 0.760 to 0.953)for the SVM model.DCA demonstrated that all models had good clinical net benefit,while calibration curves indicated high accuracy of the predictions,supporting the robustness and reliability of the models.Conclusion:A CT radiomics-based model for predicting Ki-67 expression in ccRCC was successfully developed.This model provides valuable guidance for treatment planning and prognostic assessment in ccRCC patients.