Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: May 1, 2024

Abstract

BACKGROUND AND PURPOSE: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers.

Authors

  • Coen Hurkmans
    Department of Radiation Oncology, Catharina Hospital, Eindhoven, The Netherlands. coen.hurkmans@catharinaziekenhuis.nl.
  • Jean-Emmanuel Bibault
    Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France; INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France. Electronic address: jean-emmanuel.bibault@aphp.fr.
  • Enrico Clementel
    European Organisation for the Research and Treatment of Cancer (EORTC), Brussels, Belgium.
  • Jennifer Dhont
    Maastro Clinic, Maastrict, the Netherlands.
  • Wouter van Elmpt
    Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands. Electronic address: wouter.vanelmpt@maastro.nl.
  • Georgios Kantidakis
    Mathematical Institute Leiden University, Niels Bohrweg 1, 2333 CA Leiden, Netherlands.
  • Nicolaus Andratschke
    Department of Radiation Oncology, University Medicine Rostock, Rostock, Germany. nicolaus.andratschke@usz.ch.

Keywords

External Resources

Popular Topics
Recent Journals