English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Breast cancer treatment still remains a challenge, where molecular subtypesclassification plays a crucial role in selecting appropriate and specifictherapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype,and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is thegold-standard evaluation, although interobserver variations are reported andmolecular signatures identification is time-consuming. Fourier transforminfrared micro-spectroscopy with machine learning approaches have been used toevaluate cancer samples, presenting biochemical-related explainability.However, this explainability is harder when using deep learning. This studycreated a 1D deep learning tool for breast cancer subtype evaluation andbiochemical contribution. Sixty hyperspectral images were acquired from a humanbreast cancer microarray. K-Means clustering was applied to select tissue andparaffin spectra. CaReNet-V1, a novel 1D convolutional neural network, wasdeveloped to classify breast cancer (CA) and adjacent tissue (AT), andmolecular subtypes. A 1D adaptation of Grad-CAM was applied to assess thebiochemical impact to the classifications. CaReNet-V1 effectively classified CAand AT (test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabledthe 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 cancerbiopsies assessment, providing additional information to the pathology report.Biochemical content impact feature may be used for other studies, such astreatment efficacy evaluation and development new diagnostics and therapeuticmethods.

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