Hematologic Biomarkers and AI in Breast Cancer: A New Frontier for Risk Stratification and Treatment Response Prediction

Background Precision oncology for breast cancer increasingly utilizes hematologic biomarkers and artificial intelligence (AI) to enhance risk stratification and treatment response prediction. Recent advancements in liquid biopsy technologies and machine learning methods have significantly accelerated this field since 2020 (Bartolomucci et al., 2025). Methods A comprehensive review was conducted of literature published between 2020 and 2025, examining publicly available data on blood-based biomarkers including complete blood count (CBC) indices, circulating tumor DNA (ctDNA), and circulating microRNAs (miRNAs) in breast cancer. Special emphasis was placed on studies employing AI and advanced statistical modeling for assessing risk and predicting therapy outcomes. Findings from major cohorts and novel pilot studies were synthesized, and an illustrative AI-driven analysis of publicly accessible data was highlighted. Results Evidence increasingly indicates that routine hematologic parameters and advanced liquid biopsy markers possess significant prognostic and predictive value. For example, Araujo et al. (2024) demonstrated, in a cohort of approximately 400,000 women, that machine learning models incorporating age and neutrophil-to-lymphocyte ratio (NLR) effectively stratify breast cancer risk. Elevated NLR has consistently predicted worse survival outcomes (Gao et al., 2023; Xiang et al., 2023), and dynamic changes in NLR during neoadjuvant chemotherapy reliably forecast pathological complete response (Gao et al., 2023). Furthermore, ctDNA has emerged as a sensitive indicator of minimal residual disease and early recurrence, with AI-driven analyses notably enhancing the detection of cancer-specific genomic fragmentation patterns (Parikh et al., 2022). In metastatic breast cancer, shallow whole-genome sequencing combined with Bayesian modeling of ctDNA predicted treatment responses with up to 75% sensitivity, surpassing traditional tumor marker assessments (Beddowes et al., 2023). Additionally, circulating miRNA signatures, particularly total circulating miRNA levels, have shown significant prognostic implications for relapse (Ward Gahlawat et al., 2022). Discussion The reviewed findings underscore the substantial, yet underexplored potential of hematologic biomarkers, particularly when integrated with machine learning approaches. Such integration may facilitate non-invasive, cost-effective screening for breast cancer risk and provide real-time monitoring of treatment efficacy. However, challenges persist, particularly in data standardization, prospective validation, and effective clinical integration of AI-driven methodologies. Conclusion Hematologic biomarkers—ranging from straightforward CBC indices to sophisticated liquid biopsy analytes—are increasingly positioned to complement traditional risk assessment and tissue-based biomarkers. AI-driven analyses offer powerful tools to decode complex biomarker interactions, providing innovative opportunities for personalized breast cancer screening and therapy. Future multidisciplinary research and rigorous clinical trials are essential to validate and incorporate these promising approaches into standard clinical practice, ultimately improving patient outcomes and facilitating appropriately tailored treatments.