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Purpose: Numerous publications during the COVID-19 pandemic recommended the use of hypofractionated radiation therapy. This project assessed aggregate changes in the quality of the evidence supporting these schedules to establish a comprehensive evidence base for future reference and highlight aspects for future study., Methods and Materials: Based on a systematic review of published recommendations related to dose fractionation during the COVID-19 pandemic, 20 expert panelists assigned to 14 disease groups named and graded the highest quality of evidence schedule(s) used routinely for each condition and also graded all COVID-era recommended schedules. The American Society for Radiation Oncology quality of evidence criteria were used to rank the schedules. Process-related statistics and changes in distributions of quality ratings of the highest-rated versus recommended COVID-19 era schedules were described by disease groups and for specific clinical scenarios., Results: From January to May 2020 there were 54 relevant publications, including 233 recommended COVID-19-adapted dose fractionations. For site-specific curative and site-specific palliative schedules, there was a significant shift from established higher-quality evidence to lower-quality evidence and expert opinions for the recommended schedules (P = .022 and P < .001, respectively). For curative-intent schedules, the distribution of quality scores was essentially reversed (highest levels of evidence "pre-COVID" vs "in-COVID": high quality, 51.4% vs 4.8%; expert opinion, 5.6% vs 49.3%), although there was variation in the magnitude of shifts between disease sites and among specific indications., Conclusions: A large number of publications recommended hypofractionated radiation therapy schedules across numerous major disease sites during the COVID-19 pandemic, which were supported by a lower quality of evidence than the highest-quality routinely used dose fractionation schedules. This work provides an evidence-based assessment of these potentially practice-changing recommendations and informs individualized decision-making and counseling of patients. These data could also be used to support radiation therapy practices in the event of second waves or surges of the pandemic in new regions of the world., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Study Type – Prognosis (risk model) Level of Evidence 2a OBJECTIVE To design a decision-support tool to facilitate evidence-based treatment decisions in clinically localized prostate cancer, as individualized risk assessment and shared decision-making can decrease distress and decisional regret in patients with prostate cancer, but current individual models vary or only predict one outcome of interest. METHODS We searched Medline for previous reports and identified peer-reviewed articles providing pretreatment predictive models that estimated pathological stage and treatment outcomes in men with biopsy-confirmed, clinical T1-3 prostate cancer. Each model was entered into a spreadsheet to provide calculated estimates of extracapsular extension (ECE), seminal vesicle invasion (SVI), and lymph node involvement (LNI). Estimates of the prostate-specific antigen (PSA) outcome after radical prostatectomy (RP) or radiotherapy (RT), and clinical outcomes after RT, were also entered. The data are available at . RESULTS Entering a patient’s 2002 clinical T stage, Gleason score and pretreatment PSA level, and details from core biopsy findings, into the CaP Calculator provides estimates from predictive models of pathological extent of disease, four models for ECE, four for SVI and eight for LNI. The 5-year estimates of PSA relapse-free survival after RT and 10-year estimates after RP were available. A printout can be generated with individualized results for clinicians to review with each patient. CONCLUSIONS The CaP Calculator is a free, online ‘clearing house’ of several predictive models for prostate cancer, available in an accessible, user-friendly format. With further development and testing with patients, the CaP Calculator might be a useful decision-support tool to help doctors promote evidence-based shared decision-making in prostate cancer. [ABSTRACT FROM AUTHOR]