Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The
Anabel Franco-Moreno1,2, Elena Madroñal-Cerezo3,4, Cristina Lucía de Ancos-Aracil3,4
1Department of Internal Medicine, Hospital Universitario Infanta Leonor-Virgen de la Torre, 28031 Madrid, Spain.
Medicina (Kaunas, Lithuania)
|January 25, 2025
Summary
This study developed a machine learning model to predict hidden cancer risk in patients with venous thromboembolism (VTE). The model achieved high accuracy, aiding early cancer detection after VTE.
Area of Science:
- Oncology
- Hematology
- Medical Informatics
Background:
- Venous thromboembolism (VTE) can be an early indicator of undiagnosed cancer.
- Accurate risk stratification is crucial for timely cancer diagnosis in VTE patients.
Purpose of the Study:
- To develop and validate a machine learning (ML) predictive model for occult cancer risk following venous thromboembolism (VTE).
- To identify key clinical and laboratory predictors of hidden malignancy in VTE patients.
Main Methods:
- A case-control study nested within a prospective VTE registry.
- Utilized XGBoost, LightGBM, and CatBoost ML algorithms for model development.
- Performed both clinical and ML-driven feature selection on 121 variables.
Main Results:
- The CatBoost model demonstrated superior performance with an ROC-AUC of 0.86 in the test set.
- Key predictors included age, gender, blood pressure, D-dimer, hemoglobin, and platelet count.
- Achieved 62% sensitivity and 94% specificity for detecting hidden cancer.
Conclusions:
- This is the first ML-based risk score to identify occult cancer risk in VTE patients, showing high diagnostic accuracy.
- The model requires external validation due to potential biases and limitations in the current study.
- Clinical utility may be enhanced by addressing information bias and sample size limitations.
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