Advances in Breast Imaging: Quantitative Assessment, AI Applications, and Imaging Biomarkers
Background: Breast cancer remains a major public health burden, and improvements in imaging-based detection, risk stratification, and diagnostic workflow may support earlier diagnosis, reduce false-negative interpretations, and improve population-level screening efficiency. Recent advances in quantitative breast imaging, radiomics, and artificial intelligence (AI) have expanded the role of imaging beyond lesion detection toward tumor characterization, prognostication, and precision medicine. However, evidence regarding their diagnostic and predictive performance remains heterogeneous. This study aimed to systematically evaluate the diagnostic accuracy of quantitative imaging biomarkers, radiomics, and AI-based approaches in breast cancer and to assess their associations with tumor biological characteristics. Methods: A systematic review and meta-analysis were conducted in accordance with the PRISMA 2020 guidelines. MEDLINE (PubMed), Embase, Scopus, Web of Science, Cochrane CENTRAL, and IEEE Xplore were searched for studies published between January 2015 and December 2024. Eligible studies evaluated quantitative imaging biomarkers, radiomics, or AI applications in breast cancer diagnosis or characterization. Diagnostic performance measures, including sensitivity, specificity, area under the curve (AUC), and biomarker associations with pathological features, were extracted. A random-effects meta-analysis was performed to pool AUC values where appropriate. Results: Thirteen studies involving quantitative imaging biomarkers (n = 5), radiomics (n = 4), and AI-based detection systems (n = 4) met the inclusion criteria. For differentiation of benign and malignant breast lesions, apparent diffusion coefficient (ADC) measurements demonstrated excellent diagnostic performance, with a pooled AUC of 0.94 (95% CI: 0.91-0.97). Individual studies reported sensitivities ranging from 84.1% to 92.5% and specificities from 90.2% to 91.1%, with optimal ADC thresholds between 1.23 and 1.30 × 10−3 mm2/s. Across studies evaluating tumor grade, lower ADC values were generally associated with higher-grade tumors; however, quantitative pooling was not retained because of the small number of contributing studies, non-independent comparisons, and substantial heterogeneity. Radiomics studies achieved molecular subtype classification accuracies ranging from 77% to 100%, particularly when derived from magnetic resonance imaging (MRI) and ADC maps. AI systems showed excellent diagnostic performance, with AUCs ranging from 0.876 to 0.97, consistently matching or exceeding radiologist performance. Hybrid AI-radiologist approaches yielded the highest diagnostic accuracy and reduced false-negative interpretations. Conclusion: Quantitative imaging biomarkers, particularly ADC, provide useful diagnostic information and may offer prognostic insights in breast cancer. Radiomics shows strong potential for noninvasive molecular characterization, while AI-based systems achieve high diagnostic accuracy and improve clinical workflow when integrated with radiologist interpretation. These technologies represent promising components of precision breast imaging.