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Erfan Hatamabadi Farahani1, Hossein Sadeghi1, Fatemeh Seif2

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The risk of secondary cancer (SC) increases with effective organ dose. Linear regression models effectively predict SC risk, considering organ radiation sensitivity for improved cancer survivor care.

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Area of Science:

  • Oncology
  • Medical Physics
  • Biostatistics

Background:

  • Rising cancer rates necessitate effective diagnosis and treatment strategies.
  • Cancer survivors face potential secondary cancer (SC) risks influenced by treatments and lifestyle.
  • Understanding SC risk factors is crucial for long-term survivor health management.

Purpose of the Study:

  • To establish a novel relationship between effective organ dose and SC risk using linear regression.
  • To compare different prediction methods for SC risk in lung, colon, and breast cancer.
  • To investigate the influence of effective dose on SC development in cancer survivors.

Main Methods:

  • Utilized linear regression models to analyze the dose-SC risk relationship.
  • Employed machine learning (ML) for forecasting SC likelihood based on effective organ doses.
  • Compared prediction methods across lung, colon, and breast cancer datasets.

Main Results:

  • A positive correlation was observed between effective organ dose and SC risk.
  • Linear regression model coefficients reflect organ-specific radiation sensitivity.
  • The model demonstrated effectiveness in predicting SC risk based on dose.

Conclusions:

  • Linear regression models are significant for predicting SC risk from effective organ doses.
  • Organ radiation sensitivity is a key factor in SC risk assessment.
  • Findings aid in better understanding and managing long-term health for cancer survivors.