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

CONCLUSIONS: We highlight Random Forest as an accurate machine learning method to identify known and new predictors of symptomatic RP. Furthermore, this analysis confirms the importance of lung V20, lung mean and pack-year as predictors of RP while also introducing esophagus max as an important RP predictor.
PMID: 30935565 [PubMed - in process]

Source: Radiotherapy and Oncology : journal of the European Society for Therapeutic Radiology and Oncology - March 31, 2019 Category: Radiology Authors: Luna JM, Chao HH, Diffenderfer ES, Valdes G, Chinniah C, Ma G, Cengel KA, Solberg TD, Berman AT, Simone CB Tags: Radiother Oncol Source Type: research