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.

开发了一种 1D 深度学习工具用于乳腺癌分型和生化贡献的评估，该模型基于高光谱图像和 CaRNet-V1，对乳腺癌、邻近组织以及分子亚型的分类具有高准确性，并能够评估对分类的生化影响，有潜力应用于乳腺癌活检的评估以及其他相关研究如治疗效果评估和新诊断治疗方法的开发。

基于一维卷积神经网络模型的乳腺癌亚型分类与生化内容评估，利用微红外光谱图像

One-dimensional convolutional neural network model for breast cancer  subtypes classification and biochemical content evaluation using micro-FTIR  hyperspectral images