多序列MRI影像组学预测局部晚期宫颈鳞癌同步放化疗早期治疗反应的价值  

The value of multi-sequence MRI radiomics in predicting the early response to concurrent chemoradiotherapy for locally advanced cervical squamous cell carcinoma

作　　者：佟晶 卑贵光 阚杨杨 贺帅 尉丽君 罗娅红 Tong Jing;Bei Guiguang;Kan Yangyang;He Shuai;Wei Lijun;Luo Yahong(Department of Medical Imaging,Cancer Hospital of China Medical University,Liaoning Cancer Hospital&Institute,Shenyang 110042,China;Department of Radiology,General Hospital of Northern Theater Command,Shenyang 110016,China)

机构地区：[1]中国医科大学肿瘤医院辽宁省肿瘤医院医学影像科,沈阳110042 [2]北部战区总医院放射诊断科,沈阳110016

出　　处：《中华放射学杂志》2022年第11期1223-1229,共7页Chinese Journal of Radiology

基　　金：国家公益性行业科研专项基金(201402020)。

摘　　要：目的探讨MRI影像组学模型预测局部晚期宫颈鳞癌同步放化疗早期治疗反应的价值。方法回顾性收集2013年1月至2019年6月辽宁省肿瘤医院经病理证实的367例局部晚期宫颈鳞癌(国际妇产科联合会分期为ⅡB~ⅣA期)患者, 因无法手术而接受完整的同步放化疗, 于治疗前2周内及治疗第4周末行盆腔平扫MRI、DWI及动态增强MRI, 根据实体瘤疗效评价标准1.1进行评价, 将患者分为完全缓解(CR)组(247例)和非CR组(120例)。采用随机拆分法, 按7∶3比例分为训练集(256例)和验证集(111例)。由2名医师在治疗前DWI、T2WI和增强T1WI(延迟期)图像上勾画感兴趣区, 最终形成三维容积感兴趣区。于3个单序列图像分别提取1 906个影像组学特征, 并利用特征相关分析和树模型筛选特征。使用逻辑回归(LR)、支持向量机(SVM)和随机森林(RF)3种分类器学习算法进行机器学习, 获得最佳分类器。基于最佳分类器, 建立3个单序列影像组学模型, 并采用多因素LR分析得到多序列联合模型。通过DeLong检验比较3个单序列模型与多序列联合模型的受试者操作特征曲线下面积(AUC)差异。通过决策分析曲线评估多序列联合模型的临床应用价值。结果在训练集和验证集中, LR分类器模型的性能最佳。基于LR分类器, DWI、T2WI、增强T1WI序列和联合序列在训练集中的AUC分别为0.77、0.74、0.79、0.86, 验证集中的AUC分别为0.71、0.66、0.75、0.77。在训练集中, 联合模型的AUC值高于DWI、T2WI、增强T1WI序列模型, 差异均有统计学意义(Z=3.01、3.56、2.83, P=0.003、0.001、0.005);在验证集中, 多序列联合模型与T2WI模型的AUC差异有统计学意义(Z=2.46, P=0.015)。决策分析曲线显示当阈值概率在0.44~0.88范围内, 多序列联合模型产生了净效益。结论基于LR分类器, 通过综合多序列MRI图像影像组学特征建立的联合模型对评估局部晚期宫颈鳞癌同步放化疗早�Objective To investigate the predictive value of MRI radiomics model in assessing the early response to concurrent chemoradiotherapy for locally advanced cervical squamous cell carcinoma.Methods A total of 367 patients with pathologically proven locally advanced cervical squamous cell carcinoma(International Federation of Gynecology and Obstetrics stageⅡB-ⅣA)in Liaoning Cancer Hospital&Institute from January 2013 to June 2019 were retrospectively collected.The patients were unable to undergo surgery and received complete concurrent chemoradiotherapy.Pelvic plain MRI,DWI and dynamic contrast-enhanced MRI were performed within 2 weeks before treatment and at the end of the 4th week of treatment.Patients were divided into complete response(CR)group(n=247)and non-CR group(n=120)according to response evaluation criteria in solid tumors 1.1.The patients were divided into a training set(n=256)and a validation set(n=111)via a randomized split method at a ratio of 7∶3.Two radiologists drew the region of interest on the DWI,T2WI and contrast-enhanced T1WI(delayed phase)images before treatment to form the volume of interest finally.Totally 1906 radiomics features were extracted from 3 single sequence images,respectively.Feature correlation analysis and tree model were used for feature selection.Three classifier learning algorithms,namely logistic regression(LR),support vector machine and random forest,were used for machine learning and the best classifier was selected.Based on the best classifier,3 single sequence radiomics models were built,and a multi-sequence combined model was obtained by multivariate LR analysis.The differences in the area under the receiver operating characteristic curve(AUC)of the 3 single sequence models and the multi-sequence combined model were compared by DeLong test.The clinical application value of the multi-sequence combined model was evaluated by decision analysis curve.Results In the training set and validation set,the LR classifier model had the best performance.Based on the LR classi

关 键 词：宫颈肿瘤 癌 鳞状细胞 磁共振成像 影像组学 同步放化疗 

分 类 号：R737.33[医药卫生—肿瘤] R445.2[医药卫生—临床医学]