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Objective <p>To develop a personalized risk stratification nomogram, integrating clinicopathological, sonographic, and mammographic features, to identify high-risk patients who may benefit from postmastectomy radiotherapy (PMRT).</p> Materials and methods <p>A retrospective analysis was conducted on 408 patients from Medical Center 1 (January 2011 to June 2019) and 190 patients from Medical Center 2 (January 2017 to June 2019) with pathologically staged pT1–2N1M0 breast cancer following mastectomy, with preoperative mammography (MG) and ultrasound (US) imaging. After propensity score matching (PSM), the multimodal nomogram was developed using univariate and multivariate Cox regression analyses.</p> Results <p>With multivariate analysis, independent risk factors were identified, including age, pathologic T stage, positive axillary lymph nodes, lymphovascular invasion, microcalcifications, and vascularity on US, architectural distortion, and suspicious calcifications on MG (all <i>p</i> &lt; 0.05). The <i>C</i>-index for the multimodal nomogram was 0.816 (95% CI: 0.774–0.859) in the training and 0.846 (95% CI: 0.772–0.920) in the external validation cohort, demonstrating superior prognostic accuracy, discriminative ability, and clinical applicability than clinicopathological and imaging-only models. Risk stratification using this nomogram showed that PMRT significantly improved RFS in the high-risk group (training cohort: HR = 0.392; external validation cohort: HR = 0.358, both <i>p</i> &lt; 0.05), while patients in the low-risk group did not derive benefit from PMRT (training cohort: HR = 0.173; external validation cohort: HR = 0, both <i>p</i> &gt; 0.05).</p> Conclusion <p>This multimodal nomogram served as a clinical decision-support tool for clinicians to assess the risk-benefit balance of PMRT and had potential clinical application to guide further personalized adjuvant therapy for women with pT1–2N1M0 breast cancer.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can the multimodal nomogram integrating clinicopathological, ultrasonic, and mammographic parameters identify high-risk pT1–2N1M0 patients who may benefit from postmastectomy radiation therapy</i>?</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>By effectively risk-stratifying, the nomogram identified high-risk patients who derived significant benefit from PMRT while distinguishing low-risk patients who could potentially avoid unnecessary treatment</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The multimodal nomogram served as a clinical decision-support tool for clinicians to optimize personalized adjuvant therapeutic approaches and improve survival outcomes for patients with pT1–2N1M0 breast cancer</i>.</p> Graphical Abstract <p></p>

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Identify high-risk patients of T1–2N1M0 breast cancer who benefit from postmastectomy radiotherapy: a dual-center retrospective propensity score-matched study

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Ziting Xu,
  • Yanyan Zhang,
  • Yudie Zou,
  • Yalin He,
  • Li Zhang,
  • Jiekun Huo,
  • Yu Liang,
  • Weimin Xu,
  • Yang Gao,
  • Deli Chen,
  • Shuochun Chen,
  • Weijun Huang,
  • Ge Wen,
  • Yingjia Li

摘要

Objective

To develop a personalized risk stratification nomogram, integrating clinicopathological, sonographic, and mammographic features, to identify high-risk patients who may benefit from postmastectomy radiotherapy (PMRT).

Materials and methods

A retrospective analysis was conducted on 408 patients from Medical Center 1 (January 2011 to June 2019) and 190 patients from Medical Center 2 (January 2017 to June 2019) with pathologically staged pT1–2N1M0 breast cancer following mastectomy, with preoperative mammography (MG) and ultrasound (US) imaging. After propensity score matching (PSM), the multimodal nomogram was developed using univariate and multivariate Cox regression analyses.

Results

With multivariate analysis, independent risk factors were identified, including age, pathologic T stage, positive axillary lymph nodes, lymphovascular invasion, microcalcifications, and vascularity on US, architectural distortion, and suspicious calcifications on MG (all p < 0.05). The C-index for the multimodal nomogram was 0.816 (95% CI: 0.774–0.859) in the training and 0.846 (95% CI: 0.772–0.920) in the external validation cohort, demonstrating superior prognostic accuracy, discriminative ability, and clinical applicability than clinicopathological and imaging-only models. Risk stratification using this nomogram showed that PMRT significantly improved RFS in the high-risk group (training cohort: HR = 0.392; external validation cohort: HR = 0.358, both p < 0.05), while patients in the low-risk group did not derive benefit from PMRT (training cohort: HR = 0.173; external validation cohort: HR = 0, both p > 0.05).

Conclusion

This multimodal nomogram served as a clinical decision-support tool for clinicians to assess the risk-benefit balance of PMRT and had potential clinical application to guide further personalized adjuvant therapy for women with pT1–2N1M0 breast cancer.

Key Points

Question Can the multimodal nomogram integrating clinicopathological, ultrasonic, and mammographic parameters identify high-risk pT1–2N1M0 patients who may benefit from postmastectomy radiation therapy?

Findings By effectively risk-stratifying, the nomogram identified high-risk patients who derived significant benefit from PMRT while distinguishing low-risk patients who could potentially avoid unnecessary treatment.

Clinical relevance The multimodal nomogram served as a clinical decision-support tool for clinicians to optimize personalized adjuvant therapeutic approaches and improve survival outcomes for patients with pT1–2N1M0 breast cancer.

Graphical Abstract