Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning
ObjectiveTo establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology.MethodsThis retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2020.604288/full |
| _version_ | 1818384192471302144 |
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| author | Zefan Liu Guannan Zhu Xian Jiang Yunuo Zhao Hao Zeng Jing Jing Xuelei Ma Xuelei Ma |
| author_facet | Zefan Liu Guannan Zhu Xian Jiang Yunuo Zhao Hao Zeng Jing Jing Xuelei Ma Xuelei Ma |
| author_sort | Zefan Liu |
| collection | DOAJ |
| description | ObjectiveTo establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology.MethodsThis retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor signature. Next, LASSO and Random Forest methods were used to optimize and model. Finally, the clinical information was combined to accurately predict the survival outcomes of GBC patients.ResultsFifteen CT features were selected through LASSO and random forest. On the basis of relative importance GLZLM-HGZE, GLCM-homogeneity and NGLDM-coarseness were included in the final model. The hazard ratio of the CT-based model was 1.462(95% CI: 1.014–2.107). According to the median of risk score, all patients were divided into high and low risk groups, and survival analysis showed that high-risk groups had a poor survival outcome (P = 0.012). After inclusion of clinical factors, we used multivariate COX to classify patients with GBC. The AUC values in the test set and validation set for 3 years reached 0.79 and 0.73, respectively.ConclusionGBC survival outcomes could be predicted by radiomics based on LASSO and Random Forest. |
| first_indexed | 2024-12-14T03:18:21Z |
| format | Article |
| id | doaj.art-5032384aae1240799a7ac7fce2042be3 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-14T03:18:21Z |
| publishDate | 2020-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-5032384aae1240799a7ac7fce2042be32022-12-21T23:19:05ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-11-011010.3389/fonc.2020.604288604288Survival Prediction in Gallbladder Cancer Using CT Based Machine LearningZefan Liu0Guannan Zhu1Xian Jiang2Yunuo Zhao3Hao Zeng4Jing Jing5Xuelei Ma6Xuelei Ma7Laboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, ChinaLaboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, ChinaLaboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, ChinaLaboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, ChinaLaboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, ChinaLaboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, ChinaLaboratory of Tumor Targeted and Immune Therapy, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Biotherapy, Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaObjectiveTo establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology.MethodsThis retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor signature. Next, LASSO and Random Forest methods were used to optimize and model. Finally, the clinical information was combined to accurately predict the survival outcomes of GBC patients.ResultsFifteen CT features were selected through LASSO and random forest. On the basis of relative importance GLZLM-HGZE, GLCM-homogeneity and NGLDM-coarseness were included in the final model. The hazard ratio of the CT-based model was 1.462(95% CI: 1.014–2.107). According to the median of risk score, all patients were divided into high and low risk groups, and survival analysis showed that high-risk groups had a poor survival outcome (P = 0.012). After inclusion of clinical factors, we used multivariate COX to classify patients with GBC. The AUC values in the test set and validation set for 3 years reached 0.79 and 0.73, respectively.ConclusionGBC survival outcomes could be predicted by radiomics based on LASSO and Random Forest.https://www.frontiersin.org/articles/10.3389/fonc.2020.604288/fullradiomicsmachine learninggallbladder cancerprognosisrandom forest |
| spellingShingle | Zefan Liu Guannan Zhu Xian Jiang Yunuo Zhao Hao Zeng Jing Jing Xuelei Ma Xuelei Ma Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning Frontiers in Oncology radiomics machine learning gallbladder cancer prognosis random forest |
| title | Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning |
| title_full | Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning |
| title_fullStr | Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning |
| title_full_unstemmed | Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning |
| title_short | Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning |
| title_sort | survival prediction in gallbladder cancer using ct based machine learning |
| topic | radiomics machine learning gallbladder cancer prognosis random forest |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2020.604288/full |
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