The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy

Ethan Wang, Allen Yen, Brian Hrycushko, Siqiu Wang, Jingyin Lin, Xinran Zhong, Michael Dohopolski, Chika Nwachukwu, Zohaib Iqbal, Kevin Albuquerque

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Purpose: Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. Materials and Methods: From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%. Results: Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans. Conclusion: In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.

Original languageEnglish (US)
Article number100546
JournalPhysics and Imaging in Radiation Oncology
Volume29
DOIs
StatePublished - Jan 2024

Keywords

  • Adaptive Radiotherapy
  • Artificial Intelligence
  • Cone-beam CT
  • Deformable Image Registration

ASJC Scopus subject areas

  • Radiation
  • Oncology
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy'. Together they form a unique fingerprint.

Cite this