Analyzing Secondary Cancer Risk: A Machine Learning Approach.

Abstract

Objective: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors including treatment modalities, lifestyle choices, and habits such as smoking and alcohol consumption. This study aims to establish a novel relationship using linear regression models between dose and the risk of SC, comparing different prediction methods for lung, colon, and breast cancer.

Methods: Machine learning (ML) models have demonstrated their usefulness in forecasting the likelihood of SC risks based on effective doses in the organ. Linear regression analysis is a widely utilized technique for examining the relationship between predictor variables and continuous responses, particularly in scenarios with limited sample sizes. This study employs linear regression models to analyze the relationship between effective dose and the risk of SC, comparing different prediction methods across lung, colon, and breast cancer.

Result: The results indicate that the risk of SC increases with the effective dose in the organ. The linear regression model provides coefficients that mirror the radiation sensitivity of the specific organ, demonstrating the model's effectiveness in predicting SC risk based on dose.

Conclusion: The study highlights the significance of using linear regression models to predict the risk of SC based on effective doses in the organ. The findings underscore the importance of considering the radiation sensitivity of specific organs in SC risk prediction, which can aid in better understanding and managing the long-term health of cancer survivors.