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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Related Experiment Video

Updated: Jul 17, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Published on: September 27, 2024

Risk Stratification to Optimize Colorectal Cancer Screening: Development and Validation of a Decision-Tree Model for

María Capilla-Lozano1,2, Carmen Quiñones-Torrelo3, Lucas Sebastián-Peris4

  • 1Digestive Disease Department, Clinic University Hospital of Valencia, INCLIVA Health Research Institute, Valencia, Spain.

Digestive Endoscopy : Official Journal of the Japan Gastroenterological Endoscopy Society
|July 16, 2026
PubMed
Summary

A new decision-tree model effectively stratifies colorectal cancer risk after a positive fecal immunochemical test. This approach prioritizes colonoscopy for high-risk individuals, optimizing resource allocation in screening programs.

Keywords:
colorectal neoplasmsearly detection of cancerfecal occult blood testmass screeningrisk assessment

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Area of Science:

  • Gastroenterology
  • Oncology
  • Public Health

Background:

  • Fecal immunochemical test (FIT) is effective for colorectal cancer screening.
  • Colonoscopy demand often exceeds endoscopy capacity, leading to delays.
  • Delays in colonoscopy after a positive FIT reduce screening effectiveness.

Purpose of the Study:

  • To develop and validate a decision-tree model for stratifying colorectal cancer risk.
  • To prioritize colonoscopy for individuals at highest risk following a positive FIT.
  • To optimize the use of endoscopic resources in colorectal cancer screening programs.

Main Methods:

  • Prospective study within a Spanish regional population-based screening program (2021-2024).
  • Participants with positive FIT were randomly assigned to derivation (65%) and validation (35%) cohorts.
  • Fecal hemoglobin concentration, clinical, and laboratory data were used to develop the decision-tree model.

Main Results:

  • Colorectal cancer detected in 5.5% of 1773 participants; advanced-stage disease in 1.5%.
  • The model stratified participants into low, intermediate, high, and very high-risk groups.
  • The very high-risk group (FIT > 100 μg/g and age > 65) had a colorectal cancer prevalence of 21.8% and advanced-stage prevalence of 7.6%.

Conclusions:

  • A decision-tree model effectively stratifies colorectal cancer risk.
  • Prioritizing colonoscopy for the very high-risk group significantly lowers the number needed to scope.
  • The model optimizes endoscopic resource allocation in colorectal cancer screening programs.