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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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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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Receiver Operating Characteristic Plot01:15

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Updated: Jul 4, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A

Gohar Mohammadi1, Mehdi Azizmohammad Looha2, Mohammad Amin Pourhoseingholi3

  • 1Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Asian Pacific Journal of Cancer Prevention : APJCP
|January 29, 2024
PubMed
Summary

Machine learning models accurately predict colorectal cancer (CRC) survival using key factors like diagnosis time and tumor characteristics. The Naïve Bayes model shows optimal efficacy for mortality prediction in CRC patients.

Keywords:
Data miningFeature selectionMachine Learning Algorithmscolorectal cancermortality prediction

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide.
  • Accurate prediction of CRC patient survival is crucial for effective treatment planning.
  • Machine learning (ML) offers promising tools for enhancing prognostic accuracy in oncology.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting survival outcomes in colorectal cancer (CRC) patients.
  • To identify key clinical and demographic predictors of CRC survival.
  • To compare the performance of various ML algorithms in CRC survival prediction.

Main Methods:

  • Retrospective analysis of 1853 CRC patients from tertiary hospitals in Iran.
  • Development and evaluation of six ML models: logistic regression, Naïve Bayes, SVM, NN, DT, and LGBM.
  • Feature selection using Random Forest and performance assessment via 10-fold cross-validation and AUC.

Main Results:

  • Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type were identified as significant survival predictors.
  • Naïve Bayes (NB) and Light Gradient Boosting Machine (LGBM) models achieved the highest predictive accuracy with an AUC of 0.70.
  • The NB model demonstrated optimal mortality prediction with balanced sensitivity and specificity.

Conclusions:

  • Clinical variables like diagnosis time, age, and tumor characteristics are vital for predicting CRC survival.
  • The Naïve Bayes model shows high efficacy for predicting mortality in CRC patients.
  • Recommendations include early diagnosis, integrated digital health records, and incorporating prognostic variables into treatment guidelines.