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

Abstract

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 in situ 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 ex-vivo. 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 ex-vivo 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 $940{\mathrm{cm}}^{-1}$ and the symmetric ring breathing at $1004{\mathrm{cm}}^{-1}$ associated with phenylalanine.

Conclusions: Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.

Keywords: Raman spectroscopy; biochemistry; breast cancer; breast-conserving surgery; machine learning; support vector machines; tissue optics.