A COMPREHENSIVE EVALUATION OF BREAST CANCER DETECTION AND DIAGNOSTIC METHODS: TRADITIONAL AND EMERGING TECHNOLOGIES
TL;DR
No single modality is universally optimal for breast cancer detection, and a risk-stratified, personalised screening approach is recommended, with DBT as preferred standard for average-risk populations and MRI for high-risk individuals.
Abstract
ABSTRACT: Background: Breast cancer is the most frequently diagnosed malignancy in women worldwide, with over 2.3 million new cases annually. Despite advances in treatment, early detection remains the primary determinant of survival. Objective: To systematically evaluate the diagnostic performance of Digital Mammography (DM), Digital Breast Tomosynthesis (DBT), Ultrasound, MRI, Contrast-Enhanced Mammography (CEM), and AI-assisted imaging for breast cancer detection in women aged 30–70 years. Methods: A PRISMA-compliant systematic review of peer-reviewed literature (2010–2024) was conducted. Studies sourced from PubMed/MEDLINE, Cochrane Library, Embase, and Scopus. QUADAS-2 was applied for quality assessment. Outcome measures: cancer detection rate (CDR), sensitivity, specificity, stage at diagnosis, and recall rate. Results: Of 142 included studies (>4.2 million examinations), DBT demonstrated superior CDR (4.0–6.5/1000; 95% CI: 3.7–6.9) over DM (3.4–5.0/1000; 95% CI: 3.1–5.4) with lower recall rates. MRI achieved the highest sensitivity (90–99%) but lowest specificity (72–89%). CEM and AI-assisted imaging showed clinically promising performance, though evidence remains emerging. Conclusion: No single modality is universally optimal. A risk-stratified, personalised screening approach is recommended, with DBT as preferred standard for average-risk populations and MRI for high-risk individuals. Future prospective trials should address equity, AI validation, and access.