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Updated: Jan 8, 2026
Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
Published on: November 11, 2020
Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer
Zhijun Li1, Deyong Kang2, Chuan Wang3
1Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China.
Abstract:
The prognostic value of nuclear features based on tumor-associated collagen signatures (TCMF2) is still unclear. In this paper, we extracted and quantified the TCMF2 from 941 invasive breast cancer patients in H&E images. The least absolute shrinkage and selection operator regression were used to build a TCMF2-score. The univariate and multivariate Cox proportional hazards regression analyses showed that the TCMF2-score is an independent prognostic factor with an advantage in the prognosis of early-stage invasive breast cancer. When the TCMF2, the microscopic features of TACS-based collagen (TCMF1) and the tumor-associated collagen signatures (TACS) were combined, they showed better accuracy in patient stratification than the clinical model (CLI) or the model based on TACS + TCMF1. Our results identify that TCMF2 improves the performance of the TACS-based prediction model, and the TACS-based full model (TACS + TCMF1 + TCMF2) may help us stratify patients more accurately and provide more appropriate adjuvant therapy.
