Machine learning predicts the prognosis of breast cancer patients with initial bone metastases
BackgroundBone is the most common metastatic site of patients with advanced breast cancer and the survival time is their primary concern; however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still co...
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| Format: | Article |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1003976/full |
| _version_ | 1811204355305504768 |
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| author | Chaofan Li Mengjie Liu Jia Li Weiwei Wang Cong Feng Yifan Cai Fei Wu Xixi Zhao Chong Du Yinbin Zhang Yusheng Wang Shuqun Zhang Jingkun Qu |
| author_facet | Chaofan Li Mengjie Liu Jia Li Weiwei Wang Cong Feng Yifan Cai Fei Wu Xixi Zhao Chong Du Yinbin Zhang Yusheng Wang Shuqun Zhang Jingkun Qu |
| author_sort | Chaofan Li |
| collection | DOAJ |
| description | BackgroundBone is the most common metastatic site of patients with advanced breast cancer and the survival time is their primary concern; however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still controversial.MethodThe data used for analysis in this study were obtained from the SEER database (2010–2019). We made a COX regression analysis to identify prognostic factors of patients with bone metastatic breast cancer (BMBC). Through cross-validation, we constructed an XGBoost model to predicting survival in patients with BMBC. We also investigated the prognosis of patients treated with neoadjuvant chemotherapy plus surgical and chemotherapy alone using propensity score matching and K–M survival analysis.ResultsOur validation results showed that the model has high sensitivity, specificity, and correctness, and it is the most accurate one to predict the survival of patients with BMBC (1-year AUC = 0.818, 3-year AUC = 0.798, and 5-year survival AUC = 0.791). The sensitivity of the 1-year model was higher (0.79), while the specificity of the 5-year model was higher (0.86). Interestingly, we found that if the time from diagnosis to therapy was ≥1 month, patients with BMBC had even better survival than those who started treatment immediately (HR = 0.920, 95%CI 0.869–0.974, P < 0.01). The BMBC patients with an income of more than USD$70,000 had better OS (HR = 0.814, 95%CI 0.745–0.890, P < 0.001) and BCSS (HR = 0.808 95%CI 0.735–0.889, P < 0.001) than who with income of < USD$50,000. We also found that compared with chemotherapy alone, neoadjuvant chemotherapy plus surgical treatment significantly improved OS and BCSS in all molecular subtypes of patients with BMBC, while only the patients with bone metastases only, bone and liver metastases, bone and lung metastases could benefit from neoadjuvant chemotherapy plus surgical treatment.ConclusionWe constructed an AI model to provide a quantitative method to predict the survival of patients with BMBC, and our validation results indicate that this model should be highly reproducible in a similar patient population. We also identified potential prognostic factors for patients with BMBC and suggested that primary surgery followed by neoadjuvant chemotherapy might increase survival in a selected subgroup of patients. |
| first_indexed | 2024-04-12T03:11:44Z |
| format | Article |
| id | doaj.art-13970681089046308af377a7acbd04a4 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T03:11:44Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-13970681089046308af377a7acbd04a42022-12-22T03:50:20ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-09-011010.3389/fpubh.2022.10039761003976Machine learning predicts the prognosis of breast cancer patients with initial bone metastasesChaofan Li0Mengjie Liu1Jia Li2Weiwei Wang3Cong Feng4Yifan Cai5Fei Wu6Xixi Zhao7Chong Du8Yinbin Zhang9Yusheng Wang10Shuqun Zhang11Jingkun Qu12Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Radiation Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Otolaryngology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaDepartment of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, ChinaBackgroundBone is the most common metastatic site of patients with advanced breast cancer and the survival time is their primary concern; however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still controversial.MethodThe data used for analysis in this study were obtained from the SEER database (2010–2019). We made a COX regression analysis to identify prognostic factors of patients with bone metastatic breast cancer (BMBC). Through cross-validation, we constructed an XGBoost model to predicting survival in patients with BMBC. We also investigated the prognosis of patients treated with neoadjuvant chemotherapy plus surgical and chemotherapy alone using propensity score matching and K–M survival analysis.ResultsOur validation results showed that the model has high sensitivity, specificity, and correctness, and it is the most accurate one to predict the survival of patients with BMBC (1-year AUC = 0.818, 3-year AUC = 0.798, and 5-year survival AUC = 0.791). The sensitivity of the 1-year model was higher (0.79), while the specificity of the 5-year model was higher (0.86). Interestingly, we found that if the time from diagnosis to therapy was ≥1 month, patients with BMBC had even better survival than those who started treatment immediately (HR = 0.920, 95%CI 0.869–0.974, P < 0.01). The BMBC patients with an income of more than USD$70,000 had better OS (HR = 0.814, 95%CI 0.745–0.890, P < 0.001) and BCSS (HR = 0.808 95%CI 0.735–0.889, P < 0.001) than who with income of < USD$50,000. We also found that compared with chemotherapy alone, neoadjuvant chemotherapy plus surgical treatment significantly improved OS and BCSS in all molecular subtypes of patients with BMBC, while only the patients with bone metastases only, bone and liver metastases, bone and lung metastases could benefit from neoadjuvant chemotherapy plus surgical treatment.ConclusionWe constructed an AI model to provide a quantitative method to predict the survival of patients with BMBC, and our validation results indicate that this model should be highly reproducible in a similar patient population. We also identified potential prognostic factors for patients with BMBC and suggested that primary surgery followed by neoadjuvant chemotherapy might increase survival in a selected subgroup of patients.https://www.frontiersin.org/articles/10.3389/fpubh.2022.1003976/fullbreast cancerbone metastasesXGBoost algorithmneoadjuvant chemotherapySEER |
| spellingShingle | Chaofan Li Mengjie Liu Jia Li Weiwei Wang Cong Feng Yifan Cai Fei Wu Xixi Zhao Chong Du Yinbin Zhang Yusheng Wang Shuqun Zhang Jingkun Qu Machine learning predicts the prognosis of breast cancer patients with initial bone metastases Frontiers in Public Health breast cancer bone metastases XGBoost algorithm neoadjuvant chemotherapy SEER |
| title | Machine learning predicts the prognosis of breast cancer patients with initial bone metastases |
| title_full | Machine learning predicts the prognosis of breast cancer patients with initial bone metastases |
| title_fullStr | Machine learning predicts the prognosis of breast cancer patients with initial bone metastases |
| title_full_unstemmed | Machine learning predicts the prognosis of breast cancer patients with initial bone metastases |
| title_short | Machine learning predicts the prognosis of breast cancer patients with initial bone metastases |
| title_sort | machine learning predicts the prognosis of breast cancer patients with initial bone metastases |
| topic | breast cancer bone metastases XGBoost algorithm neoadjuvant chemotherapy SEER |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1003976/full |
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