基于临床、病理、DWI定量参数构建列线图预测宫颈癌程序性死亡受体配体1阳性表达:不同ROI选择的比较  

Nomogram based on clinical,pathological,and DWI quantitative parameters for predicting the programmed death-ligand 1 positive expression in cervical cancer:Comparison of different ROI options

作　　者：刘开惠 杨蔚[2] 田海萍[3] 张治宁[4] 李云霞[5] 何剑莉[6] LIU Kaihui;YANG Wei;TIAN Haiping;ZHANG Zhining;LI Yunxia;HE Jianli(College of Clinical Medicine,Ningxia Medical University,Yinchuan 750004,China;Department of Radiology,General Hospital of Ningxia Medical University,Yinchuan 750004,China;Department of Pathology,General Hospital of Ningxia Medical University,Yinchuan 750004,China;Department of Gynecological Oncology,General Hospital of Ningxia Medical University,Yinchuan 750004,China;Department of Medical Oncology,General Hospital of Ningxia Medical University,Yinchuan 750004,China;Department of Radiotherapy,General Hospital of Ningxia Medical University,Yinchuan 750004,China)

机构地区：[1]宁夏医科大学临床医学院,银川750004 [2]宁夏医科大学总医院放射科,银川750004 [3]宁夏医科大学总医院病理科,银川750004 [4]宁夏医科大学总医院妇瘤科,银川750004 [5]宁夏医科大学总医院肿瘤内科,银川750004 [6]宁夏医科大学总医院放疗科,银川750004

出　　处：《磁共振成像》2023年第10期98-104,115,共8页Chinese Journal of Magnetic Resonance Imaging

基　　金：国家自然科学基金项目(编号:81860302)。

摘　　要：目的探究基于临床、病理及MR扩散加权成像(diffusion weighted imaging,DWI)定量参数构建列线图预测宫颈癌程序性死亡受体配体1(programmed death-ligand 1,PD-L1)阳性表达的价值。材料与方法回顾性收集2018年1月至2020年7月来我院就诊的初诊宫颈癌患者为训练组(683例),行盆腔MRI扫描,并对病理标本行PD-L1免疫组织化学染色。参考T2WI及增强扫描图像,分别在DWI图像上选择包含肿瘤实体成分的所有连续层面,沿肿瘤边缘手动勾画感兴趣区(region of interest,ROI),从对应的表观扩散系数(apparent diffusion coefficient,ADC)伪彩图上获得各个层面的ADC值,将所有层面的ADC值平均,记作肿瘤平均ADC(mean ADC,ADC_(mean));在DWI图像上选择包含肿瘤实体成分的最大层面,沿肿瘤边缘手动勾画ROI,获得该层面ADC值,记作肿瘤单层面ADC(single section ADC,ADC_(ss));在包含肿瘤实体成分的每一个层面上手动放置若干个圆形或类圆形30~50 mm^(2)的ROI,选取其中一个ROI对应的最小ADC(minimum ADC,ADC_(min))值,记作肿瘤ADC_(min)。比较PD-L1阳性表达组与阴性表达组间患者就诊时年龄、治疗前宫颈癌灶的FIGO分期、病理分级、宫旁浸润、淋巴结转移、不同ROI选择所提取的ADC值等临床、病理及影像资料。采用单、多因素logistic回归确定宫颈癌PD-L1阳性表达的独立相关因素,并构建临床病理模型及临床病理影像联合模型。通过受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)和De Long检验评估不同ADC值及模型的诊断效能。绘制联合模型的列线图,校准曲线及决策曲线。前瞻性收集2020年8月至2022年12月就诊的宫颈癌患者为验证组(332例)对列线图进行验证。结果FIGO分期、病理分级、宫旁浸润、淋巴结转移、ADC_(mean)、ADC_(ss)、ADC_(min)均为PD-L1阳性表达的独立相关因素(P均<0.05);三种ADC值中,ADC_(min)的诊断效能最高(AUC=0.882,95Objective:To explore the value of the nomogram based on clinical,pathological,and diffusion weighted imaging(DWI)quantitative parameters for predicting programmed death-ligand 1(PD-L1)positive expression in cervical cancer.Materials and Methods:A total of 683 patients with pathologically confirmed cervical cancer between January 2018 to June 2020 were retrospectively enrolled as the training cohort.They underwent pelvic MRI scans and PD-L1 immunohistochemical staining.The solid component of tumors on DWI images was identified using T2WI and enhanced images.The region of interest(ROI)was manually drawn around the tumor borders,and apparent diffusion coefficient(ADC)values were obtained from corresponding ADC pseudo-color images.The mean ADC value(ADC_(mean))was calculated by averaging ADC values from selected slices.Additionally,the maximum solid component slice on DWI was chosen,and the ADC value for this slice was recorded as single section ADC(ADC_(ss)).For each slice containing solid tumor components,multiple circular or circular-like ROIs(30-50 mm²)were placed to extract minimum ADC(ADC_(min))values.Differences in clinical,pathological,and imaging parameters including age at diagnosis,FIGO staging,pathological grade,parametrial invasion,lymph node status,and ADC values from different ROIs were compared between PD-L1 positive and negative groups.Univariable and multivariable logistic regression analyses were conducted to identify independent parameters related to PD-L1 positive expression.Clinical-pathological and combined clinical-pathological-imaging models were developed.Diagnostic effectiveness of different ADC values and models was assessed using area under the curve(AUC)of receiver operating characteristic(ROC)and DeLong test.The combined model's nomogram,calibration slope,and decision curve were evaluated.A validation cohort of 332 cervical cancer patients from July 2020 to December 2022 was enrolled to validate the nomogram.Results:FIGO staging,pathological grade,parametrial invasion,lymph node status

关 键 词：宫颈肿瘤 磁共振成像 扩散加权成像 免疫疗法 程序性死亡受体配体1 配体 

分 类 号：R445.2[医药卫生—影像医学与核医学] R737.33[医药卫生—诊断学]