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

Norhayati Mohd Zain; Wan Azani Mustafa

doi:10.14419/tckrxp49

Article Details

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

Authors:

Norhayati Mohd Zain | Wan Azani Mustafa

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.15 Issue 1, pp.20-31

Document Type:

Article

Publication Date:

8/2026

Document Language:

English

Document ID (Org No.):

2001387805

pISSN:

2227-5053

DOI:

10.14419/tckrxp49

Keywords:

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.

Article Details

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

Authors:

Norhayati Mohd Zain | Wan Azani Mustafa

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.15 Issue 1, pp.20-31

Document Type:

Article

Publication Date:

8/2026

Document Language:

English

Document ID (Org No.):

2001387805

pISSN:

2227-5053

DOI:

10.14419/tckrxp49

Artificial Intelligence

Radiomics

Contrast-Enhancement Mammography

Issues

Article Details

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

Authors: Norhayati Mohd Zain | Wan Azani Mustafa

Publisher: Science Publishing Corporation

Journal: International Journal of Basic and Applied Sciences Vol.15 Issue 1, pp.20-31

Document Type: Article

Publication Date: 8/2026

Document Language: English

Document ID (Org No.): 2001387805

pISSN: 2227-5053

DOI: 10.14419/tckrxp49

Artificial Intelligence

Radiomics

Contrast-Enhancement Mammography

Issues

Authors:

Norhayati Mohd Zain | Wan Azani Mustafa

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.15 Issue 1, pp.20-31

Document Type:

Article

Publication Date:

8/2026

Document Language:

English

Document ID (Org No.):

2001387805

pISSN:

2227-5053

DOI:

10.14419/tckrxp49