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.

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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

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article

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

Sare Hosseini, Mehdi Momennezhad, Alireza Mehdizadeh, Mohammad Amin Mosleh‐Shirazi et al.
8 citations
Journal of Applied Clinical Medical Physics
Advanced Radiotherapy Techniques
4.86
article

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

Sare Hosseini, Mehdi Momennezhad, Alireza Mehdizadeh, Mohammad Amin Mosleh‐Shirazi, Zohreh Pishevar, Shahrokh Nasseri, Amin Amiri Tehranizadeh, Shokoufeh Mohebbi, Najmeh Arjmandi
article en
8 citations

Abstract

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.

Journal of Applied Clinical Medical PhysicsVol. 26(3)
Mashhad University of Medical Sciences (IR), Shiraz University of Medical Sciences (IR), Imam Reza Hospital (IR)
Mashhad University of Medical Sciences
Openalex Percentile: Top 4%
Advanced Radiotherapy Techniques
4.86
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