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Background <p>Triple-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.</p> Method <p>Data 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.</p> Result <p>Multivariate analysis identified N3 stage (HR = 4.13), M1 stage (HR = 1.77), tumor size ≥ 90&#xa0;mm (HR = 1.84), mastectomy (HR = 1.28), and pathological non-response (pNR, HR = 6.87) as independent risk factors (all P &lt; 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 &lt; 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. </p> Conclusion <p>This 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.</p>

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Triple-negative breast cancer survival outcomes: prognostic model validated with SEER database

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月11日

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  • Hongyan Gao,
  • Jin Yang,
  • Yuandong Li

摘要

Background

Triple-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.

Method

Data 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.

Result

Multivariate 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.

Conclusion

This 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.