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<p>We aimed to analyze the effect of surgical approach on patients with malignant phyllode tumor of the breast (MPTB) and to develop a prognostic prediction model for patients with MPTB. We extracted MPTB patients aged 18–80 years between 2000 and 2020 from the SEER database. Covariable imbalance was reduced using the propensity-score matching (PSM) method. An analysis of Cox proportional hazard regression was performed to compare breast cancer-specific survival (BCSS) with overall survival (OS). The survival curves were generated using the Kaplan–Meier method. The 5-year BCSS and 5-year OS of patients with MPTB were predicted by ten models based on machine learning. According to multivariate Cox analysis, surgical treatment of MPTB does not affect long-term survival outcomes (<i>p</i> &gt; 0.05). Among our study, the survival outcomes of mastectomy and BCS would not be statistically significant even for patients with poor pathologic type of MPTB (<i>p</i> &gt; 0.05). In terms of AUC, CatBoost performed better than other algorithms with a 5-year BCSS of 0.8488 and a 5-year OS of 0.8512. BCS and mastectomy do not make a significant difference in the long-term survival outcomes of patients with MPTB. Therefore, we suggest that BCS is feasible and preferred provided that surgical margin requirements can be met. As a trusted model, CatBoost provides better guidance and support for the systemic treatment of patients with MPTB.</p>

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Impact of surgical approach and survival prediction of malignant phyllode tumor by machine learning

  • 期刊论文

  • 发表日期：

    2025年4月7日

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  • Gongyin Zhang,
  • Foyan Xu,
  • Lixian Wan

摘要

We aimed to analyze the effect of surgical approach on patients with malignant phyllode tumor of the breast (MPTB) and to develop a prognostic prediction model for patients with MPTB. We extracted MPTB patients aged 18–80 years between 2000 and 2020 from the SEER database. Covariable imbalance was reduced using the propensity-score matching (PSM) method. An analysis of Cox proportional hazard regression was performed to compare breast cancer-specific survival (BCSS) with overall survival (OS). The survival curves were generated using the Kaplan–Meier method. The 5-year BCSS and 5-year OS of patients with MPTB were predicted by ten models based on machine learning. According to multivariate Cox analysis, surgical treatment of MPTB does not affect long-term survival outcomes (p > 0.05). Among our study, the survival outcomes of mastectomy and BCS would not be statistically significant even for patients with poor pathologic type of MPTB (p > 0.05). In terms of AUC, CatBoost performed better than other algorithms with a 5-year BCSS of 0.8488 and a 5-year OS of 0.8512. BCS and mastectomy do not make a significant difference in the long-term survival outcomes of patients with MPTB. Therefore, we suggest that BCS is feasible and preferred provided that surgical margin requirements can be met. As a trusted model, CatBoost provides better guidance and support for the systemic treatment of patients with MPTB.