International journal of radiation oncology, biology, physics
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Int. J. Radiat. Oncol. Biol. Phys. · Mar 2014
Multicenter Study Comparative StudySupport vector machine-based prediction of local tumor control after stereotactic body radiation therapy for early-stage non-small cell lung cancer.
Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control. ⋯ These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP modeling are expected through a nonlinear combination of multiple features, eventually helping in the task of personalized treatment planning.