ArticleCommunications medicine2025
A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multimodal PET-MR segmentation for glioblastoma: complementarity for treatment planning and recurrence definition.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy.ArXiv · 2026Article
- Multimodal deep learning to support delineation of neuro-oncology organs based on the EPTN international neurological contouring atlas.Physics and imaging in radiation oncology · 2026Article
- Quantifying manual editing time in autocontouring via audit log analysis.Physics and imaging in radiation oncology · 2026Article
- Review
- Development of an Integrated Radiotherapy Simulation Platform with AI-Driven Segmentation and Ray-Casting-Based Dosimetric Evaluation.Bioengineering (Basel, Switzerland) · 2026Article
- A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.Nature communications · 2026Observational
- Deep ensemble optimized models for probabilistic CTV breast segmentation.Frontiers in artificial intelligence · 2026Article
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 2025Article
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundManual contouring of organs at risk in radiotherapy is time-consuming, taking 1-4 hours per case. Automatic segmentation using deep learning has emerged as a promising solution, with many commercial options now available. However, these methods require rigorous validation before clinical use, and current evaluation approaches lack consistency and comprehensive assessment across publications.
methodsWe developed the Comprehensive Multifaceted Technical Evaluation framework, which integrates four key assessment components: quantitative geometric measures, qualitative expert evaluation, time efficiency analysis, and dosimetric evaluation. We demonstrated this framework using an in-house automatic segmentation model for brain organs at risk, trained on 100 cases and evaluated by 8 radiation oncology experts from 4 institutions. The evaluation included geometric accuracy measurements, expert ratings of clinical acceptability, time-saving assessments, and dosimetric impact analysis comparing treatment plans.
resultsHere we show that our automatic segmentation model achieved an overall geometric accuracy of 0.78 and outperformed manual inter-rater variability. Expert evaluation revealed that 88% of automatically segmented structures were clinically acceptable with only minor adjustments needed. The evaluation and adjustment process averaged 22 minutes compared to 69 minutes for manual contouring. Dosimetric analysis showed minimal impact on treatment plans, with average dose differences of 0.30 Gray for mean dose and 0.23 Gray for maximum dose.
conclusionsThe framework provides a robust method for validating automatic segmentation models in radiotherapy. However, establishing standardized benchmarks and consensus guidelines within the radiotherapy community remains essential for proper clinical implementation and comparison of different segmentation tools.
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Registered trials
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