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

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.

该研究使用基于傅立叶变换红外显微光谱和深度学习的二维模型 CaReNet-V2 进行乳腺癌的分类和生物标志物水平预测，通过对 60 个乳腺活检样本进行图像采集和处理，达到对乳腺癌分子亚型和生物标志物水平的准确预测，有望成为乳腺癌活检的筛查分析技术并帮助优先治疗患者。

双通道卷积神经网络利用微傅里叶红外光谱成像来预测乳腺癌亚型及生物标志物水平：雌激素受体、孕激素受体、HER2 和 Ki67

Dual-path convolutional neural network using micro-FTIR imaging to  predict breast cancer subtypes and biomarkers levels: estrogen receptor,  progesterone receptor, HER2 and Ki67