Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer

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.

Authors

Institutions

Publication Details

Journal
npj Breast Cancer
Published
2025-12-14
DOI
https://doi.org/10.1038/s41523-025-00860-6
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer

Lianhuang Li, Shunwu Xu, Lida Qiu, Chuan Wang et al.
npj Breast Cancer
Radiomics and Machine Learning in Medical Imaging
article

Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer

Lianhuang Li, Shunwu Xu, Lida Qiu, Chuan Wang, Deyong Kang, Fangmeng Fu, Gangqin Xi, Xiahui Han, Jianxin Chen, Jianhua Chen, Zhijun Li, Qingyuan Zhang, Shichao Zhang, Wenhui Guo, Liqin Zheng, Jianli Ma, Shuoyu Xu
article en

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.

npj Breast CancerVol. 11(1)
Fujian Normal University (CN), Fujian Medical University (CN), Harbin Medical University (CN), Minjiang University (CN), Third Affiliated Hospital of Harbin Medical University (CN), Information Today (United States) (US), Union Hospital (CN)
National Natural Science Foundation of China, Natural Science Foundation of Fujian Province
Openalex Percentile: Top 45%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.