https://www.selleckchem.com/products/nik-smi1.html Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts. To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial. The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial. The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests. A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFodels. Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models. The purpose of this project was to assess factors that may influence variability in the pre-treatment kilovoltage cone beam computed tomography (kV CBCT) image matching process for lung stereotactic body radiation therapy (SBRT). Pre-treatment CBCT and planning CT data sets of previously-treated lung SBRT patients were gathered and anonymized from four radiotherapy centers in Alberta. Eight radiation therapists (RTTs) and four radiation oncologists (ROs) were recruited from the same fo