Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.

Journal: Journal of applied clinical medical physics
PMID: 39616629

Abstract

PURPOSE: This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geometric accuracy of auto-contouring system to the variability observed between human experts.

Authors

  • Najmeh Arjmandi
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Mohammad Amin Mosleh-Shirazi
    Physics Unit, Department of Radio-Oncology, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Shokoufeh Mohebbi
    Medical Physics Department, Reza Radiation Oncology Center, Mashhad, Iran.
  • Shahrokh Nasseri
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Alireza Mehdizadeh
    Department of Medical Physics and Medical Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Zohreh Pishevar
    Department of Radiation Oncology, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Sare Hosseini
    Department of Radiation Oncology, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Amin Amiri Tehranizadeh
    Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Mehdi Momennezhad
    Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

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