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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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Tumor Progression02:07

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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Related Experiment Video

Updated: May 5, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

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A Machine Learning Framework for Prognostic Modeling in Stage III Colon Cancer.

Rümeysa Sungur1, Selin Aktürk Esen2, Hilal Arslan3

  • 1Department of Internal Medicine, Ankara Bilkent City Hospital, 06800 Ankara, Turkey.

Journal of Clinical Medicine
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

Older age, comorbidities, and advanced disease stage predict worse survival in stage III colon cancer patients. Machine learning models effectively identified prognostic factors and improved survival prediction over traditional methods.

Keywords:
artificial intelligencecolon cancermachine learningprognosisrisk factorsstage III colon cancersurvival analysis

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Stage III colon cancer presents a significant challenge in predicting patient outcomes.
  • Identifying reliable prognostic factors is crucial for effective treatment planning and patient management.

Purpose of the Study:

  • To evaluate overall survival in stage III colon cancer patients.
  • To identify clinical, pathological, and demographic factors associated with survival.
  • To compare the predictive performance of machine learning algorithms against traditional statistical methods.

Main Methods:

  • Retrospective analysis of 452 stage III colon cancer patients.
  • Survival analysis using Kaplan-Meier and log-rank tests.
  • Prognostic factor identification and prediction using machine learning (coarse trees, bagged trees, SVM, KNN) and explainable AI (SHAP).

Main Results:

  • Older age, ECOG performance score ≥ 2, stage IIIC, N2 lymph node metastasis, and comorbidities (especially diabetes) were linked to poorer survival (p < 0.05).
  • Machine learning identified positive surgical margins, rash, mucositis, thrombocytopenia, chemotherapy cycles, tumor subtype, diarrhea, age, and anemia as key prognostic factors.
  • Ensemble machine learning models (coarse tree, bagged trees) achieved higher accuracy (87%) in predicting mortality and recurrence compared to traditional methods.

Conclusions:

  • Key prognostic factors influencing survival in stage III colon cancer were identified.
  • Machine learning approaches, integrating clinical and treatment data, enhance prognostic accuracy and support clinical decision-making.
  • Findings support the utility of AI in risk stratification for personalized colon cancer treatment.