Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer - Research article

Sep 24, 2026

Uploaded by Finn

New breast cancer biomarkers are evaluated through comparison of Biglycan (BGN) protein expression in breast tissue with and without cancer. The study uses immunohistochemistry combined with digital histological scoring (D-HScore) and supervised deep learning neural networks (SDLNN). Analyses include 24 formalin-fixed paraffin-embedded samples and a larger dataset of 129 images processed by an inceptionV3 embedding model. Results show reduced BGN expression in cancer versus normal tissue and SDLNN classification accuracy of 85.3%.

LanguageEnglish

CategoryHealthcare

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Pages11

Reading Time28 min

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Abstract

Introduction

PLOS ONE OPEN ACCESS Citation: Thiesen AP, Mielczarski B, Savaris RF (2023) Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer. PLoS ONE 18(3): e0282176 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0282176 Editor: Mauro Sergio Goncalves Pavao, Universidade Federal do Rio de Janeiro, Instituto de Bioquimica Medica, BRAZIL Received: October 12, 2022 Accepted: February 8, 2023 Published: March 27, 2023 Copyright: © 2023 Thiesen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data that support the findings of our study are openly available in Harvard Dataverse repository at [https://doi.org/10](https://doi.org/10) . 7910/DVN/OPEIL7 . The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Funding: This work was supported by FIPE (Fundode Incentivo à Pesquisa e a Eventos) grant 19- RESEARCH ARTICLE Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer Ana Paula Thiesen1, Bruna Mielczarski2, Ricardo Francalacci Savaris1,2 * 1 Universidade Federal do Rio Grande do Sul, Postgraduate Program in Health Science: Surgical Sciences, Porto Alegre, Rio Grande do Sul, Brazil, 2 Department of Obstetrics and Gynecology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil * [rsavaris@hcpa.edu.br](rsavaris@hcpa.edu.br) Abstract New breast cancer biomarkers have been sought for better tumor characterization and treatment. Among these putative markers, there is Biglycan (BGN) . BGN is a class I small leucine-rich proteoglycan family of proteins characterized by a protein core with leucine-rich repeats. The objective of this study is to compare the protein expression of BGN in breast tissue with and without cancer, using immunohistochemical technique associated with digital histological score (D-HScore) and supervised deep learning neural networks (SDLNN) . In this case-control study, 24 formalin–fixed, paraffin-embedded tissues were obtained for analysis. Normal (n = 9) and cancerous (n = 15) tissue sections were analyzed by immunohistochemistry using BGN monoclonal antibody (M01-Abnova) and 3,3’-Diaminobenzidine (DAB) as the chromogen. Photomicrographs of the slides were analysed with D-HScore, using arbitrary DAB units. Another set (n = 129) with higher magnification without ROI selection, was submitted to the inceptionV3 deep neural network image embedding recognition model. Next, supervised neural network analysis, using stratified 20 fold cross validation, with 200 hidden layers, ReLu activation, and regularization at α = 0.0001 were applied for SDLNN. The sample size was calculated for a minimum of 7 cases and 7 controls, having a power = 90%, an α error = 5%, and a standard deviation of 20, to identify a decrease from the average of 40 DAB units (control) to 4 DAB units in cancer. BGN expression in DAB units [median (range)] was 6.2 (0 .8 to 12 .4) and 27.31 (5 .3 to 81 .7) in cancer and normal breast tissue, respectively, using D-HScore (p = 0.0017, Mann-Whitney test) . SDLNN classification accuracy was 85 .3%(110 out of 129; 95%CI = 78. 1% to 90 .3%) . BGN protein expression is reduced in breast cancer tissue, compared to normal tissue. 0337 at Hospital de Cl´ınicas de Porto Alegre. Support was given to buy laboratory reagents, and by Conselho Nacional de Pesquisa (CNPq) Grant 302448/2018-7 (RFS) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing i

FAQs

What is the study’s main objective regarding Biglycan (BGN)?

To compare BGN protein expression in breast tissue with and without cancer using immunohistochemical analysis combined with digital scoring and supervised deep learning.

How was BGN protein expression quantified in the study?

BGN expression was assessed by immunohistochemistry using DAB units and scored with D-HScore, and images were also analyzed with a supervised deep learning neural network pipeline.

What were the key findings and model performance?

BGN expression was significantly reduced in cancer tissue compared with normal tissue, and the SDLNN achieved 85.3% classification accuracy on the larger image set.

Sep 24, 2026

Uploaded by Finn