Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study
Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study
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放射科医师使用基于MRI的放射组学辅助预测子宫内膜癌盆腔淋巴结转移：一项多中心研究
DOI：
10.1007/s00330-020-07099-8
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发表时间：
2020-08-04
影响因子：
5.9
通讯作者：
Qiang, Jin Wei
中科院分区：
文献类型：
--
作者：
Yan, Bi Cong;Li, Ying;Qiang, Jin Wei
目的建立MRI放射组学模型，帮助放射科医师提高对子宫内膜癌（EC）术前盆腔淋巴结转移（PLNM）的评估。方法2014年1月至2019年5月，将来自5个不同中心（A至E）的622例EC患者（年龄56.6 +/- 8.8岁，年龄范围27-85岁）分为训练集、验证集1（来自A中心的351例）和验证集2（来自B-E中心的271例）。基于T2WI、DWI、ADC和CE-T1WI图像提取放射组学特征，利用随机森林分类器选择最相关的放射组学特征，构建放射组学模型。ROC曲线用于评估训练集和验证集的表现，放射科医生仅基于MRI结果，并借助放射组学模型。使用临床决定性曲线（CDC）、净重分类指数（NRI）和总综合区分指数（IDI）来评估使用放射组学模型的临床效益。结果训练集的AUC分别为0.935，验证集1和验证集2的AUC分别为0.909和0.885，单独放射科医师1和2的AUC分别为0.623和0.643，放射学辅助放射科医师1和2的AUC分别为0.814和0.842。AUC、CDC、NRI和IDI显示放射学辅助放射科医师的诊断性能和临床净效益高于单独放射科医师。结论基于mri的放射组学模型可用于评估盆腔淋巴结的状态，帮助放射科医生提高预测EC中PLNM的性能。
Objective To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively. Methods During January 2014 and May 2019, 622 EC patients (age 56.6 +/- 8.8 years; range 27-85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B-E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model. Results The AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone. Conclusions The MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC.