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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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A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...

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Related Experiment Video

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Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
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Evaluation of a prostate SBRT planning workflow using auto-segmentation and knowledge-based planning.

Trisha Jones1,2, Kirk Luca1, Mingdong Fan1

  • 1Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA.

Journal of Applied Clinical Medical Physics
|October 8, 2025
PubMed
Summary

Artificial intelligence (AI) contouring in prostate stereotactic body radiotherapy (SBRT) shows promise for organs at risk but requires physician oversight for target volumes. Post-processed AI contours improve dosimetric outcomes when combined with physician-delineated targets.

Keywords:
AI contouringProstate SBRTknowledge‐based planning

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Healthcare

Background:

  • Prostate stereotactic body radiotherapy (SBRT) demands precise target delineation and organ at risk (OAR) contouring for optimal treatment planning.
  • Integrating artificial intelligence (AI) contouring into SBRT workflows offers potential for increased efficiency and accuracy.
  • Knowledge-based planning (KBP) is a sophisticated method for automating radiotherapy plan generation.

Purpose of the Study:

  • To evaluate the comparability of AI-generated contours versus physician-delineated contours in achieving dosimetric goals for prostate SBRT.
  • To assess the accuracy of commercial AI contouring software for prostate, rectum, and bladder delineation.
  • To determine the dosimetric impact of AI-generated OAR contours within a KBP framework.

Main Methods:

  • Retrospective analysis of 20 prostate cancer patients undergoing SBRT.
  • Application of commercial AI contouring software to CT scans for prostate, rectum, and bladder.
  • Comparison of AI contours with physician-delineated contours using Dice Similarity Coefficient (DSC), surface DSC (sDSC), and Added Path Length (APL).
  • Generation of Volumetric Modulated Arc Therapy (VMAT) plans using an in-house prostate SBRT KBP model with different contouring inputs (clinical, AI, post-processed AI).

Main Results:

  • AI prostate contours were deemed clinically unacceptable.
  • AI rectum and bladder contours showed overlap with clinical prostate contours in a significant number of cases.
  • While AI OARs initially caused minor dosimetric deviations, post-processing and recalculation on clinical contours led to acceptable plan quality, comparable to reference plans.
  • Plans utilizing post-processed AI OARs demonstrated improved rectum and bladder sparing compared to initial AI OARs.

Conclusions:

  • A fully automated AI contouring and planning workflow for prostate SBRT is not yet feasible.
  • Physician-delineated target volumes remain essential for accurate prostate SBRT planning.
  • Combining physician-delineated target volumes with post-processed AI contours for OARs shows encouraging results for achieving dosimetric goals in prostate SBRT.