Hatamabadi Farahani Erfan, Sadeghi Hossein, Seif Fatemeh, Azad Marzabadi Mahdi, Rezaee Reza
Department of Physics, Faculty of Sciences, Arak University, Arak, Iran.
Department of Radiotherapy and Medical Physics, Arak University of Medical Sciences and Khansari Hospital, Arak, Iran.
Asian Pac J Cancer Prev. 2025 Jan 1;26(1):239-248. doi: 10.31557/APJCP.2025.26.1.239.
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.
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.
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.
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.
通过及时诊断和治疗应对不断上升的癌症发病率至关重要。此外，癌症幸存者需要了解患继发性癌症（SC）的潜在风险，这可能受到多种因素影响，包括治疗方式、生活方式选择以及吸烟和饮酒等习惯。本研究旨在使用线性回归模型建立剂量与SC风险之间的新型关系，比较肺癌、结肠癌和乳腺癌的不同预测方法。
机器学习（ML）模型已证明其在根据器官中的有效剂量预测SC风险可能性方面的有用性。线性回归分析是一种广泛用于检查预测变量与连续反应之间关系的技术，特别是在样本量有限的情况下。本研究采用线性回归模型分析有效剂量与SC风险之间的关系，比较肺癌、结肠癌和乳腺癌的不同预测方法。
结果表明，SC风险随器官中的有效剂量增加而增加。线性回归模型提供的系数反映了特定器官的辐射敏感性，证明了该模型在基于剂量预测SC风险方面的有效性。
该研究强调了使用线性回归模型根据器官中的有效剂量预测SC风险的重要性。研究结果强调了在SC风险预测中考虑特定器官辐射敏感性的重要性，这有助于更好地理解和管理癌症幸存者的长期健康。
