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Multi-objective radiomics model for predicting distant failure in lung SBRT.

Zhiguo Zhou1, Michael Folkert1, Puneeth Iyengar1

  • 1Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States of America.

Physics in Medicine and Biology
|May 9, 2017
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A new multi-objective radiomics model improves prediction of distant failure after stereotactic body radiation therapy (SBRT) for lung cancer. This approach enhances sensitivity while maintaining specificity, aiding treatment decisions for high-risk patients.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Stereotactic body radiation therapy (SBRT) offers high local control for early-stage non-small cell lung cancer (NSCLC).
  • Distant failure remains a significant challenge after SBRT, necessitating improved patient stratification for adjuvant therapies.
  • Radiomics, extracting quantitative features from medical images, shows promise for predicting treatment outcomes.

Purpose of the Study:

  • To develop a novel radiomics model for accurately stratifying patients at high risk of distant failure post-SBRT.
  • To address limitations of single-objective models in radiomics, particularly with imbalanced datasets.
  • To introduce a multi-objective optimization approach considering both sensitivity and specificity.

Main Methods:

  • Development of a multi-objective radiomics model optimizing sensitivity and specificity simultaneously.
  • Implementation of an iterative multi-objective immune algorithm (IMIA) for model optimization.
  • Comparison of the multi-objective model against single-objective models using metrics like Area Under the Curve (AUC).

Main Results:

  • The proposed multi-objective radiomics model demonstrated superior sensitivity compared to single-objective models.
  • Specificity and AUC were maintained at comparable levels to single-objective models.
  • The iterative multi-objective immune algorithm (IMIA) outperformed traditional immune-inspired algorithms in optimization.

Conclusions:

  • Multi-objective radiomics models offer enhanced predictive capabilities for distant failure after SBRT.
  • The IMIA provides a robust framework for developing accurate and reliable radiomics prediction models.
  • This strategy can aid in identifying high-risk NSCLC patients who may benefit from additional systemic therapy.