Development and validation of a deep learning model to predict survival of patients with esophageal cancer
ObjectiveTo compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network.MethodsIn this population-based cohort study, we developed...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.971190/full |
| _version_ | 1811341673918103552 |
|---|---|
| author | Chen Huang Yongmei Dai Qianshun Chen Hongchao Chen Yuanfeng Lin Jingyu Wu Xunyu Xu Xiao Chen |
| author_facet | Chen Huang Yongmei Dai Qianshun Chen Hongchao Chen Yuanfeng Lin Jingyu Wu Xunyu Xu Xiao Chen |
| author_sort | Chen Huang |
| collection | DOAJ |
| description | ObjectiveTo compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network.MethodsIn this population-based cohort study, we developed and validated a deep learning survival model using consecutive cases of newly diagnosed stage I to IV esophageal cancer between January 2004 and December 2015 in a Surveillance, Epidemiology, and End Results (SEER) database. The model was externally validated in an independent cohort from Fujian Provincial Hospital. The C statistic was used to compare the performance of the deep learning survival model and TNM staging system. Two other deep learning risk prediction models were trained for treatment recommendations. A Kaplan–Meier survival curve was used to compare survival between the population that followed the recommended therapy and those who did not.ResultsA total of 9069 patients were included in this study. The deep learning network showed more promising results in predicting esophageal cancer-specific survival than the TNM stage in the internal test dataset (C-index=0.753 vs. 0.638) and external validation dataset (C-index=0.687 vs. 0.643). The population who received the recommended treatments had superior survival compared to those who did not, based on the internal test dataset (hazard ratio, 0.753; 95% CI, 0.556-0.987; P=0.042) and the external validation dataset (hazard ratio, 0.633; 95% CI, 0.459-0.834; P=0.0003).ConclusionDeep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with esophageal cancer. |
| first_indexed | 2024-04-13T18:58:28Z |
| format | Article |
| id | doaj.art-a4a36260012c4d0298489402eb81a333 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-13T18:58:28Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-a4a36260012c4d0298489402eb81a3332022-12-22T02:34:10ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-08-011210.3389/fonc.2022.971190971190Development and validation of a deep learning model to predict survival of patients with esophageal cancerChen Huang0Yongmei Dai1Qianshun Chen2Hongchao Chen3Yuanfeng Lin4Jingyu Wu5Xunyu Xu6Xiao Chen7Shengli Clinical College of Fujian Medical University, Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, ChinaShengli Clinical College of Fujian Medical University, Department of Oncology, Fujian Provincial Hospital, Fuzhou, ChinaShengli Clinical College of Fujian Medical University, Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, ChinaShengli Clinical College of Fujian Medical University, Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, ChinaShengli Clinical College of Fujian Medical University, Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, ChinaShengli Clinical College of Fujian Medical University, Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, ChinaShengli Clinical College of Fujian Medical University, Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, ChinaCollege of Mathematics and Data Science (Software College), Minjiang University, Fuzhou, ChinaObjectiveTo compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network.MethodsIn this population-based cohort study, we developed and validated a deep learning survival model using consecutive cases of newly diagnosed stage I to IV esophageal cancer between January 2004 and December 2015 in a Surveillance, Epidemiology, and End Results (SEER) database. The model was externally validated in an independent cohort from Fujian Provincial Hospital. The C statistic was used to compare the performance of the deep learning survival model and TNM staging system. Two other deep learning risk prediction models were trained for treatment recommendations. A Kaplan–Meier survival curve was used to compare survival between the population that followed the recommended therapy and those who did not.ResultsA total of 9069 patients were included in this study. The deep learning network showed more promising results in predicting esophageal cancer-specific survival than the TNM stage in the internal test dataset (C-index=0.753 vs. 0.638) and external validation dataset (C-index=0.687 vs. 0.643). The population who received the recommended treatments had superior survival compared to those who did not, based on the internal test dataset (hazard ratio, 0.753; 95% CI, 0.556-0.987; P=0.042) and the external validation dataset (hazard ratio, 0.633; 95% CI, 0.459-0.834; P=0.0003).ConclusionDeep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with esophageal cancer.https://www.frontiersin.org/articles/10.3389/fonc.2022.971190/fulldeep learningesophageal cancerDeepSurvsurvival predictiontreatment recommendation |
| spellingShingle | Chen Huang Yongmei Dai Qianshun Chen Hongchao Chen Yuanfeng Lin Jingyu Wu Xunyu Xu Xiao Chen Development and validation of a deep learning model to predict survival of patients with esophageal cancer Frontiers in Oncology deep learning esophageal cancer DeepSurv survival prediction treatment recommendation |
| title | Development and validation of a deep learning model to predict survival of patients with esophageal cancer |
| title_full | Development and validation of a deep learning model to predict survival of patients with esophageal cancer |
| title_fullStr | Development and validation of a deep learning model to predict survival of patients with esophageal cancer |
| title_full_unstemmed | Development and validation of a deep learning model to predict survival of patients with esophageal cancer |
| title_short | Development and validation of a deep learning model to predict survival of patients with esophageal cancer |
| title_sort | development and validation of a deep learning model to predict survival of patients with esophageal cancer |
| topic | deep learning esophageal cancer DeepSurv survival prediction treatment recommendation |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.971190/full |
| work_keys_str_mv | AT chenhuang developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer AT yongmeidai developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer AT qianshunchen developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer AT hongchaochen developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer AT yuanfenglin developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer AT jingyuwu developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer AT xunyuxu developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer AT xiaochen developmentandvalidationofadeeplearningmodeltopredictsurvivalofpatientswithesophagealcancer |