LAUSR

Clinical, pathological and molecular information combined with cancer stage in prognostication algorithms can offer more personalized estimates of survival, which may guide treatment choices. Our aim was to evaluate the quality of prognostication tools in esophageal cancer. We systematically searched MEDLINE & Embase from 2005- 2017 for studies reporting development or validation of models predicting long-term survival in esophageal cancer. We evaluated tools using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies guidelines and the American Joint Committee on Cancer acceptance criteria for risk models. We identified 16 prognostication tools for patients treated with curative intent and one for patients with metastatic disease. These tools frequently excluded adenocarcinoma, contained outdated data and were developed with a limited sample size. Nine tools were developed in China for squamous cell cancer, and 11 used data on patients diagnosed prior to 2010. The majority of tools excluded key prognostic factors such as age and sex. Tumor stage and grade were the most commonly, but not universally, included factors. Twelve tools were designed to predict overall survival; five predicted cancer-specific survival. Bootstrap internal validation was performed for most tools; c-statistics ranged from 0.63–0.77 and graphically evaluated calibration was ‘good’. Five tools were externally validated; c-statistics ranged from 0.70–0.77. Existing tools cannot be confidently used for esophageal cancer prognostication in current clinical practice. Better quality tools may help to more individually and accurately estimate disease course, select further treatments, and risk-stratify for future clinical trials. All authors have declared no conflicts of interest.