Label-Free Biochemical Characterization and Subtype Classification of Human Breast Pathologies Using Fingerprint Raman Spectroscopy and Machine Learning

Breast cancer remains a major cause of cancer-related mortality, highlighting the need for improved molecular characterization of breast pathologies. Raman spectroscopy (RS) provides a label-free, non-destructive approach for probing tissue biochemical composition. This study investigated Raman spectral alterations across five human breast histopathological categories and explored their potential for machine learning-assisted classification. Formalin-fixed, paraffin-embedded (FFPE) tissue specimens were obtained from five patients, with one patient representing each category: normal breast tissue, intraductal papilloma, high-grade ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC). A total of 100 Raman spectra (20 per patient) were acquired using 785 nm excitation over the fingerprint region (500–2000 cm−1). Spectral preprocessing involved Savitzky–Golay smoothing, asymmetrically reweighted penalized least-squares (arPLS) baseline correction, and vector normalization. The investigated spectra exhibited reduced relative lipid-associated contributions near 1063, 1300, and 1445 cm−1, accompanied by increased relative protein- and nucleic-acid-associated features near 1003, 1420, and 1600–1650 cm−1. The phenylalanine-to-lipid intensity ratio (I1003/I1445) increased from ≤0.2 in normal tissue to ≥1.5 in invasive lesions, suggesting a potential spectroscopic descriptor of biochemical remodeling. Exploratory subtype-associated differences were also observed, including a prominent aromatic C=C-associated feature near 1618 cm−1 in IDC and an enhanced carbohydrate-associated contribution near 890 cm−1 in ILC. A support vector machine (SVM) classifier was evaluated using patient-independent leave-one-patient-out (LOPO) cross-validation. Across the five held-out patients, the model correctly classified 89 of 100 spectra, achieving an overall spectrum-level accuracy of 89.0%, sensitivity of 90.0%, specificity of 87.5%, and F1-score of 90.8%. Although these findings demonstrate encouraging discrimination within the investigated cohort, the limited number of biologically independent patients and the representation of each histopathological category by a single patient restrict the generalizability of the results. This study provides preliminary proof-of-concept evidence supporting the integration of Raman-derived biochemical features with machine learning for breast pathology characterization, warranting further validation in larger, multi-patient and multicenter cohorts.