Keywords
Breast Cancer, Detection, Mammography, Magnetic Resonance Imaging, Liquid Biopsy, Artificial Intelligence.
Disciplines
Biomedical Engineering and Bioengineering | Engineering | Medicine and Health Sciences
Abstract
A comprehensive evaluation of current and novel diagnostic techniques is necessary since the diagnosis of breast cancer is essential for improving treatment efficacy and raising long-term survival. Evidence from conventional imaging methods including mammography, ultrasonography, and magnetic resonance imaging is combined in this systematic review with advancements in liquid biopsies and current AI-based diagnostic tools. Using specific keywords related to breast cancer, early detection, imaging, biomarkers, and artificial intelligence, a thorough search was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore. Reviews, case reports, and studies unrelated to breast cancer were excluded; only clinical, experimental, and computational research published in English were accepted. Because research designs varied in terms of methodology, data extraction and synthesis were carried out narratively. The research is extensive: 54 studies on reader performance were included in a recent systematic review on AI-assisted breast imaging, while 136 main papers were included in another meta-analysis of liquid biopsy techniques for early detection. For every diagnostic category across many modalities, the current review makes use of dozens to more than a hundred research. Along with notable performance gains from deep learning- based classification systems, results regularly demonstrate improved sensitivity and specificity employing multimodal imaging techniques, increasing the clinical potential for detection based on circulating biomarkers. In conclusion, the results show that the best chance for accurate early detection of breast cancer is to combine sophisticated imaging, molecular testing, and AI-optimized analysis. Regular clinical application requires comprehensive prospective validation, uniform evaluation methodologies, and ongoing standardization.
Author ORCID Identifier
Lama Yassine: www.orcid.org/0009-0001-2312-5098
Amira J. Zaylaa: www.orcid.org/0000-0001-8420-5668
Recommended Citation
Yassine, Lama and Zaylaa, Amira J.
(2026)
"DETECTION AND DIAGNOSTIC SCREENING OF BREAST CANCER: A SYSTEMATIC REVIEW AND FUTURE FRONTIERS,"
BAU Journal - Science and Technology: Vol. 7:
Iss.
2, Article 10.
DOI: https://doi.org/10.54729/2959-331X.1179
ISSN
2959-331X