ORIGINAL RESEARCH article
Front. Oncol.
Sec. Breast Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1630583
This article is part of the Research TopicAdvances in Radiation Research and Applications: Biology, Environment and MedicineView all 11 articles
Research on the Application of Distinguishing Between Benign and Malignant Breast Nodules Using MRI and US Radiomics
Provisionally accepted
- Fuling Central Hospital, Chongqing University, Chongqing, China
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This study aims to develop and validate a model based on clinical and radiomic features to investigate its value in distinguishing between benign and malignant breast nodules.Methods:The study included 139 patients with breast diseases, divided into a training set (n=111) and a validation set (n=28) at an 8:2 ratio. All patients' dynamic contrast-enhanced MRI (DCE-MRI), diffusionweighted imaging (DWI), T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and ultrasound (US) images were uploaded to the 3D Slicer software. Using a double-blind method, regions of interest (ROIs) were manually delineated on T1WI, T2WI, DWI, the first phase of DCE, and US images. Radiomic models were constructed using radiomic features. A comprehensive model was built by combining clinical and radiomic features through multivariate logistic regression and visualized as a nomogram. The area under the curve (AUC), accuracy, specificity, and sensitivity of five different radiomic models were compared to evaluate their discriminatory performance. A combined model was created using the T2WI radiomic model and clinical features, and the predictive performance of the clinical model, radiomic model, and combined model were compared and validated.Results: For the T1WI radiomic model, the AUC values for the training and test sets were 0.885 and 0.778, respectively. For the T2WI radiomic model, the AUC values were 0.950 and 0.871. For the DCE radiomic model, the AUC values were 0.854 and 0.749. For the DWI radiomic model, the AUC values were 0.878 and 0.763. For the US radiomic model, the AUC values were 0.878 and 0.737. The combined model using T2WI and clinical features achieved AUC values of 0.975 and 0.942 for the training and test sets, respectively.Conclusion:The model combining T2WI and clinical features demonstrated higher value in non-invasively distinguishing between benign and malignant breast nodules.
Keywords: Breast, Benign and malignant nodules, Radiomics, machine learning, Magnetic Resonance Imaging, Ultrasound -
Received: 18 May 2025; Accepted: 24 Jun 2025.
Copyright: © 2025 Liu, Zhou, Liu, Wei, Hu and Yu. 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:
Yifan Liu, Fuling Central Hospital, Chongqing University, Chongqing, China
Xiaoli Yu, Fuling Central Hospital, Chongqing University, Chongqing, China
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