Novel models based on machine learning to predict the prognosis of metaplastic breast cancer
Background: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice. Methods: This study utilized patient data from the SEER database (2010–2018) for da...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Breast |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0960977624001899 |
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| author | Yinghui Zhang Wenxin An Cong Wang Xiaolei Liu Qihong Zhang Yue Zhang Shaoqiang Cheng |
| author_facet | Yinghui Zhang Wenxin An Cong Wang Xiaolei Liu Qihong Zhang Yue Zhang Shaoqiang Cheng |
| author_sort | Yinghui Zhang |
| collection | DOAJ |
| description | Background: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice. Methods: This study utilized patient data from the SEER database (2010–2018) for data analysis. We utilized prognostic factors to develop a novel machine learning model (CatBoost) for predicting patient survival rates. Simultaneously, our hospital's cohort of MBC patients was utilized to validate our model. We compared the benefits of radiotherapy among the three groups of patients. Results: The CatBoost model we developed exhibits high accuracy and correctness, making it the best-performing model for predicting survival outcomes in patients with MBC (1-year AUC = 0.833, 3-year AUC = 0.806; 5-year AUC = 0.810). Furthermore, the CatBoost model maintains strong performance in an external independent dataset, with AUC values of 0.937 for 1-year survival, 0.907 for 3-year survival, and 0.890 for 5-year survival, respectively. Radiotherapy is more suitable for patients undergoing breast-conserving surgery with M0 stage [group1: (OS:HR = 0.499, 95%CI 0.320–0.777 p < 0.001; BCSS: HR = 0.519, 95%CI 0.290–0.929 p = 0.008)] and those with T3-4/N2-3M0 stage undergoing mastectomy [group2: (OS:HR = 0.595, 95%CI 0.437–0.810 p < 0.001; BCSS: HR = 0.607, 95%CI 0.427–0.862 p = 0.003)], compared to patients with stage T1-2/N0-1M0 undergoing mastectomy [group3: (OS:HR = 1.090, 95%CI 0.673–1.750 p = 0.730; BCSS: HR = 1.909, 95%CI 1.036–3.515 p = 0.038)]. Conclusion: We developed three machine learning prognostic models to predict survival rates in patients with MBC. Radiotherapy is considered more appropriate for patients who have undergone breast-conserving surgery with M0 stage as well as those in stage T3-4/N2-3M0 undergoing mastectomy. |
| format | Article |
| id | doaj-art-f276e3c6ae6343c1b46603b96fcac433 |
| institution | Kabale University |
| issn | 1532-3080 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Breast |
| spelling | doaj-art-f276e3c6ae6343c1b46603b96fcac4332025-02-12T05:30:35ZengElsevierBreast1532-30802025-02-0179103858Novel models based on machine learning to predict the prognosis of metaplastic breast cancerYinghui Zhang0Wenxin An1Cong Wang2Xiaolei Liu3Qihong Zhang4Yue Zhang5Shaoqiang Cheng6Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, ChinaDepartment of Urology Surgery, Harbin Medical University Cancer Hospital, Harbin, ChinaDepartment of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, ChinaDepartment of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, ChinaDepartment of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, ChinaDepartment of Breast Medicine, Harbin Medical University Cancer Hospital, Harbin, China; Corresponding author. Department of Breast Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, China.Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China; Corresponding author. Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, China.Background: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice. Methods: This study utilized patient data from the SEER database (2010–2018) for data analysis. We utilized prognostic factors to develop a novel machine learning model (CatBoost) for predicting patient survival rates. Simultaneously, our hospital's cohort of MBC patients was utilized to validate our model. We compared the benefits of radiotherapy among the three groups of patients. Results: The CatBoost model we developed exhibits high accuracy and correctness, making it the best-performing model for predicting survival outcomes in patients with MBC (1-year AUC = 0.833, 3-year AUC = 0.806; 5-year AUC = 0.810). Furthermore, the CatBoost model maintains strong performance in an external independent dataset, with AUC values of 0.937 for 1-year survival, 0.907 for 3-year survival, and 0.890 for 5-year survival, respectively. Radiotherapy is more suitable for patients undergoing breast-conserving surgery with M0 stage [group1: (OS:HR = 0.499, 95%CI 0.320–0.777 p < 0.001; BCSS: HR = 0.519, 95%CI 0.290–0.929 p = 0.008)] and those with T3-4/N2-3M0 stage undergoing mastectomy [group2: (OS:HR = 0.595, 95%CI 0.437–0.810 p < 0.001; BCSS: HR = 0.607, 95%CI 0.427–0.862 p = 0.003)], compared to patients with stage T1-2/N0-1M0 undergoing mastectomy [group3: (OS:HR = 1.090, 95%CI 0.673–1.750 p = 0.730; BCSS: HR = 1.909, 95%CI 1.036–3.515 p = 0.038)]. Conclusion: We developed three machine learning prognostic models to predict survival rates in patients with MBC. Radiotherapy is considered more appropriate for patients who have undergone breast-conserving surgery with M0 stage as well as those in stage T3-4/N2-3M0 undergoing mastectomy.http://www.sciencedirect.com/science/article/pii/S0960977624001899Metaplastic breast cancerCatBoost algorithmSEERSHAPRadiotherapy |
| spellingShingle | Yinghui Zhang Wenxin An Cong Wang Xiaolei Liu Qihong Zhang Yue Zhang Shaoqiang Cheng Novel models based on machine learning to predict the prognosis of metaplastic breast cancer Breast Metaplastic breast cancer CatBoost algorithm SEER SHAP Radiotherapy |
| title | Novel models based on machine learning to predict the prognosis of metaplastic breast cancer |
| title_full | Novel models based on machine learning to predict the prognosis of metaplastic breast cancer |
| title_fullStr | Novel models based on machine learning to predict the prognosis of metaplastic breast cancer |
| title_full_unstemmed | Novel models based on machine learning to predict the prognosis of metaplastic breast cancer |
| title_short | Novel models based on machine learning to predict the prognosis of metaplastic breast cancer |
| title_sort | novel models based on machine learning to predict the prognosis of metaplastic breast cancer |
| topic | Metaplastic breast cancer CatBoost algorithm SEER SHAP Radiotherapy |
| url | http://www.sciencedirect.com/science/article/pii/S0960977624001899 |
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