ArticleNPJ precision oncology2026
Multidimensional MRI radiomics-based model predicts recurrence risk in triple-negative breast cancer.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Development and external validation of a machine learning-based multimodal radiomics nomogram for predicting progression-free survival in triple-negative breast cancer.Translational cancer research · 2026Article
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Authors and funding
8 authors.
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Abstract
Patients with triple-negative breast cancer (TNBC) often have a poor prognosis. Existing staging systems cannot accurately assess patients' recurrence risk, due to the tumor's significant heterogeneity. Given the lack of a reliable prognostic assessment method, this study aimed to propose a combined multimodal prognostic model. We systematically integrated the clinicopathological indicators with multi-scale radiomics features in a large-scale TNBC cohort (n = 700). Remarkably, "intratumoral habitat heterogeneity analysis" and "peritumoral microenvironment characteristics" were innovatively incorporated in our model to reflect the potential biological behavior of individual tumors. The best consistency index (C-index) illuminated the superior prognostic capabilities of the combined model. Time-dependent ROC analysis demonstrated robust AUC at the 1-, 3-, and 5-year disease-free survival (DFS). Overall, our predictive model could provide a more comprehensive description of the biological aggressiveness of TNBC, overcoming the limitations of traditional staging systems in individualized prognosis assessment and better assisting doctors in developing personalized treatment and follow-up plans.
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Registered trials
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