Evaluating the dosimetric impact of deep‐learning‐based auto‐segmentation in prostate cancer radiotherapy: Insights into real‐world clinical implementation and inter‐observer variability
Our automated contouring system can reduce inter-expert variability and achieve dosimetric accuracy comparable to gold standard reference contours, highlighting its potential for streamlining clinical workflows. The quantitative analysis revealed no consistent trend of increasing or decreasing PTVs derived from automatically contoured CTVs and OAR doses due to automated contours, indicating minimal impact on treatment outcomes. These findings support the clinical feasibility of utilizing our deep-learning-based auto-contouring model for prostate cancer radiotherapy planning.
Authors
- Sare Hosseini (ORCID: https://orcid.org/0000-0002-1242-763X)
- Mehdi Momennezhad (ORCID: https://orcid.org/0000-0002-1509-0446)
- Alireza Mehdizadeh (ORCID: https://orcid.org/0000-0002-2867-7887)
- Mohammad Amin Mosleh‐Shirazi (ORCID: https://orcid.org/0000-0003-3021-6489)
- Zohreh Pishevar
- Shahrokh Nasseri (ORCID: https://orcid.org/0000-0002-6973-8586)
- Amin Amiri Tehranizadeh
- Shokoufeh Mohebbi
- Najmeh Arjmandi
Institutions
- Mashhad University of Medical Sciences (IR)
- Shiraz University of Medical Sciences (IR)
- Imam Reza Hospital (IR)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/acm2.14569
- Citations
- 8
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.86
Funders
- Mashhad University of Medical Sciences