Consistency in contouring of organs at risk by artificial intelligence vs oncologists in head and neck cancer patients

Camilla Panduro Nielsen*, Ebbe Laugaard Lorenzen, Kenneth Jensen, Nis Sarup, Carsten Brink, Bob Smulders, Anne Ivalu Sander Holm, Eva Samsøe, Martin Skovmos Nielsen, Patrik Sibolt, Peter Sandegaard Skyt, Ulrik Vindelev Elstrøm, Jørgen Johansen, Ruta Zukauskaite, Jesper Grau Eriksen, Mohammad Farhadi, Maria Andersen, Christian Maare, Jens Overgaard, Cai GrauJeppe Friborg, Christian Rønn Hansen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Background: In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. Materials and Methods: The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. Results: The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 − 0.90] and 0.68 [0.51 − 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 − 1.1] mm and 1.9 mm [1.5 − 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in (Formula presented.) NTCP. Conclusions: The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the (Formula presented.) NTCP calculations could be discerned.

Original languageEnglish
JournalActa Oncologica
Volume62
Issue number11
Pages (from-to)1418-1425
ISSN0284-186X
DOIs
Publication statusPublished - Nov 2023
Event21st Acta Oncologica Symposium—BiGART 2023 - Biology-Guided Adaptive Radiotherapy - Aarhus, Denmark
Duration: 20. Jun 202321. Jun 2023

Conference

Conference21st Acta Oncologica Symposium—BiGART 2023 - Biology-Guided Adaptive Radiotherapy
Country/TerritoryDenmark
CityAarhus
Period20/06/202321/06/2023

Keywords

  • AI
  • contouring
  • head and neck cancer
  • organs at risk
  • proton treatment
  • Protons
  • Radiotherapy Planning, Computer-Assisted/methods
  • Humans
  • Artificial Intelligence
  • Organs at Risk
  • Head and Neck Neoplasms

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