Applications of Artificial Intelligence and Radiomics inContrast-Enhanced ‎Mammography: A RecentSystematic Analysis

  • Authors

    • Norhayati Mohd Zain Medical Imaging Department, School of Integrative Medicine & Life Sciences, KPJ Healthcare University, 71800 ‎Nilai, Malaysia https://orcid.org/0000-0001-7216-087X
    • Wan Azani Mustafa Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia and Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, ‎‎02600 Arau, Perlis, Malaysia
    https://doi.org/10.14419/tckrxp49

    Received date: December 4, 2025

    Accepted date: January 2, 2026

    Published date: January 8, 2026

  • Artificial Intelligence; Radiomics; Contrast-Enhancement Mammography

  • Abstract

    This systematic literature review investigates the recent advancements in the applications of Artificial Intelligence (AI) and Radiomics in ‎Contrast-Enhanced Mammography (CEM), focusing on their diagnostic, predictive, and prognostic value in breast cancer imaging. The ‎study aims to synthesize current evidence on the integration of AI-based algorithms and radiomic approaches that enhance lesion ‎detection, classification, and clinical interpretation. The review follows the PRISMA protocol to ensure methodological rigor and ‎transparency. A comprehensive search was conducted across Scopus and PubMed databases using the keywords contrast, ‎mammography, and artificial intelligence, retrieving relevant studies published in 2025. After applying inclusion and exclusion criteria, 36 ‎primary studies were selected for qualitative synthesis. The analysis identified three major thematic domains: (1) AI architectures and ‎classification/detection models, (2) Radiomics and multi-modality predictive/prognostic models, and (3) Segmentation, ‎microcalcification, and data-tooling for detection. The findings revealed that hybrid and ensemble deep learning models significantly ‎improved diagnostic performance, while radiomics-based approaches enhanced molecular subtype prediction, risk stratification, and ‎treatment planning. Furthermore, advances in segmentation and synthetic data augmentation improved lesion localization and model ‎robustness, supporting more accurate and reproducible image interpretation. Despite methodological progress, challenges persist ‎regarding data standardization, model explainability, and clinical validation across diverse populations. The review concludes that ‎integrating AI and radiomics within CEM holds substantial potential for transforming breast cancer diagnostics by improving precision, ‎interpretability, and clinical decision-making. Continued development of standardized frameworks and multicenter validation is ‎essential to ensure reliable, ethical, and clinically applicable AI adoption in breast imaging practice‎.

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    Mohd Zain, N., & Mustafa, W. A. (2026). Applications of Artificial Intelligence and Radiomics inContrast-Enhanced ‎Mammography: A RecentSystematic Analysis. International Journal of Basic and Applied Sciences, 15(1), 20-31. https://doi.org/10.14419/tckrxp49