磁共振成像2025，Vol.16Issue(3)：70-76,82,8.DOI:10.12015/issn.1674-8034.2025.03.011
基于术前多参数MRI的深度迁移学习预测子宫内膜癌淋巴脉管间隙浸润
Prediction of lymphovascular space invasion in endometrial carcinoma based on preoperative multiparameter MRI deep transfer learning features
摘要
Abstract
Objective:This study aimed to develop a model based on deep transfer learning(DTL)features from preoperative multiparametric magnetic resonance imaging(MRI)to predict lymphovascular space invasion(LVSI)status in patients with endometrial carcinoma(EC).Materials and Methods:A retrospective analysis was conducted on clinical information and preoperative MRI images of 187 EC patients who were surgically and pathologically confirmed in our hospital from February 2016 to July 2023.The patients were randomly divided into a training set(131 patients)and a test set(56 patients)in a 7∶3 ratio.Regions of interest were delineated on axial T2-weighted imaging,diffusion-weighted imaging,apparent diffusion coefficient(ADC)maps,and contrast-enhanced T1-weighted imaging,manually.Subsequently,12 DTL models were established using ResNet50,ResNet101,and DenseNet121 networks.Fusion models were then established using three decision-level fusion methods:mean,maximum,and minimum,with the best model selected as the final DTL model.A clinical model was established after screening clinical features through univariate and multivariate logistic regression analysis,and a DTL-clinical combined model was developed using logistic regression incorporating DTL and clinical features.The receiver operating characteristic curve was used to assess the diagnostic performance of the models for LVSI in EC patients,the area under the curve(AUC)was compared using the DeLong test.The calibration curve was used to analyze the goodness of fit of the models,and the decision curve was used to explore the clinical applicability of the models.Results:In the test set,the ResNet101 model based on the ADC images showed the highest AUC value of 0.850[95%confidence interval(CI):0.736 to 0.963]for diagnosing LVSI in EC patients.The fusion model established using the mean fusion method had the highest AUC value of 0.932(95%CI:0.868 to 0.996)in the test set,representing the best DTL model.Logistic regression analysis indicated that age was an independent risk factor for LVSI.The DTL-clinical combined model had an AUC of 0.934(95%CI:0.871 to 0.997)in the test set,with significantly better diagnostic performance than the clinical model[AUC:0.554(95%CI:0.436 to 0.671),P<0.001]and no statistical difference compared to the DTL model(P=0.909).The combined model demonstrated good fit in both the training and test sets(Hosmer-Lemeshow test:P=0.814 and 0.402,respectively)and offered greater clinical net benefit.Conclusions:The DTL model based on preoperative multiparametric MRI,as well as the combined model integrating DTL features with clinical features,can effectively predict the LVSI status of EC patients,outperforming clinical models.DTL demonstrates excellent performance on our small-sample EC MRI data,providing important clinical assistance for preoperative LVSI prediction.关键词
子宫内膜癌/淋巴脉管间隙浸润/多参数磁共振成像/深度学习/迁移学习Key words
endometrial carcinoma/lymphvascular space invasion/multiparametric magnetic resonance imaging/deep learning/transfer learning分类
医药卫生引用本文复制引用
郭冉,彭如臣,李艳翠,沈秀芝,郝攀,信瑞强..基于术前多参数MRI的深度迁移学习预测子宫内膜癌淋巴脉管间隙浸润[J].磁共振成像,2025,16(3):70-76,82,8.基金项目
Special Fund for Youth Scientific Research Incubation of Beijing Luhe Hospital of Capital Medical University in 2023(No.LHYY2023-LC209). 2023年度首都医科大学附属北京潞河医院青年科研孵育专项(编号:LHYY2023-LC209) （No.LHYY2023-LC209）
