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Deep learning-based survival prediction of oral cancer patients

Authors
 Dong Wook Kim  ;  Sanghoon Lee  ;  Sunmo Kwon  ;  Woong Nam  ;  In-Ho Cha  ;  Hyung Jun Kim 
Citation
 SCIENTIFIC REPORTS, Vol.9(1) : 6994, 2019 
Journal Title
 SCIENTIFIC REPORTS 
Issue Date
2019
Abstract
The Cox proportional hazards model commonly used to evaluate prognostic variables in survival of cancer patients may be too simplistic to properly predict a cancer patient's outcome since it assumes that the outcome is a linear combination of covariates. In this retrospective study including 255 patients suitable for analysis who underwent surgical treatment in our department from 2000 to 2017, we applied a deep learning-based survival prediction method in oral squamous cell carcinoma (SCC) patients and validated its performance. Survival prediction using DeepSurv, a deep learning based-survival prediction algorithm, was compared with random survival forest (RSF) and the Cox proportional hazard model (CPH). DeepSurv showed the best performance among the three models, the c-index of the training and testing sets reaching 0.810 and 0.781, respectively, followed by RSF (0.770/0.764), and CPH (0.756/0.694). The performance of DeepSurv steadily improved with added features. Thus, deep learning-based survival prediction may improve prediction accuracy and guide clinicians both in choosing treatment options for better survival and in avoiding unnecessary treatments.
Files in This Item:
T201901718.pdf Download
DOI
10.1038/s41598-019-43372-7
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Dong Wook(김동욱) ORCID logo https://orcid.org/0000-0001-6167-6475
Kim, Hyung Jun(김형준) ORCID logo https://orcid.org/0000-0001-8247-4004
Nam, Woong(남웅) ORCID logo https://orcid.org/0000-0003-0146-3624
Cha, In Ho(차인호) ORCID logo https://orcid.org/0000-0001-8259-2190
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/170055
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