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Deep learning-based survival prediction of oral cancer patients

 Dong Wook Kim  ;  Sanghoon Lee  ;  Sunmo Kwon  ;  Woong Nam  ;  In-Ho Cha  ;  Hyung Jun Kim 
 SCIENTIFIC REPORTS, Vol.9(1) : 6994, 2019 
Journal Title
Issue Date
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.
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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
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