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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
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Related Experiment Video

Updated: May 4, 2026

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
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Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer.

Sijuan Huang1,2, Jingheng Wu1,3, Xi Lin1,4

  • 1Sun Yat-sen University Cancer Center, Guangzhou 510060, China.

Bioengineering (Basel, Switzerland)
|June 26, 2025
PubMed
Summary

Automated radiotherapy for prostate cancer using auto-segmentation and auto-planning shows accuracy comparable to manual methods. This approach offers improved organ-at-risk sparing and clinical feasibility for online radiotherapy applications.

Keywords:
auto-planningauto-segmentationautomated radiotherapyprostate cancer

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Radiotherapy for prostate cancer requires precise delineation of tumor volumes and organs at risk (OARs).
  • Manual segmentation and planning are time-consuming and can be subject to inter-observer variability.
  • Automated methodologies offer potential for increased efficiency and consistency in radiotherapy.

Purpose of the Study:

  • To develop and evaluate the clinical feasibility of auto-segmentation and auto-planning for automated radiotherapy in prostate cancer.
  • To assess the accuracy and efficiency of AI-driven segmentation and planning processes.
  • To compare automated plans with manually generated plans in terms of target coverage and OAR sparing.

Main Methods:

  • A 3D U-Net model was trained for auto-segmentation of gross tumor volume (GTV), clinical tumor volume (CTV), nodal CTV (CTVnd), and OARs using data from 166 patients.
  • Performance metrics included Dice Similarity Coefficient (DSC), Recall, Precision, Volume Ratio (VR), Hausdorff distance (HD95), and volumetric revision degree (VRD).
  • An auto-planning network was trained on 77 treatment plans; dosimetric differences and clinical acceptability were evaluated, including the impact of OAR editing.

Main Results:

  • Auto-segmentation achieved high accuracy (DSC for GTV/CTV/CTVnd: 0.87/0.88/0.82; OAR DSC ≥ 0.83) with rapid processing (1 min 20 s/case).
  • Auto-planning demonstrated superior conformity (p ≤ 0.01) and OAR sparing (p ≤ 0.03) compared to manual plans, with only 6.7% of auto-plans deemed unacceptable.
  • 75% of auto-plans were considered superior to manual plans, and OAR editing showed no significant impact on dosimetry.

Conclusions:

  • Auto-segmentation accuracy is comparable to manual segmentation in prostate cancer radiotherapy.
  • Auto-planning provides equivalent or superior OAR protection and meets the demands of online automated radiotherapy.
  • These automated methodologies facilitate clinical application and improve the efficiency of radiotherapy delivery.