Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches
Journal Title: Journal of Radiation Oncology Informatics - Year 2009, Vol 1, Issue 1
Abstract
Purpose: 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. Materials and methods: 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 patient’s 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 dose-response explorer system (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. These tools are demonstrated on an institutional non-small cell lung carcinoma (NSCLC) dataset of patients who received radiotherapy. Results: Our methods were applied to an NSCLC dataset that consists of 209 patients’ information, each having 160 variables. Using several feature selection methods, relevant features were searched. Subsequently, with the selected features, various classification algorithms were tested. Through these experiments, we showed the usefulness of machine learning methods in the analysis of radiation oncology dataset. Conclusions: We have presented an open-source software tool and several machine learning algorithms for analyzing radiotherapy outcomes. We demonstrated the tool on a lung cancer patient dataset. We believe that the improved tool will provide radiation oncology modelers with new means to analyze radiation response data.
Authors and Affiliations
Jung Oh, Aditya Apte, Rawan Al-Lozi, Jeffrey Bradley, Issam El Naqa
Keywords
Related Articles
- EP ID EP83437
- DOI 10.5166/jroi-1-1-5
- Views 53
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Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches
Purpose: 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...