Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

  • Jung Hun Oh Division of Bioinformatics and Outcomes Research, Department of Radiation Oncology, Washington University School of Medicine, MO 63110
  • Aditya Apte Division of Bioinformatics and Outcomes Research, Department of Radiation Oncology, Washington University School of Medicine, MO 63110
  • Rawan Al-Lozi Division of Bioinformatics and Outcomes Research, Department of Radiation Oncology, Washington University School of Medicine, MO 63110
  • Jeffrey Bradley Division of Bioinformatics and Outcomes Research, Department of Radiation Oncology, Washington University School of Medicine, MO 63110
  • Issam M. El Naqa Division of Bioinformatics and Outcomes Research, Department of Radiation Oncology, Washington University School of Medicine, MO 63110

Abstract

Radiation pneumonitis (RP) is a potentially fatal side effect arising in lung cancer patients who receive radiotherapy as part of their treatment. For the modeling of RP outcomes data, several predictive models based on traditional statistical methods and machine learning techniques have been reported. However, no guidance to variation in performance has been provided to date. In this study, we explore several machine learning algorithms for classification of RP data. The performance of these classification algorithms is investigated in conjunction with several feature selection strategies and the impact of the feature selection strategy on performance is further evaluated. The extracted features include patients demographic, clinical and pathological variables, treatment techniques, and dose-volume metrics. In conjunction, we have been developing an in-house Matlab-based open source software tool, called DREES, customized for modeling and exploring dose response in radiation oncology. This software has been upgraded with a popular classification algorithm called support vector machine (SVM), which seems to provide improved performance in our exploration analysis and has strong potential to strengthen the ability of radiotherapy modelers in analyzing radiotherapy outcomes data.
Published
27-09-2017
How to Cite
Oh, J., Apte, A., Al-Lozi, R., Bradley, J., & El Naqa, I. Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches. Journal of Radiation Oncology Informatics, 1(1), 30-43. https://doi.org/10.5166/jroi-1-1-5
Section
Articles