Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer  

作　　者：Zhi-Yao Wei Zhe Zhang Dong-Li Zhao Wen-Ming Zhao Yuan-Guang Meng 

机构地区：[1]Department of Obstetrics and Gynecology,Seventh Medical Center of Chinese People’s Liberation Army General Hospital,Beijing 100700,China [2]National Genomics Data Center and Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information,Beijing Institute of Genomics,Chinese Academy of Sciences and China National Center for Bioinformation,Beijing 100700,China

出　　处：《World Journal of Clinical Cases》2024年第26期5908-5921,共14页世界临床病例杂志

摘　　要：BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer(EC)patients.Radiomics based on magnetic resonance imaging(MRI)in combination with clinical features may be useful to predict the risk grade of EC.AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.METHODS The study comprised 112 EC patients.The participants were randomly separated into training and validation groups with a 7:3 ratio.Logistic regression analysis was applied to uncover independent clinical predictors.These predictors were then used to create a clinical nomogram.Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images,the Mann-Whitney U test,Pearson test,and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features,which were subsequently utilized to generate a radiomic signature.Seven machine learning strategies were used to construct radiomic models that relied on the screening features.The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.RESULTS Having an accuracy of 0.82 along with an area under the curve(AUC)of 0.915[95%confidence interval(CI):0.806-0.986],the random forest method trained on radiomics characteristics performed better than expected.The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram(AUC:0.75,95%CI:0.611-0.899)and the combined nomogram(AUC:0.869,95%CI:0.702-0.986)that integrated clinical parameters and radiomic signature.CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

关 键 词：Endometrial cancer Risk stratification Radiomics Machine learning NOMOGRAM 

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]