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Introduction: High-risk prostate cancer is associated with poorer overall survival (OS) and biochemical control compared to more favorable risk groups. External beam radiation therapy (EBRT) is widely used; however, outcomes data are limited with respect to time elapsed between diagnosis and initiation of EBRT. Methods: The National Cancer Database was queried from 2004 to 2015 for patients diagnosed with high-risk adenocarcinoma of the prostate who received androgen deprivation therapy (ADT) and definitive EBRT. Logistic regression was utilized to determine covariates associated with missing EBRT treatments. OS was analyzed using multivariate cox proportional hazards models and propensity score matching. Results: 9,610 patients met inclusion criteria with median follow-up of 40.6 months and median age of 72 years. Median PSA was 8.7 and median EBRT dose was 78 Gy. ADT was initiated at a median of 36 days and EBRT at a median of 63 days post-diagnosis. Median number of prolonged treatment days was 2.2. Black race (OR: 1.40; p 74 days (HR: 1.20; p = 0.01) and prolonged treatment>3 days of EBRT (HR: 1.26; p = 0.005) were associated with an increased hazard of death. The 5-year OS was 79.6% and 82.9% for patients with prolonged treatment of 3 days or more of EBRT and those missing 3 days or less, respectively (p = 0.0006). Conclusion: In this hypothesis-generating study, prolonged treatment delays and missing three or more EBRT treatments was associated with poorer OS in patients with high-risk adenocarcinoma of the prostate.

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. Keywords: Radiation esophagitis, Machine learning, Non-small cell lung cancer, Chemoradiation, Radiation-induced toxicity, Intensity-modulated radiation therapy, Proton beam therapy