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.
Highlights
Within the tumor microenvironment, aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework
We evaluated the consistency of the collagen framework annotation procedure by Bland–Altman difference analysis of the two “manual” approaches focusing on differences in the count of annotated objects, average object size, and the dominant orientation of annotated objects
We explored the informative value of bright-field microscopy images to capture the collagen framework in tumor tissue by an artificial neural network (ANN)
Summary
Aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Collagen-dense microenvironment may have multiple impacts: it can be viewed as a static, spacefilling material in which tumor cells are embedded, known to stimulate metastatic tumor progression by directing the migration of malignant cells along the straightened and aligned structure of ECM towards the blood vessels[5,6]. In addition to this “biomechanical” aspect, collagen participates in biological modulation of cellular events by interacting with specific cellular receptors to trigger various signaling pathways. Image-based collagen biomarkers and the potential clinical value of this technique remain to be explored
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