ArticleAnnals of translational medicine2020
Development and validation of a prognostic nomogram for patients with triple-negative breast cancer with histology of infiltrating duct carcinoma.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.NPJ precision oncology · 2026Article
- Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts.Scientific reports · 2025Article
- Review
- Analysis of prognostic factors and nomogram construction for postoperative survival of triple-negative breast cancer.Frontiers in immunology · 2025Article
- Risk Stratification Model for Predicting the Overall Survival of Elderly Triple-Negative Breast Cancer Patients: A Population-Based Study.Frontiers in medicine · 2021Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to develop prognostic nomograms from a cohort of patients with triple-negative breast cancer (TNBC) with histology of infiltrating duct carcinoma (IDC) by correlating their clinical and pathological parameters with the rates of disease-free survival (DFS) and overall survival (OS).
methodsWe retrospectively analyzed TNBC patients with histology of IDC at our institution between 2009 and 2012. Age, family history, menopausal status, surgery type, T stage, N stage, histological grade, vascular invasion, perineural invasion, cytokeratin 5/6 status, Ki-67 expression, and epithelial cadherin (E-cadherin) status were analyzed. Predictors were used in multivariable logistic regression analysis to develop a nomogram to predict DFS and OS rates. The nomograms were then subjected to internal validation, with external validation of the nomogram for predicting OS using separate cohorts of TNBC patients known from the Cancer Genome Atlas (TCGA) database. Using the concordance index (C-index) with calibration curves, the predictive accuracy and discriminative ability were calculated.
resultsA total of 242 eligible TNBC patients were included for analysis. The median follow-up time was 70.73 months. Of the patients, 32.6%, 42.6%, and 24.8% had stage I, II, and III disease, respectively. The 3- and 5-year survival rates were 81.0% and 76.5% for DFS, and 86.5% and 81.1%, for OS, respectively. Age, T stage, N stage, and E-cadherin status were found to be risk factors. The nomograms based on those risk factors accurately predicted the 3- and 5-year survival rates. The C-index was 0.798 and 0.821 for DFS and OS, respectively. Besides, the nomogram for OS showed relatively reliable performance in stratifying different risk groups of patients in training and validation cohorts identified from the TCGA database. The C-index reached 0.843. DFS validation was not completed, as there was insufficient data.
conclusionsUsing clinicopathological information, we produced a prognostic nomogram that accurately predicts the 3- and 5-year DFS and OS for patients with TNBC with histology of IDC. More external confirmation is required.
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