Publications by authors named "Hongniu Wang"
Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine.
World J Gastrointest Oncol
December 2024
Article Synopsis
- Survival rates after surgery for gastric neuroendocrine neoplasms (g-NENs) are low, and traditional prognostic models like the CoxPH show limited ability to predict patient outcomes post-surgery.
- Machine learning techniques, particularly the random survival forest (RSF) model, can analyze complex data to improve predictions of survival outcomes.
- The study highlights that the RSF model, which uses the lymph node ratio (LNR), is more effective than CoxPH in predicting disease-specific survival in g-NEN patients and could lead to better personalized treatment strategies.
Article Synopsis
- A recent study by Hong developed an AI-driven prediction system to assess complications for patients undergoing laparoscopic radical gastrectomy for gastric cancer.
- This new system uses random forest models and incorporates data from various medical centers to improve prediction accuracy and patient management.
- The research emphasizes AI's role in clinical decision support and suggests potential for future studies to enhance AI applications in diagnosing and treating gastric cancer.