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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine learning computational model to predict lung cancer using electronic medical records.

Matanel Levi¹, Teddy Lazebnik², Shiri Kushnir³

¹Adelson School of Medicine, Ariel University, Ariel, Israel.

Cancer Epidemiology

|July 25, 2024

Summary

Machine learning models can modestly predict lung cancer (LC) risk using established factors like smoking history and BMI. These models show promise for improving early detection in lung cancer screening programs.

Keywords:

Artificial intelligence Lung cancer Machine learning Prediction Smoking

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Area of Science:

Oncology
Medical Informatics
Computational Biology

Background:

Lung cancer (LC) screening is crucial and relies on risk criteria or calculators.
Machine learning (ML) offers a novel approach to identify unique, hidden disease risk associations.

Purpose of the Study:

To develop and evaluate an ML model for predicting lung cancer risk.
To assess the model's performance using established risk factors.

Main Methods:

An ML model was developed using the TPOT tool, an ensemble of Random Forest and XGboost.
Data from 4076 patients (LC vs. controls) aged ≥35 years were analyzed.
Included factors: age, gender, BMI, smoking history, SES, COPD/emphysema/CB, ILD/PF, and family history.

Main Results:

The overall model achieved 71.2% accuracy, 69% sensitivity, and 74% PPV.
Smoking and never-smoking subgroups showed higher accuracy (74.8% and 73.0%, respectively).
Key predictors varied: COPD/emphysema/CB for smokers, BMI/age/SES for never-smokers.

Conclusions:

Established LC risk factors can be utilized in ML models for modest prediction.
Further research is necessary to validate and enhance these ML models for clinical application.