The Impact of Predictive Modeling in Improving the Prognosis of Patients with Colorectal Cancer

EasyChair Preprint 13567

Abstract

Colorectal cancer is a significant global health burden, with prognosis being a critical factor in patient outcomes. Predictive modeling has emerged as a valuable tool in healthcare, offering the potential to enhance prognostic accuracy and guide treatment decisions. This abstract provides an overview of the impact of predictive modeling in improving the prognosis of patients with colorectal cancer.

The utilization of predictive modeling in colorectal cancer involves the collection and integration of vast amounts of patient data, including clinical, genomic, and imaging data. By identifying relevant predictive factors, such as tumor characteristics, patient demographics, and genetic markers, predictive models can be developed to evaluate prognosis. These models enable early detection of colorectal cancer, personalized treatment planning, and identification of high-risk patients.

The impact of predictive modeling in improving prognosis is significant. Early detection facilitates timely intervention and improves patient outcomes by enabling early-stage diagnosis. Predictive models aid in individualized treatment planning, considering factors such as tumor stage, genetic mutations, and patient comorbidities. This personalized approach enhances treatment efficacy and minimizes unnecessary interventions.

Keyphrases: Clinical Decision Support, Prognosis evaluation, Treatment Selection, personalized medicine, predictive modeling, risk stratification

@booklet{EasyChair:13567,
  author    = {Edwin Frank},
  title     = {The Impact of Predictive Modeling in Improving the Prognosis of Patients with Colorectal Cancer},
  howpublished = {EasyChair Preprint 13567},
  year      = {EasyChair, 2024}}