ORIGINAL RESEARCH article
Front. Oncol.
Sec. Breast Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1593806
This article is part of the Research TopicAdvancing Breast Cancer Care Through Transparent AI and Federated Learning: Integrating Radiological, Histopathological, and Clinical Data for Diagnosis, Recurrence Prediction, and SurvivorshipView all 10 articles
Advancing Breast Cancer Relapse Prediction with Radiomics and Neural Networks: A Clinically Interpretable Framework
Provisionally accepted
- 1Department of Computer Engineering and Mathematics, School of Engineering, University of Rovira i Virgili, Tarragona, Spain
- 2Biomedical Research Institute, Hospital Verge de la Cinta, Tortosa, Spain
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Early assessment of breast cancer relapse can significantly impact survival rates and overall oncological outcomes, highlighting the need to use sophisticated diagnostic strategies in clinical trials. This work utilizes clinically relevant radiomic features extracted from digital mammograms to develop a deep learning-based model for forecasting breast cancer relapse. Features, including tumor size, shape, margin characteristics, molecular subtype, and breast density, were systematically extracted from our private, in-house dataset, providing a comprehensive representation of intrinsic tumor properties and assisting in relapse prediction. The predictive model demonstrated outstanding performance with an average area under the curve (AUC) of 0.957, highlighting its effectiveness in identifying possible relapse. This approach not only underscores the abilities of radiomics in enhancing the granularity of tumor assessment but also assists in identifying cancer recurrence during the treatment stage, promising significant strides toward personalized cancer therapy.
Keywords: Breast cancer recurrence, diagnosis, personalized treatment, relapse, Clinical features, radiomic features
Received: 14 Mar 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Khalid, Mursil, Lopez, Bosch, Puig and Rashwan. 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:
Adnan Khalid, Department of Computer Engineering and Mathematics, School of Engineering, University of Rovira i Virgili, Tarragona, 43007, Spain
Hatem A. Rashwan, Department of Computer Engineering and Mathematics, School of Engineering, University of Rovira i Virgili, Tarragona, 43007, Spain
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