Assessing the completeness of reporting in imaging studies using artificial neural network models for cancer diagnosis: Adherence to the TRIPOD-AI guideline

The reproducibility of radiological studies using ANN-based models to screen or characterise cancer is limited by suboptimal reporting practices. Potential measures to support more complete reporting include making adherence to appropriate reporting guidelines a condition of manuscript submission and mandating code and data sharing practices. Furthermore, stronger emphasis on reporting clinically relevant outcome measures (as opposed to just statistical measures of model performance) would greatly support decision-making with respect to implementation of study findings into clinical practice.