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 2019 article cited by 320

Source Open-access PDF More in Medicine & Health

importance 4/10 ai assisted Machine Learning in Healthcare oncology stage: research

Problem

Traditional Cox proportional hazards models may be too simplistic to accurately predict survival outcomes for oral squamous cell carcinoma patients.

Why it matters

Better survival prediction can inform treatment decisions for oral cancer patients.

What the AI did

DeepSurv, a deep learning-based survival prediction algorithm, was trained on 255 retrospective patients and compared against random survival forest and Cox models.

Results

DeepSurv c-index 0.810 training / 0.781 testing, beating RSF (0.770/0.764) and CPH (0.756/0.694)

Impact

DeepSurv outperformed standard methods on a 255-patient retrospective cohort; clinical adoption not stated.

Record

Venue	Scientific Reports
Published	2019
Work type	article
Authors	Dong Wook Kim, Sanghoon Lee, Sunmo Kwon, Woong Nam, In–Ho Cha, Hyung Jun Kim
Citations	320 (OpenAlex, at import time)
References	29
Model or method	DeepSurv
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41598-019-43372-7
Open access	gold
Keywords	oral cancer, DeepSurv, survival prediction, c-index, deep learning

Importance rating

4 / 10

real but narrower advance

Better survival prediction could guide oral cancer treatment choices; evidence from a single 255-patient retrospective cohort, a real but narrow advance.

Basis: stated. The source itself states the scale. Stated scale: 255 retrospective oral SCC patients. Method and limits.

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