Deep learning approach for survival prediction for patients with synovial sarcoma
Synovial sarcoma is a rare disease with diverse progression characteristics. We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. The purpose of this study is to evaluate the performance of the proposed prediction model and demonstrate its cl...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2018-09-01
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| Series: | Tumor Biology |
| Online Access: | https://doi.org/10.1177/1010428318799264 |
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| _version_ | 1849416186658291712 |
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| author | Ilkyu Han June Hyuk Kim Heeseol Park Han-Soo Kim Sung Wook Seo |
| author_facet | Ilkyu Han June Hyuk Kim Heeseol Park Han-Soo Kim Sung Wook Seo |
| author_sort | Ilkyu Han |
| collection | DOAJ |
| description | Synovial sarcoma is a rare disease with diverse progression characteristics. We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. The purpose of this study is to evaluate the performance of the proposed prediction model and demonstrate its clinical usage. The study involved 242 patients who were diagnosed with synovial sarcoma in three institutions between March 2001 and February 2013. The patients were randomly divided into a training set (80%) and a testing set (20%). Fivefold cross validation was performed utilizing the training set. The test set was retained for the final testing. A Cox proportional hazard model, simple neural network, and the proposed survival neural network were all trained utilizing the same training set, and fivefold cross validation was performed. The final testing was performed utilizing the isolated test data to determine the best prediction model. The multivariate Cox proportional hazard regression analysis revealed that size, initial metastasis, and margin were independent prognostic factors. In fivefold cross validation, the median value of the receiver-operating characteristic curve (area under the curve) was 0.87 in the survival neural network, which is significantly higher compared to the area under the curve of 0.792 for the simple neural network (p = 0.043). In the final test, survival neural network model showed the better performance (area under the curve: 0.814) compared to the Cox proportional hazard model (area under the curve: 0.629; p = 0.0001). The survival neural network model predicted survival of synovial sarcoma patients more accurately compared to Cox proportional hazard model. |
| format | Article |
| id | doaj-art-140ca0482d6d4b439d33c75c2c9c89b4 |
| institution | Kabale University |
| issn | 1423-0380 |
| language | English |
| publishDate | 2018-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Tumor Biology |
| spelling | doaj-art-140ca0482d6d4b439d33c75c2c9c89b42025-08-20T03:33:15ZengSAGE PublishingTumor Biology1423-03802018-09-014010.1177/1010428318799264Deep learning approach for survival prediction for patients with synovial sarcomaIlkyu Han0June Hyuk Kim1Heeseol Park2Han-Soo Kim3Sung Wook Seo4Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, KoreaOrthopaedic Oncology Clinic, National Cancer Center, Goyang, KoreaDepartment of Orthopaedic Surgery, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, KoreaDepartment of Orthopaedic Surgery, Seoul National University Hospital, Seoul, KoreaDepartment of Health Sciences and Technology, Sungkyunkwan University, Seoul, KoreaSynovial sarcoma is a rare disease with diverse progression characteristics. We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. The purpose of this study is to evaluate the performance of the proposed prediction model and demonstrate its clinical usage. The study involved 242 patients who were diagnosed with synovial sarcoma in three institutions between March 2001 and February 2013. The patients were randomly divided into a training set (80%) and a testing set (20%). Fivefold cross validation was performed utilizing the training set. The test set was retained for the final testing. A Cox proportional hazard model, simple neural network, and the proposed survival neural network were all trained utilizing the same training set, and fivefold cross validation was performed. The final testing was performed utilizing the isolated test data to determine the best prediction model. The multivariate Cox proportional hazard regression analysis revealed that size, initial metastasis, and margin were independent prognostic factors. In fivefold cross validation, the median value of the receiver-operating characteristic curve (area under the curve) was 0.87 in the survival neural network, which is significantly higher compared to the area under the curve of 0.792 for the simple neural network (p = 0.043). In the final test, survival neural network model showed the better performance (area under the curve: 0.814) compared to the Cox proportional hazard model (area under the curve: 0.629; p = 0.0001). The survival neural network model predicted survival of synovial sarcoma patients more accurately compared to Cox proportional hazard model.https://doi.org/10.1177/1010428318799264 |
| spellingShingle | Ilkyu Han June Hyuk Kim Heeseol Park Han-Soo Kim Sung Wook Seo Deep learning approach for survival prediction for patients with synovial sarcoma Tumor Biology |
| title | Deep learning approach for survival prediction for patients with synovial sarcoma |
| title_full | Deep learning approach for survival prediction for patients with synovial sarcoma |
| title_fullStr | Deep learning approach for survival prediction for patients with synovial sarcoma |
| title_full_unstemmed | Deep learning approach for survival prediction for patients with synovial sarcoma |
| title_short | Deep learning approach for survival prediction for patients with synovial sarcoma |
| title_sort | deep learning approach for survival prediction for patients with synovial sarcoma |
| url | https://doi.org/10.1177/1010428318799264 |
| work_keys_str_mv | AT ilkyuhan deeplearningapproachforsurvivalpredictionforpatientswithsynovialsarcoma AT junehyukkim deeplearningapproachforsurvivalpredictionforpatientswithsynovialsarcoma AT heeseolpark deeplearningapproachforsurvivalpredictionforpatientswithsynovialsarcoma AT hansookim deeplearningapproachforsurvivalpredictionforpatientswithsynovialsarcoma AT sungwookseo deeplearningapproachforsurvivalpredictionforpatientswithsynovialsarcoma |