ArticleDiscover oncology2026
Triple-negative breast cancer survival outcomes: prognostic model validated with SEER database.
Article in Discover oncology, 2026. 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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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
2 citing papers in PubMed.
- Developing neoantigen cancer vaccines: where are we now?Nature communications · 2026Review
- Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.PloS one · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTriple-negative breast cancer (TNBC) lacks targeted therapies and precise prognostic tools. This study developed a prognostic nomogram integrating clinicopathological factors and treatment response dynamics to improve survival prediction.
methodData from 2,978 TNBC patients (SEER database, 2000–2020) were analyzed. Independent prognostic factors were identified via Cox regression. A nomogram incorporating race, AJCC N/M stage, tumor size, surgery type, and pathological response (pCR/pPR/pNR) was constructed. Performance was evaluated using C-index, ROC-AUC, calibration, decision curve analysis (DCA), and compared to AJCC-TNM staging.
resultMultivariate analysis identified N3 stage (HR = 4.13), M1 stage (HR = 1.77), tumor size ≥ 90 mm (HR = 1.84), mastectomy (HR = 1.28), and pathological non-response (pNR, HR = 6.87) as independent risk factors (all P < 0.05). The nomogram achieved superior discrimination (C-index: 0.780 [training], 0.773 [validation] vs. TNM’s 0.715–0.720). AUCs for 1-/3-/5-year survival were 0.858/0.823/0.820 (training) and 0.0.864/0.802/0.799 (validation). Calibration errors were < 5% for 1–3-year predictions. DCA demonstrated a 7–10% net benefit increase over TNM staging, with 3.9 additional correct decisions per 100 patients at the 40% risk threshold.
conclusionThis nomogram dynamically integrates pathological treatment response, significantly outperforming TNM staging (ΔC-index = + 0.066). It enables personalized risk stratification and clinical decision-making, particularly for guiding therapy intensification in high-risk subgroups (e.g., N3/pNR). Future models should incorporate molecular biomarkers (e.g., PD-L1, BRCA) and socioeconomic variables to enhance precision.
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