Wei ZY, Zhang Z, Zhao DL, Zhao WM, Meng YG. Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer. World J Clin Cases 2024; 12(26): 5908-5921 [DOI: 10.12998/wjcc.v12.i26.5908]
Corresponding Author of This Article
Yuan-Guang Meng, PhD, Chief Doctor, Surgeon, Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, No. 5 Nanmencang, Dongsishitiao, Dongcheng District, Beijing 100700, China. meng6512@vip.sina.com
Research Domain of This Article
Obstetrics & Gynecology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Zhi-Yao Wei, Zhe Zhang, Dong-Li Zhao, Yuan-Guang Meng, Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
Wen-Ming Zhao, 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
Author contributions: Wei ZY, Zhang Z, and Zhao DL contributed to methodology; Wei ZY contributed to writing the original draft and formal analysis; Meng YG contributed to writing, reviewing, and editing; Wei ZY and Zhao DL contributed to supervision; All authors contributed to data curation and conceptualization and read and approved the final manuscript.
Institutional review board statement: This study was performed in line with the principles of the Declaration of Helsinki. The Institutional Ethics Review Board approved this retrospective study and waived the requirement for written informed consent (No: 2022-403).
Informed consent statement: The retrospective inquiry in question was exempt from the requirement of informed consent as it had obtained approval from the institutional review board.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yuan-Guang Meng, PhD, Chief Doctor, Surgeon, Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, No. 5 Nanmencang, Dongsishitiao, Dongcheng District, Beijing 100700, China. meng6512@vip.sina.com
Received: May 26, 2024 Revised: June 19, 2024 Accepted: July 3, 2024 Published online: September 16, 2024 Processing time: 54 Days and 22.6 Hours
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.
Core Tip: Our research focused on the utilization of clinical features and radiomics derived from magnetic resonance imaging (MRI) in order to predict the risk grade of endometrial cancer (EC). In our studies, we constructed a predictive model capable of predicting preoperative EC risk. The MRI-based radiomics model put forth in this research showed strong predictive ability and great potential value for assessing the level of EC risk. The integration of predictive models into clinical practice would greatly enhance the preoperative selection of customized therapy.
