ArticleAsian Pacific journal of cancer prevention : APJCP2025
Analyzing Secondary Cancer Risk: A Machine Learning Approach.
Article in Asian Pacific journal of cancer prevention : APJCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study.Bioengineering (Basel, Switzerland) · 2026Article
- Breast cancer radiotherapy and the risk of lung injury: Advances and perspectives.Advances in radiotherapy & nuclear medicine · 2026Article
- Machine learning for early prediction of secondary cancer after radiotherapy.Scientific reports · 2025Article
- Predicting cancer mortality using machine learning methods: a global vs. Iran analysis.BMC cancer · 2025Article
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Authors and funding
5 authors.
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Abstract
objectiveAddressing 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.
methodsMachine 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.
resultThe 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.
conclusionThe 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.
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