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

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.

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

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.