摘要
目的 探讨基于动态对比增强磁共振成像(dynamic contrast enhancement magnetic resonance imaging,DCE-MRI)药代动力学参数直方图特征预测前列腺癌(prostate cancer,PCa)内分泌治疗反应的价值。材料与方法 回顾性分析2018年1月至2023年10月河西学院附属张掖人民医院(中心1)和2020年2月至2023年2月甘肃省人民医院(中心2)PCa患者在内分泌治疗前2周的临床、影像资料,将中心1收集的105例病例按7∶3的比例分为训练集(73例)和内部验证集(32例),将中心2收集的47例病例作为外部验证集。选取DCE-MRI原始图像,通过Siemens Syngo.via工作站获得药代动力学参数容积转运常数(volume transfer contrast,Ktrans)、速率常数(rate contrast,Kep)、血管外细胞外容积分数(extravascular extracellular volume fraction,Ve)伪彩图。在3D Slicer软件中参照轴位T2WI在药代动力学参数伪彩图上逐层勾画全前列腺腺体感兴趣区(region of interest,ROI)后提取直方图特征,经最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)降维筛选出8个最优特征并计算直方图特征。采用单因素及后向多因素logistic回归分析内分泌治疗反应良好组和不良组的独立预测因素,并构建临床模型、直方图特征模型、联合模型。采用受试者工作特性曲线、校准曲线和决策曲线评价模型的效能,通过DeLong检验评估各模型曲线下面积(area under the curve,AUC),最后基于联合模型的独立预测因素绘制列线图。结果 训练集、内部验证集和外部验证集中治疗反应良好组和不良组之间Gleason评分、MRI-T分期、直方图特征差异均存在统计学意义(P<0.001)。后向多因素logistic回归分析显示Gleason评分(OR=0.925,95%CI:0.859~0.958,P=0.038)、MRI-T分期(OR=0.871,95%CI:0.800~0.949,P=0.002)及直方图特征(OR=0.096,95%CI:0.056~0.137,P<0.001)是PCa内分泌治疗反应的独立预测因素;临床模型在训练集、内部验证集及外部验证集的AUC分别为0.857(95%CI:0.774~0.939)、0.953(95%CI:0.888~0.996)、0.808(95%CI:0.676~0.941);直方图特征模型在训练集、内部验证集及外部验证集的AUC为0.874(95%CI:0.769~0.951)、0.816(95%CI:0.664~0.967)、0.674(95%CI:0.517~0.831);联合模型在训练集、内部验证集及外部验证集的AUC为0.951(95%CI:0.906~0.994)、0.973(95%CI:0.922~0.995)、0.830(95%CI:0.699~0.960);决策曲线和校准曲线分析表明,联合模型具有良好的临床应用价值和稳定性;DeLong检验及NRI值显示联合模型的预测效能优于临床模型和直方图特征模型。结论 DCE-MRI药代动力学参数直方图特征是预测PCa内分泌治疗反应的独立预测因素,联合模型在预测PCa内分泌治疗反应方面具有较好的价值,为临床治疗决策提供了新的思路。
Objective:To explore the value of predicting the response of prostate cancer(PCa) to endocrine therapy based on the histogram features of pharmacokinetic parameters of dynamic contrast enhancement magnetic resonance imaging(DCE-MRI).Materials and Methods:Retrospectively collect the clinical and imaging data of PCa patients from Zhangye People's Hospital Affiliated to Hexi University from January 2018 to October 2023 and Gansu Provincial People's Hospital from February 2020 to February 2023,two weeks before endocrine therapy.A total of 105 cases were collected from Zhangye People's Hospital Affiliated to Hexi University,which were divided into a training set(73 cases) and an internal validation set(32 cases) at a ratio of 7∶ 3.A total of 47 cases were collected from Gansu Provincial People's Hospital as an external validation set.Select the original DCE-MRI images,and obtain the pseudo-color maps of pharmacokinetic parameters including volume transfer constant(K~(trans)),rate constant(K_(ep)),and extravascular extracellular volume fraction(Ve) through the Siemens Syngovia workstation.In the 3D Slicer software,referring to the axial T2-weighted imaging(T2WI),delineate the region of interest(ROI) of the whole prostate gland layer by layer on the pseudo-color maps of pharmacokinetic parameters,and then extract the histogram features.Through dimensionality reduction by the least absolute shrinkage and selection operator(LASSO),8 optimal features were screened out and the histogram features was calculated.Univariate and backward multivariate logistic regression were used to analyze the independent predictive factors of the good-response group and the poor-response group of endocrine therapy,and a clinical model,a histogram features model,and a combined model were constructed.The area under the curve(AUC) was calculated using the receiver operating characteristic(ROC) curve,and the calibration curve and decision curve were used to evaluate the performance of the model.The efficacy of each model was evaluated by the DeLong test.Finally,a nomogram was drawn based on the independent predictive factors of the combined model.Results:In the training set,internal validation set,and external validation set,there were statistically significant differences in Gleason score,MRI-T stage,and histogram features between the good-response group and the poor-response group(P < 0.001).Backward multivariate logistic regression analysis showed that the Gleason score(OR = 0.925,95% CI:0.859 to 0.958,P = 0.038),MRI-T stage(OR = 0.871,95% CI:0.800 to 0.949,P =0.002),and histogram features(OR = 0.096,95% CI:0.056 to 0.137,P < 0.001) were independent predictive factors for the response of PCa to endocrine therapy.The AUCs of the clinical model in the training set,internal validation set,and external validation set were 0.857(95% CI:0.774 to 0.939),0.953(95% CI:0.888 to 0.996),and 0.808(95% CI:0.676 to 0.941),respectively.The AUCs of the histogram features model in the training set,internal validation set,and external validation set were 0.874(95% CI:0.769 to 0.951),0.816(95% CI:0.664 to 0.967),and 0.674(95% CI:0.517 to 0.831),respectively.The AUCs of the combined model in the training set,internal validation set,and external validation set were 0.951(95% CI:0.906 to 0.994),0.973(95% CI:0.922 to 0.995),and 0.830(95%CI:0.699 to 0.960),respectively.The analysis of the decision curve and calibration curve showed that the combined model had good clinical application value and stability.The DeLong test and NRI value showed that the predictive efficacy of the combined model was better than that of the clinical model and the histogram features model.Conclusions:The histogram features of DCE-MRI pharmacokinetic parameters is an independent predictive factor for predicting the response of PCa to endocrine therapy.The combined model has good value in predicting the response of PCa to endocrine therapy,providing new ideas for clinical treatment decisions.
作者
李静
邹彩霞
潘妮妮
陈俊
黄刚
LI Jing;ZOU Caixia;PAN Nini;CHEN Jun;HUANG Gang(The First Clinical Medical College of Gansu University of Traditional Chinese Medicine,Lanzhou 730000,China;Institute of Imaging,Zhangye People's Hospital,Hexi University,Zhangye 734000,China;Department of Radiology,Gansu Provincial People's Hospital,Lanzhou 730000,China;Department of Health Imaging Diagnosis,Bayer Healthcare Co.,Ltd,Wuhan 430000,China)
出处
《磁共振成像》
北大核心
2025年第4期70-80,共11页
Chinese Journal of Magnetic Resonance Imaging
基金
甘肃省卫生健康委卫生健康行业科研项目(编号:GS-62000000001-2024-010)
甘肃省教育厅高校教师创新基金项目(编号:2024A-156)
2024年度张掖市市级科技计划项目(编号:ZY2024BJ08)
河西学院校长基金创新团队项目(编号:CXTD2024014)。
关键词
前列腺癌
磁共振成像
动态对比增强
直方图
内分泌治疗
prostate cancer
magnetic resonance imaging
dynamic contrast enhancement
histogram
endocrine therapy
