{"title":"Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer.","authors":"Zhi-Yao Wei,Zhe Zhang,Dong-Li Zhao,Wen-Ming Zhao,Yuan-Guang Meng","doi":"10.12998/wjcc.v12.i26.5908","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nPreoperative 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.\r\n\r\nAIM\r\nTo construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.\r\n\r\nMETHODS\r\nThe 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.\r\n\r\nRESULTS\r\nHaving 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.\r\n\r\nCONCLUSION\r\nThe MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.","PeriodicalId":23912,"journal":{"name":"World Journal of Clinical Cases","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Clinical Cases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.12998/wjcc.v12.i26.5908","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The World Journal of Clinical Cases (WJCC) is a high-quality, peer reviewed, open-access journal. The primary task of WJCC is to rapidly publish high-quality original articles, reviews, editorials, and case reports in the field of clinical cases. In order to promote productive academic communication, the peer review process for the WJCC is transparent; to this end, all published manuscripts are accompanied by the anonymized reviewers’ comments as well as the authors’ responses. The primary aims of the WJCC are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in clinical cases.