Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)BeigetragenBegutachtung

Beitragende

  • Fernanda Villegas - , Karolinska Institutet (Autor:in)
  • Riccardo Dal Bello - , Universität Zürich (Autor:in)
  • Emilie Alvarez-Andres - , Klinik und Poliklinik für Strahlentherapie und Radioonkologie, OncoRay - Nationales Zentrum für Strahlenforschung in der Onkologie (Autor:in)
  • Jennifer Dhont - , Université libre de Bruxelles (ULB) (Autor:in)
  • Tomas Janssen - , Netherlands Cancer Institute (Autor:in)
  • Lisa Milan - , Ente Ospedaliero Cantonale (EOC) (Autor:in)
  • Charlotte Robert - , Institut Gustave Roussy (Autor:in)
  • Ghizela Ana Maria Salagean - , Babes-Bolyai University, TopMed Medical Centre (Autor:in)
  • Natalia Tejedor - , Hospital de la Santa creu i Sant Pau (Autor:in)
  • Petra Trnková - , Medizinische Universität Wien (Autor:in)
  • Marco Fusella - , Abano Terme Hospital (Autor:in)
  • Lorenzo Placidi - , Fondazione Policlinico Universitario Agostino Gemelli IRCCS (Autor:in)
  • Davide Cusumano - , Mater Olbia Hospital (MOH) (Autor:in)

Abstract

Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.

Details

OriginalspracheEnglisch
Aufsatznummer110387
FachzeitschriftRadiotherapy and oncology
Jahrgang198
PublikationsstatusVeröffentlicht - Sept. 2024
Peer-Review-StatusJa

Externe IDs

PubMed 38885905

Schlagworte

Schlagwörter

  • Artificial intelligence, Clinical implementation, Deep learning, MR-only planning, MR-only radiotherapy, Synthetic CT