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September 10, 2025

Prognostic Factors in Survivors of Gastric Cancer: A Comparative Study of Cox Proportional Hazards Model and Machine Learning Approaches Using Korean National Cohort Data

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The study identifies remnant stomach and cancer stage as critical factors affecting recurrence and mortality.
All models, including machine learning techniques, achieved a concordance index over 0.7, indicating reliable predictions.
Survivors of gastric cancer in Korea showed significant outcomes based on various prognostic indicators over a median 6.19-year follow-up.
Machine learning offers advantages in flexibility when traditional statistical assumptions may be violated in cancer prognosis.

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Implication

Retrospective cohort analysis identifies key prognostic factors in gastric cancer survivors, suggesting machine learning approaches are comparable to traditional methods.

Cite This Study

Lee et al. (2025) studied this question.

synapsesocial.com/papers/68c1c32e54b1d3bfb60f1672 https://doi.org/10.21203/rs.3.rs-7022055/v1

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Implication

Retrospective cohort analysis identifies key prognostic factors in gastric cancer survivors, suggesting machine learning approaches are comparable to traditional methods.

Prognostic Factors in Survivors of Gastric Cancer: A Comparative Study of Cox Proportional Hazards Model and Machine Learning Approaches Using Korean National Cohort Data

Key Points

Discussion

Implication

Cite This Study

Prognostic Factors in Survivors of Gastric Cancer: A Comparative Study of Cox Proportional Hazards Model and Machine Learning Approaches Using Korean National Cohort Data

Key Points

Discussion

Implication

Cite This Study

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