Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation. (May 2020)

• Record Type: Journal Article
• Authors: Luna, José Marcio; Chao, Hann-Hsiang; Shinohara, Russel T.; Ungar, Lyle H.; Cengel, Keith A.; Pryma, Daniel A.; Chinniah, Chidambaram; Berman, Abigail T.; Katz, Sharyn I.; Kontos, Despina; Simone, Charles B.; Diffenderfer, Eric S.
• Is Part Of: Clinical and translational radiation oncology. Volume 22(2020)
• Page Start: 69
• Page End: 75
• Publication Date: 2020-05
• Subjects: Radiation esophagitis -- Machine learning -- Non-small cell lung cancer -- Chemoradiation -- Radiation-induced toxicity -- Intensity-modulated radiation therapy -- Proton beam therapy; Cancer -- Radiotherapy -- Periodicals; Oncology -- Periodicals; Cancer -- Radiotherapy; Oncology; Radiation Oncology; Neoplasms -- radiotherapy; Translational Medical Research; Periodicals; Electronic journals; Periodicals; 616.9940642
• DOI: 10.1016/j.ctro.2020.03.007
• Languages: English
• ISSNs: 2405-6308

Abstract

Highlights: A large cohort to predict radiation esophagitis in lung cancer patients was used. Modern machine learning models were implemented to predict radiation esophagitis. Previously published predictors of grade ≥ 3 radiation esophagitis may be unreliable. Abstract: Background and Purpose: Radiation esophagitis is a clinically important toxicity seen with treatment for locally-advanced non-small cell lung cancer. There is considerable disagreement among prior studies in identifying predictors of radiation esophagitis. We apply machine learning algorithms to identify factors contributing to the development of radiation esophagitis to uncover previously unidentified criteria and more robust dosimetric factors. Materials and Methods: We used machine learning approaches to identify predictors of grade ≥ 3 radiation esophagitis in a cohort of 202 consecutive locally-advanced non-small cell lung cancer patients treated with definitive chemoradiation from 2008 to 2016. We evaluated 35 clinical features per patient grouped into risk factors, comorbidities, imaging, stage, histology, radiotherapy, chemotherapy and dosimetry. Univariate and multivariate analyses were performed using a panel of 11 machine learning algorithms combined with predictive power assessments. Results: All patients were treated to a median dose of 66.6 Gy at 1.8 Gy per fraction using photon (89.6%) and proton (10.4%) beam therapy, most often with concurrent chemotherapy (86.6%). 11.4% of patients developed grade ≥ 3 radiation esophagitis. On univariate analysis, no individual feature was found to predict radiation esophagitis (AUC range 0.45–0.55, p ≥ 0.07). In multivariate analysis, all machine learning algorithms exhibited poor predictive performance (AUC range 0.46–0.56, p ≥ 0.07). Conclusions: Contemporary machine learning algorithms applied to our modern, relatively large institutional cohort could not identify any reliable predictors of grade ≥ 3 radiation esophagitis. Additional patients are needed, and novel patient-specific and treatment characteristics should be investigated to develop clinically meaningful methods to mitigate this survival altering toxicity.