ArticleFrontiers in oncology2026
Development and verification of a prognostic nomogram model for triple-negative breast cancer patients with neoadjuvant therapy.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: A favorable prognostic nomogram model for predicting overall survival (OS) and stratifying prognostic risk in triple-negative breast cancer (TNBC) patients treated with neoadjuvant therapy (NAT) is lacking. The aim of this study was to formulate an excellent model for predicting the prognosis of these patients. Methods: From January 2018 to December 2024, 804 patients with TNBC who underwent NAT at Henan Cancer Hospital were included. Patients were randomly assigned to a training cohort (n = 603) or an internal validation cohort (n = 201). A prognostic nomogram model was developed and validated. Predictive performance and discrimination were further measured and compared with other models. Results: Age, pretreatment tumor size, pretreatment lymph node metastasis, pathological status, skin invasion, and lymphovascular invasion (LVI) were identified as independent prognostic factors and were included in the construction of a prognostic model for TNBC treated with NAT. The time-dependent receiver operating characteristic (ROC) and the C-index of our model consistently outperformed other prognostic factors in both the training and internal validation cohorts. Using the optimal cut-off values (90 and 160) selected by X-tile, patients were stratified into low-risk (total points ≤ 90), moderate-risk (90 < total points ≤160), and high-risk (total points > 160) groups with significantly different OS. Conclusions: In patients with TNBC, the large sample nomogram model, including age, pretreatment tumor size, pretreatment lymph node metastasis, pathological status, skin invasion, and LVI, was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, risk stratification derived from the model can identify unfavorable survival in early-stage TNBC, guide personalized treatment strategies, and select comparable study cohorts in clinical trials.
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