Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling
Background: Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. Thus, it is necessary to establish a novel predictive model of high accuracy for the prognosis of IBC patients. Methods: Clinical information of 1,230 IBC patients from 2010...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523324003723 |
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| _version_ | 1832591871386320896 |
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| author | Yiwei Jia Chaofan Li Cong Feng Shiyu Sun Yifan Cai Peizhuo Yao Xinyu Wei Zeyao Feng Yanbin Liu Wei Lv Huizi Wu Fei Wu Lu Zhang Shuqun Zhang Xingcong Ma |
| author_facet | Yiwei Jia Chaofan Li Cong Feng Shiyu Sun Yifan Cai Peizhuo Yao Xinyu Wei Zeyao Feng Yanbin Liu Wei Lv Huizi Wu Fei Wu Lu Zhang Shuqun Zhang Xingcong Ma |
| author_sort | Yiwei Jia |
| collection | DOAJ |
| description | Background: Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. Thus, it is necessary to establish a novel predictive model of high accuracy for the prognosis of IBC patients. Methods: Clinical information of 1,230 IBC patients from 2010 to 2020 was extracted from the Surveillance, Epidemiology and End Results (SEER) database. Cox analysis was applied to identify clinicopathological characteristics associated with the overall survival (OS) of IBC patients. Random survival forest (RSF) algorithm was adopted to construct an accurate prognostic prediction model for IBC patients. Kaplan–Meier analysis was performed for survival analyses. Results: Race, N stage, M stage, molecular subtype, history of chemotherapy and surgery, and response to neoadjuvant therapy were identified as independent predictive factors for the OS of IBC patients. The top five significant variables included surgery, response to neoadjuvant therapy, chemotherapy, breast cancer molecular subtypes, and M stage. The C-index of RSF model was 0.7704 and the area under curve (AUC) values for 1, 3, 5 years in training and validation datasets were 0.879–0.955, suggesting the excellent predictive performance of RSF model. IBC patients were divided into high-risk group and low-risk group according the risk score of RSF model, and the OS of patients in the low-risk group was significantly longer than those in the high-risk group. Conclusion: In this study, we constructed a prognosis prediction model for IBC patients through RSF algorithm, which may potentially serve as a useful tool during clinical decision-making. |
| format | Article |
| id | doaj-art-c9d3a99234f947da981f1bd972ec3a55 |
| institution | Kabale University |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-c9d3a99234f947da981f1bd972ec3a552025-01-22T05:41:27ZengElsevierTranslational Oncology1936-52332025-02-0152102246Prognostic prediction for inflammatory breast cancer patients using random survival forest modelingYiwei Jia0Chaofan Li1Cong Feng2Shiyu Sun3Yifan Cai4Peizhuo Yao5Xinyu Wei6Zeyao Feng7Yanbin Liu8Wei Lv9Huizi Wu10Fei Wu11Lu Zhang12Shuqun Zhang13Xingcong Ma14The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, ChinaDepartment of Tumor and Immunology in Precision Medical Institute, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, ChinaThe Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China; Corresponding authors.The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China; Corresponding authors.Background: Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. Thus, it is necessary to establish a novel predictive model of high accuracy for the prognosis of IBC patients. Methods: Clinical information of 1,230 IBC patients from 2010 to 2020 was extracted from the Surveillance, Epidemiology and End Results (SEER) database. Cox analysis was applied to identify clinicopathological characteristics associated with the overall survival (OS) of IBC patients. Random survival forest (RSF) algorithm was adopted to construct an accurate prognostic prediction model for IBC patients. Kaplan–Meier analysis was performed for survival analyses. Results: Race, N stage, M stage, molecular subtype, history of chemotherapy and surgery, and response to neoadjuvant therapy were identified as independent predictive factors for the OS of IBC patients. The top five significant variables included surgery, response to neoadjuvant therapy, chemotherapy, breast cancer molecular subtypes, and M stage. The C-index of RSF model was 0.7704 and the area under curve (AUC) values for 1, 3, 5 years in training and validation datasets were 0.879–0.955, suggesting the excellent predictive performance of RSF model. IBC patients were divided into high-risk group and low-risk group according the risk score of RSF model, and the OS of patients in the low-risk group was significantly longer than those in the high-risk group. Conclusion: In this study, we constructed a prognosis prediction model for IBC patients through RSF algorithm, which may potentially serve as a useful tool during clinical decision-making.http://www.sciencedirect.com/science/article/pii/S1936523324003723Inflammatory breast cancerRandom survival forestMachine learningSEER |
| spellingShingle | Yiwei Jia Chaofan Li Cong Feng Shiyu Sun Yifan Cai Peizhuo Yao Xinyu Wei Zeyao Feng Yanbin Liu Wei Lv Huizi Wu Fei Wu Lu Zhang Shuqun Zhang Xingcong Ma Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling Translational Oncology Inflammatory breast cancer Random survival forest Machine learning SEER |
| title | Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling |
| title_full | Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling |
| title_fullStr | Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling |
| title_full_unstemmed | Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling |
| title_short | Prognostic prediction for inflammatory breast cancer patients using random survival forest modeling |
| title_sort | prognostic prediction for inflammatory breast cancer patients using random survival forest modeling |
| topic | Inflammatory breast cancer Random survival forest Machine learning SEER |
| url | http://www.sciencedirect.com/science/article/pii/S1936523324003723 |
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