  Breast cancer molecular subtypes classification plays an import role to sort
patients with divergent prognosis. The biomarkers used are Estrogen Receptor
(ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers
expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB),
HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is
used to classify subtypes, although interlaboratory and interobserver
variations can affect its accuracy, besides being a time-consuming technique.
The Fourier transform infrared micro-spectroscopy may be coupled with deep
learning for cancer evaluation, where there is still a lack of studies for
subtypes and biomarker levels prediction. This study presents a novel 2D deep
learning approach to achieve these predictions. Sixty micro-FTIR images of
320x320 pixels were collected from a human breast biopsies microarray. Data
were clustered by K-means, preprocessed and 32x32 patches were generated using
a fully automated approach. CaReNet-V2, a novel convolutional neural network,
was developed to classify breast cancer (CA) vs adjacent tissue (AT) and
molecular subtypes, and to predict biomarkers level. The clustering method
enabled to remove non-tissue pixels. Test accuracies for CA vs AT and subtype
were above 0.84. The model enabled the prediction of ER, PR, and HER2 levels,
where borderline values showed lower performance (minimum accuracy of 0.54).
Ki67 percentage regression demonstrated a mean error of 3.6%. Thus, CaReNet-V2
is a potential technique for breast cancer biopsies evaluation, standing out as
a screening analysis technique and helping to prioritize patients.
