ArticleBreast cancer (Dove Medical Press)2025
A Machine-Learning Model for the Prediction of Triple-Negative Breast Cancer Based on Multiparameter MRI.
Article in Breast cancer (Dove Medical Press), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- A multiparametric magnetic resonance imaging model incorporating the relative apparent diffusion coefficient for preoperative discrimination of triple-negative breast cancer.American journal of translational research · 2026Article
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Authors and funding
7 authors.
Funding
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Abstract
Objective: To explore the difference between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) based on multi-parametric MRI imaging features and construct a prediction model to identify TNBC. Methods: A retrospective study enrolled 1353 women with 1376 malignant lesions who had no additional therapy before surgery between January 2019 and December 2020 in a single center. The images were accessed according to BI-RADS-MR Results: Necrosis, edema, the maximum diameter of lesions, enhancement ratio in each phase, time to peak, gland enhancement ratio, wash-in slope and the number and diameter of the vessels were independent predictors predicting TNBC. The AUCs of the model were 0.795 (95% CI: 0.758-0.832) and 0.705 (95% CI: 0.640-0.770) in the primary cohort and validation cohort, respectively. Conclusion: The model based on multiparameter MRI has good predictive ability and can be used to predict the probability of TNBC.
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