ESTRO 2021 Abstract Book

S363

ESTRO 2021

Figure 1. PROBAST risk of bias assessment Figure 1 reports the ROB assessed using the tailored PROBAST items for the 20 analysed articles. Analysis domain is the major cause of ROB in AI-based treatment planning studies (50% of the studies). In these studies, the steps on how to deal with suboptimal plans is either under-reported or unclear. Adherence to the tailored TRIPOD items was poor in blinding (15%), sample size (10%), and suboptimal plan reporting (15%). Furthermore, given the statistical complexity of model development/validation, the statistical methods used are neglected, resulting in a high ROB. Only 20% of the studies provided supplementary material such as a full model description or code, the statistical analysis or the study data sets (Figure 2).

Figure 2.Adherence to individual TRIPOD items

Conclusion Most PROBAST and TRIPOD criteria can be used to score articles on AI in radiotherapy treatment planning. Using these items, the articles show a high risk of bias and underreport several important study aspects as judged by the TRIPOD criteria. Similar research is expected to increase the consistency and transparency of the published evidence base while also reducing study waste.

Symposium: The role of deep learning in CBCT-based workflows

SP-0475 Deep learning-based CBCT image intensity correction for adaptive radiotherapy M. Maspero 1 1 UMC Utrecht, Radiotherapy, Utrecht, The Netherlands