Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Abstract Within the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mindaugas Morkunas, Dovile Zilenaite, Aida Laurinaviciene, Povilas Treigys, Arvydas Laurinavicius
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ccbfdc0545846f997f05b87e19fde0a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2ccbfdc0545846f997f05b87e19fde0a
record_format	dspace
spelling	oai:doaj.org-article:2ccbfdc0545846f997f05b87e19fde0a2021-12-02T16:31:02ZTumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients10.1038/s41598-021-94862-62045-2322https://doaj.org/article/2ccbfdc0545846f997f05b87e19fde0a2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94862-6https://doaj.org/toc/2045-2322Abstract Within the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value < 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.Mindaugas MorkunasDovile ZilenaiteAida LaurinavicienePovilas TreigysArvydas LaurinaviciusNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mindaugas Morkunas Dovile Zilenaite Aida Laurinaviciene Povilas Treigys Arvydas Laurinavicius Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
description	Abstract Within the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value < 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.
format	article
author	Mindaugas Morkunas Dovile Zilenaite Aida Laurinaviciene Povilas Treigys Arvydas Laurinavicius
author_facet	Mindaugas Morkunas Dovile Zilenaite Aida Laurinaviciene Povilas Treigys Arvydas Laurinavicius
author_sort	Mindaugas Morkunas
title	Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
title_short	Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
title_full	Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
title_fullStr	Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
title_full_unstemmed	Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
title_sort	tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2ccbfdc0545846f997f05b87e19fde0a
work_keys_str_mv	AT mindaugasmorkunas tumorcollagenframeworkfrombrightfieldhistologyimagespredictsoverallsurvivalofbreastcarcinomapatients AT dovilezilenaite tumorcollagenframeworkfrombrightfieldhistologyimagespredictsoverallsurvivalofbreastcarcinomapatients AT aidalaurinaviciene tumorcollagenframeworkfrombrightfieldhistologyimagespredictsoverallsurvivalofbreastcarcinomapatients AT povilastreigys tumorcollagenframeworkfrombrightfieldhistologyimagespredictsoverallsurvivalofbreastcarcinomapatients AT arvydaslaurinavicius tumorcollagenframeworkfrombrightfieldhistologyimagespredictsoverallsurvivalofbreastcarcinomapatients
_version_	1718383860916420608

Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Ejemplares similares