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Background <p>This study aimed to evaluate the clinical feasibility and performance of CT-based auto-segmentation models integrated into an All-in-One radiotherapy workflow for rectal cancer.</p> Methods <p>This study included 312 rectal cancer patients, with 272 used to train three nnU-Net models for CTV45, CTV50, and GTV segmentation, and 40 for evaluation across one internal (<i>n</i> = 10), one clinical AIO (<i>n</i> = 10), and two external cohorts (<i>n</i> = 10 each). Segmentation accuracy (DSC, HD, HD95, ASSD, ASD) and time efficiency were assessed.</p> Results <p>In the internal testing set, mean DSC of CTV45, CTV50, and GTV were 0.90, 0.86, and 0.71; HD were 17.08, 25.48, and 79.59&#xa0;mm; HD 95 were 4.89, 7.33, and 56.49&#xa0;mm; ASSD were 1.23, 1.90, and 6.69&#xa0;mm; and ASD were 1.24, 1.58, and 11.61&#xa0;mm. Auto-segmentation reduced manual delineation time by 63.3–88.3% (<i>p</i> &lt; 0.0001). In clinical practice, average DSC of CTV45, CTV50 and GTV were 0.93, 0.88, and 0.78; HD were 13.56, 23.84, and 35.38&#xa0;mm; HD 95 were 3.33, 6.46, and 21.34&#xa0;mm; ASSD were 0.78, 1.49, and 3.30&#xa0;mm; and ASD were 0.74, 1.18, and 2.13&#xa0;mm. The results from the multi-center testing also showed applicability of these models, since the average DSC of CTV45 and GTV were 0.84 and 0.80 respectively.</p> Conclusions <p>The models demonstrated high accuracy and clinical utility, effectively streamlining target volume delineation and reducing manual workload in routine practice.</p> Trial registration <p>The study protocol was approved by the Institutional Review Board of Peking University Third Hospital (Approval No. (2024) Medical Ethics Review No. 182-01).</p>

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CT-based auto-segmentation of multiple target volumes for all-in-one radiotherapy in rectal cancer patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Xuemin Li,
  • Luqi Wang,
  • Mengying Yang,
  • Xianan Li,
  • Ting Zhao,
  • Mingqing Wang,
  • Siyi Lu,
  • Yunsong Ji,
  • Wei Zhang,
  • Lecheng Jia,
  • Ran Peng,
  • Junjie Wang,
  • Hao Wang

摘要

Background

This study aimed to evaluate the clinical feasibility and performance of CT-based auto-segmentation models integrated into an All-in-One radiotherapy workflow for rectal cancer.

Methods

This study included 312 rectal cancer patients, with 272 used to train three nnU-Net models for CTV45, CTV50, and GTV segmentation, and 40 for evaluation across one internal (n = 10), one clinical AIO (n = 10), and two external cohorts (n = 10 each). Segmentation accuracy (DSC, HD, HD95, ASSD, ASD) and time efficiency were assessed.

Results

In the internal testing set, mean DSC of CTV45, CTV50, and GTV were 0.90, 0.86, and 0.71; HD were 17.08, 25.48, and 79.59 mm; HD 95 were 4.89, 7.33, and 56.49 mm; ASSD were 1.23, 1.90, and 6.69 mm; and ASD were 1.24, 1.58, and 11.61 mm. Auto-segmentation reduced manual delineation time by 63.3–88.3% (p < 0.0001). In clinical practice, average DSC of CTV45, CTV50 and GTV were 0.93, 0.88, and 0.78; HD were 13.56, 23.84, and 35.38 mm; HD 95 were 3.33, 6.46, and 21.34 mm; ASSD were 0.78, 1.49, and 3.30 mm; and ASD were 0.74, 1.18, and 2.13 mm. The results from the multi-center testing also showed applicability of these models, since the average DSC of CTV45 and GTV were 0.84 and 0.80 respectively.

Conclusions

The models demonstrated high accuracy and clinical utility, effectively streamlining target volume delineation and reducing manual workload in routine practice.

Trial registration

The study protocol was approved by the Institutional Review Board of Peking University Third Hospital (Approval No. (2024) Medical Ethics Review No. 182-01).