Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study
The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | SLAS Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S247263032400102X |
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| author | Weijia Wang Xin Li Haiyuan Yu Fangxuan Li Guohua Chen |
| author_facet | Weijia Wang Xin Li Haiyuan Yu Fangxuan Li Guohua Chen |
| author_sort | Weijia Wang |
| collection | DOAJ |
| description | The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70 %) and validation (30 %) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making. |
| format | Article |
| id | doaj-art-24d7aafd958f49f5bc1636a4438aa6d2 |
| institution | Kabale University |
| issn | 2472-6303 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SLAS Technology |
| spelling | doaj-art-24d7aafd958f49f5bc1636a4438aa6d22024-12-18T08:51:06ZengElsevierSLAS Technology2472-63032024-12-01296100220Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison studyWeijia Wang0Xin Li1Haiyuan Yu2Fangxuan Li3Guohua Chen4Department of Oncology, Qilu Hospital of Shandong University Dezhou Hospital (Dezhou People's Hospital), Dezhou City 253000, Shandong, ChinaDepartment of Radiotherapy, Qilu Hospital of Shandong University Dezhou Hospital (Dezhou People's Hospital), Dezhou City 253000, Shandong, ChinaDepartment of Quality Management Office, Qilu Hospital of Shandong University Dezhou Hospital (Dezhou People's Hospital), Dezhou City 253000, Shandong, ChinaCancer Prevention Center, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital (TMUCIH), National Clinical Research Center for Cancer, Tianjin 300060, ChinaDepartment of Oncology, Qilu Hospital of Shandong University Dezhou Hospital (Dezhou People's Hospital), Dezhou City 253000, Shandong, China; Corresponding author.The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70 %) and validation (30 %) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making.http://www.sciencedirect.com/science/article/pii/S247263032400102XGallbladder adenocarcinomaPrognosisMachine learning modelThe Surveillance, epidemiology, and end results program (SEER) |
| spellingShingle | Weijia Wang Xin Li Haiyuan Yu Fangxuan Li Guohua Chen Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study SLAS Technology Gallbladder adenocarcinoma Prognosis Machine learning model The Surveillance, epidemiology, and end results program (SEER) |
| title | Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study |
| title_full | Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study |
| title_fullStr | Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study |
| title_full_unstemmed | Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study |
| title_short | Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study |
| title_sort | machine learning model for early prediction of survival in gallbladder adenocarcinoma a comparison study |
| topic | Gallbladder adenocarcinoma Prognosis Machine learning model The Surveillance, epidemiology, and end results program (SEER) |
| url | http://www.sciencedirect.com/science/article/pii/S247263032400102X |
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