Oncotarget

Predicting Colorectal Cancer Survival: How Machine Learning Combines Clinical and Biological Clues

Mar 25, 2026
A deep dive into using machine learning to predict colorectal cancer survival by merging clinical records with molecular markers. They cover which data sources and preprocessing steps matter and how features like genes and non-coding RNAs are identified. The conversation reviews model choices, accuracy results, and the challenges of validation and population bias.
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INSIGHT

Integrating Clinical And Molecular Data Boosts Prediction

  • Combining clinical features like pathological stage, age, and lymph node status with molecular markers improves survival prediction for colorectal cancer.
  • The study used TCGA data from 545 patients and preprocessed clinical and biological features, including differential expression and CRNA network analysis.
ADVICE

Address Missing Data With Multiple Strategies

  • Handle missing clinical and demographic data carefully because exclusions or imputation change model size and performance.
  • The study tested three cases: filter missing core features, exclude demographic-missing patients, or impute with most frequent category.
INSIGHT

Lasso Plus SHAP Creates Interpretable Feature Pipeline

  • The modeling pipeline combined lasso feature selection, SHAP interpretability, and ensemble classifiers like SVM, Random Forest, AdaBoost, and stacking.
  • Lasso ranked features; SHAP explained each feature's impact before training multiple classifiers for robust prediction.
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