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Background <p>Manual 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.</p> Methods <p>We 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.</p> Results <p>Here 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.</p> Conclusions <p>The 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.</p>

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A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月31日

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  • Robert Poel,
  • Elias Rüfenacht,
  • Stefan Scheib,
  • Hossein Hemmatazad,
  • Reinhardt Krcek,
  • Sébastien Tran,
  • Edourd Romano,
  • Susanne Rogers,
  • Sonja Stieb,
  • Mohamed Riyas Poolakundan,
  • Hissa Hussein Al-Abdulla,
  • Robert Foerster,
  • Christina Schröder,
  • Christoph Oehler,
  • Julian Hong,
  • Sebastiaan Breedveld,
  • Nicolaus Andratschke,
  • Peter Manser,
  • Michael K. Fix,
  • Daniel M. Aebersold,
  • Mauricio Reyes,
  • Ekin Ermiş

摘要

Background

Manual 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.

Methods

We 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.

Results

Here 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.

Conclusions

The 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.