Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non-Small Cell Lung Cancer.

Affiliation

University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio. [Email] [Email]

Abstract

Radiation pneumonitis is an important adverse event in patients with non-small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2.

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