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Clinical factors, including T-stage, Gleason score, and baseline prostate-specific antigen, are used to stratify patients with prostate cancer (PCa) into risk groups. This provides prognostic information for a heterogeneous disease such as PCa and guides treatment selection. In this article, we hypothesize that nonclinical factors may also impact treatment selection and their adherence to treatment guidelines. A total of 552 patients with intermediate- and high-risk PCa treated with definitive radiation with or without androgen deprivation therapy (ADT) between 2010 and 2017 were identified from 34 medical centers within the Veterans Health Administration. Medical charts were manually reviewed, and details regarding each patient's clinical history and treatment were extracted. Support Vector Machine and Random forest-based classification was used to identify clinical and nonclinical predictors of adherence to the treatment guidelines from the National Comprehensive Cancer Network (NCCN). We created models for predicting both initial treatment intent and treatment alterations. Our results demonstrate that besides clinical factors, the center in which the patient was treated (nonclinical factor) played a significant role in adherence to NCCN guidelines. Furthermore, the treatment center served as an important predictor to decide on whether or not to prescribe ADT; however, it was not associated with ADT duration and weakly associated with treatment alterations. Such center-bias motivates further investigation on details of center-specific barriers to both NCCN guideline adherence and on oncological outcomes. In addition, we demonstrate that publicly available data sets, for example, that from Surveillance, Epidemiology, and End Results (SEERs), may not be well equipped to build such predictive models on treatment plans.

Background: After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures., Methods: We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making., Findings: Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance., Interpretation: States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic., Competing Interests: The authors have declared that no competing interests exist.