ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gynecological Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1569729
Predicting Recurrence Risk in Endometrial Cancer: A Multisequence MRI Intratumoral and Peritumoral Radiomics Nomogram Approach
Provisionally accepted- 1Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
- 2Bengbu Medical College, Bengbu, Anhui Province, China
- 3Division of Life Sciences and Medicine, The First Affiliated Hospital of University of Science and Technology of China Anhui Provincial Hospital, Hefei, Anhui Province, China
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Objective: To assess the predictive value of a nomogram model incorporating clinical factors and multisequence MRI intratumoral and peritumoral radiomics features for estimating recurrence risk in endometrial cancer (EC) patients. Materials and Methods: This retrospective study included 184 patients with EC. The samples were randomly divided into a training set and a test set according to a 7:3 ratio, and intratumoral and peritumoral radiomics features were extracted from diffusionweighted imaging (DWI) and T2-weighted imaging (T2WI) sequences. Optimal radiomics features were selected using the f-classification function, minimum redundancy maximum relevance (mRMR) method, and least absolute shrinkage and selection operator (Lasso). Nine machine learning classifiers were employed to construct the intratumoral model (RM1). The best-performing classifiers were then used to develop the intratumoral and peritumoral 2 mm radiomics model (RM2) and the intratumoral and peritumoral 4 mm radiomics model (RM3). The radiomics scores (Rad-score) from the top-performing radiomics model were combined with clinical factors to create the nomogram model (FM). The predictive performance of the FM model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve assessment, clinical decision curve analysis (DCA), clinical impact curve (CIC), and the DeLong test. Feature importance analysis using the SHapley Additive exPlanations (SHAP) methodology. Results: The logistic regression classifier (LR) showed optimal predictive efficacy, and RM2 demonstrated the best diagnostic performance. The clinical decision curve and DeLong test results indicated that the FM model was the optimal recurrence model in EC patients. Conclusion: A nomogram model integrating MRI radiomics features from intratumoral and peritumoral regions and clinical factors effectively predicts recurrence in EC patients.
Keywords: Magnetic Resonance Imaging, peritumoral radiomics, machine learning, endometrial cancer, Recurrence
Received: 01 Feb 2025; Accepted: 10 Apr 2025.
Copyright: © 2025 Li, Ma, Chen, Wei, xu, Zhao and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhizhen Gao, Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, 233004, Anhui, China
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