Multicentre prospective risk analysis of a fully automated radiotherapy workflow

Geert De Kerf*, Ana Barragan-Montero, Charlotte L. Brouwer, Pietro Pisciotta, Marie-Claude Biston, Marco Fusella, Geoffroy Herbin, Esther Kneepkens, Livia Marrazzo, Joshua Mason, Camila Panduro Nielsen, Koen Snijders, Stephanie Tanadini-Lang, Aude Vaandering, Tomas M. Janssen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background and Purpose: Fully automated workflows (FAWs) for radiotherapy treatment preparation are feasible, but remain underutilized in clinical settings. A multicentre prospective risk analysis was conducted to support centres in managing FAW-related risks and to identify workflow steps needing improvement. Material and Methods: Eight European radiotherapy centres performed a failure mode and effect analysis (FMEA) on a hypothetical FAW, with a manual review step at the end. Centres assessed occurrence, severity and detectability of provided, or newly added, failure modes to obtain a risk score. Quantitative analysis was performed on curated data, while qualitative analysis summarized free text comments. Results: Manual review and auto-segmentation were identified as the highest-risk steps and the highest scoring failure modes were associated with inadequate manual review (high detectability and severity score), incorrect (i.e. outside of intended use) application of the FAW (high severity score) and protocol violations during patient preparation (high occurrence score). The qualitative analysis highlighted amongst others the risk of deviation from protocol and the difficulty for manual review to recognize automation errors. The risk associated with the technical parts of the workflow was considered low. Conclusions: The FMEA analysis highlighted that points where people interact with the FAW were considered higher risk than lack of trust in the FAW itself. Major concerns were the ability of people to correctly judge output in case of low generalizability and increasing skill degradation. Consequently, educational programs and interpretative tools are essential prerequisites for widespread clinical application of FAWs.
Original languageEnglish
Article number100765
Number of pages7
JournalPhysics & Imaging in Radiation Oncology
Volume34
DOIs
Publication statusPublished - 1 Apr 2025

Keywords

  • Automation
  • FMEA
  • Auto-contouring
  • Auto-planning
  • Computer assisted radiotherapy planning
  • ARTIFICIAL-INTELLIGENCE
  • IMPLEMENTATION

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