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 molecular subtypes classification plays an import role to sortpatients with divergent prognosis. The biomarkers used are Estrogen Receptor(ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkersexpression levels, subtypes are classified as Luminal A (LA), Luminal B (LB),HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry isused to classify subtypes, although interlaboratory and interobservervariations can affect its accuracy, besides being a time-consuming technique.The Fourier transform infrared micro-spectroscopy may be coupled with deeplearning for cancer evaluation, where there is still a lack of studies forsubtypes and biomarker levels prediction. This study presents a novel 2D deeplearning approach to achieve these predictions. Sixty micro-FTIR images of320x320 pixels were collected from a human breast biopsies microarray. Datawere clustered by K-means, preprocessed and 32x32 patches were generated usinga fully automated approach. CaReNet-V2, a novel convolutional neural network,was developed to classify breast cancer (CA) vs adjacent tissue (AT) andmolecular subtypes, and to predict biomarkers level. The clustering methodenabled to remove non-tissue pixels. Test accuracies for CA vs AT and subtypewere 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-V2is a potential technique for breast cancer biopsies evaluation, standing out asa screening analysis technique and helping to prioritize patients.

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