One-dimensional convolutional neural network model for breast cancer
  subtypes classification and biochemical content evaluation using micro-FTIR
  hyperspectral images
Matheus del-Valle Emerson Soares Bernardes Denise Maria Zezell
Breast cancer treatment still remains a challenge, where molecular subtypes
classification plays a crucial role in selecting appropriate and specific
therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype,
and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is the
gold-standard evaluation, although interobserver variations are reported and
molecular signatures identification is time-consuming. Fourier transform
infrared micro-spectroscopy with machine learning approaches have been used to
evaluate cancer samples, presenting biochemical-related explainability.
However, this explainability is harder when using deep learning. This study
created a 1D deep learning tool for breast cancer subtype evaluation and
biochemical contribution. Sixty hyperspectral images were acquired from a human
breast cancer microarray. K-Means clustering was applied to select tissue and
paraffin spectra. CaReNet-V1, a novel 1D convolutional neural network, was
developed to classify breast cancer (CA) and adjacent tissue (AT), and
molecular subtypes. A 1D adaptation of Grad-CAM was applied to assess the
biochemical impact to the classifications. CaReNet-V1 effectively classified CA
and AT (test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and
0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled
the evaluation of the most contributing wavenumbers to the predictions,
providing a direct relationship with the biochemical content. Therefore,
CaReNet-V1 and hyperspectral images is a potential approach for breast cancer
biopsies assessment, providing additional information to the pathology report.
Biochemical content impact feature may be used for other studies, such as
treatment efficacy evaluation and development new diagnostics and therapeutic
methods.