Predicting radiation pneumonitis in locally advanced stage II–III non-small cell lung cancer using machine learning

Keyphrases

• Non-small Cell Lung Cancer (NSCLC) 100%
• Locally Advanced Stage 100%
• Machine Learning 100%
• Radiation Pneumonitis 100%
• Lung 72%
• Random Forest 27%
• Multivariate Analysis 18%
• Esophagus 18%
• Univariate Analysis 18%
• Locally Advanced Non-small Cell Lung Cancer (LA-NSCLC) 18%
• Pack-years 18%
• Clinical Features 9%
• Dose-limiting Toxicity 9%
• Significant Predictors 9%
• Machine Learning Techniques 9%
• Non-small Cell Lung Cancer Patients 9%
• Feature Selection 9%
• Machine Learning Algorithms 9%
• Radiotherapy Dose 9%
• Definitive Chemoradiation 9%
• Dosimetric Constraints 9%
• New Predictor 9%
• Decision Stump 9%

Medicine and Dentistry

• Non Small Cell Lung Cancer 100%
• Radiation Pneumonia 100%
• Lung 72%
• Esophagus 18%
• Univariate Analysis 18%
• Multivariate Analysis 18%
• Clinical Feature 9%
• Sensitivity and Specificity 9%
• Chemoradiotherapy 9%
• Feature Extraction 9%
• Radiation Therapy 9%

Immunology and Microbiology

• Pneumonitis 100%
• Random Forest 27%
• Esophagus 18%
• Feature Extraction 9%