Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer.

[SIGNIFICANCE] As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing detection of cancer could reduce re-excision procedure rates and improve patient survival.

[AIM] Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue . The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer.

[APPROACH] The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis.

[RESULTS] Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around and the symmetric ring breathing at associated with phenylalanine.

[CONCLUSIONS] Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.