Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients
Autor: | Aida Laurinaviciene, Povilas Treigys, Mindaugas Morkunas, Dovile Zilenaite, Arvydas Laurinavicius |
---|---|
Rok vydání: | 2021 |
Předmět: |
0301 basic medicine
Pathology chemistry.chemical_compound Breast cancer 0302 clinical medicine Neoplasms Image Processing Computer-Assisted Tumor Microenvironment Breast Lymph node Aged 80 and over Multidisciplinary Tissue microarray Carcinoma Ductal Breast Middle Aged Prognosis Primary tumor Extracellular Matrix Gene Expression Regulation Neoplastic Treatment Outcome medicine.anatomical_structure 030220 oncology & carcinogenesis Medicine Female Collagen Breast carcinoma Adult Diagnostic Imaging medicine.medical_specialty Science Breast Neoplasms Article 03 medical and health sciences Biomarkers Tumor medicine Humans Sirius Red Aged Proportional Hazards Models business.industry Histology medicine.disease 030104 developmental biology chemistry Tumor progression Cancer imaging Neural Networks Computer business |
Zdroj: | Scientific reports, Berlin : Nature research, 2021, vol. 11, no. 1, art. no. 15474, p. [1-13] Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) Scientific Reports |
ISSN: | 2045-2322 |
Popis: | 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 |
Databáze: | OpenAIRE |
Externí odkaz: |