Summary of Deep Neural Networks For Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery, by Yuhan Zheng et al.
Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery
by Yuhan Zheng, Jessie A Elliott, John V Reynolds, Sheraz R Markar, Bartłomiej W. Papież, ENSURE study group
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates three machine learning models’ ability to predict disease-free survival (DFS) and overall survival (OS) in patients with esophageal cancer. The researchers use a large international dataset from the ENSURE study to evaluate the performance of Cox Proportional Hazards, DeepSurv, and DeepHit models. They find that post-operative pathologic features are more significant predictors of outcomes than clinical stage features. The results suggest that deep neural networks (DNNs) can be effective prognostic tools for improving predictive accuracy and providing personalized guidance for risk stratification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Esophageal cancer is a serious problem worldwide, with many patients not surviving after treatment. To make better decisions, doctors need to know which factors are most important in predicting how well a patient will do. This paper looks at three special computer programs that can help predict how long it takes for the disease to come back or if the patient will survive. The researchers use a big dataset from many countries and find that some things about the patient’s health after treatment are more important than others. They think these special computer programs could be useful tools to help doctors make better decisions. |
Keywords
» Artificial intelligence » Machine learning
