Advanced Computational Modeling and Machine Learning for Risk Stratification, Treatment Optimization, and Prognostic Forecasting in Appendiceal Neoplasms.

ABSTRACT:

Background

Appendiceal neoplasms account for less than 1% of gastrointestinal cancers but are increasing in incidence worldwide. Their marked histological variations and differences create multiple challenges for prognosis and management planning, as current staging systems are limited in certain aspects for capturing the entire disease complexity.

Methods

We synthesized data from 18 large observational studies, including 67,001 patients diagnosed between 1973 and 2024. Using advanced computational modeling, we combined multiple statistical methods and machine learning techniques to improve risk stratification, survival prediction, treatment optimization, and forecasting. A novel overlap-aware weighting methodology was applied to prevent double-counting across overlapping re

SUBMITTER: Alnajjar JS 

PROVIDER: S-EPMC12692461 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature