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Related Experiment Video

Updated: Jul 23, 2025

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Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy.

Luis A Maduro Bustos1,2, Abhirup Sarkar1, Laura A Doyle1,2

  • 1Department of Radiation Oncology, Christiana Care Helen F. Graham Cancer Center, Newark, Delaware, USA.

Journal of Applied Clinical Medical Physics
|July 19, 2023
PubMed
Summary

Siemens Healthineers AI-Rad Companion Organs RT (Organs-RT) demonstrates clinical feasibility for auto-contouring organs at risk (OARs) in radiotherapy planning. The algorithm significantly reduces contouring time, offering substantial time-saving efficiency across various anatomical regions.

Keywords:
artificial intelligencecontouringconvolutional neural networksdeep-learningsegmentation

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Area of Science:

  • Radiotherapy
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Accurate delineation of organs at risk (OARs) is crucial for effective radiotherapy planning.
  • Manual contouring of OARs is time-consuming and subject to inter-observer variability.
  • AI-driven auto-contouring tools aim to improve efficiency and consistency in radiotherapy workflows.

Purpose of the Study:

  • To evaluate the clinical feasibility of the Siemens Healthineers AI-Rad Companion Organs RT (Organs-RT) auto-contouring algorithm.
  • To assess the accuracy of Organs-RT auto-contours for OARs in the pelvis, thorax, and head and neck (H&N) regions.
  • To measure the time-saving efficiency of the Organs-RT algorithm compared to manual contouring.

Main Methods:

  • Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, 10 H&N) were used.
  • Organs-RT generated auto-contours, compared against three independent expert human users.
  • Contour accuracy was evaluated using Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), and visual inspection by a physician.
  • Time-saving efficiency was measured by recording manual delineation times.

Main Results:

  • High agreement (DSC ≥ 0.92) was observed for OARs like the bladder, heart, lungs, and femoral heads.
  • Poorer agreement (DSC ≤ 0.81) was noted for the rectum, esophagus, and lips.
  • Time-saving efficiency was 67% for H&N, 83% for pelvis, and 84% for thorax.
  • Clinical usability scores indicated that 72.5% (pelvis), 82% (thorax), and 50% (H&N) of Organs-RT contours were deemed usable with minimal or no edits.

Conclusions:

  • The Organs-RT algorithm shows clinical feasibility as an auto-contouring tool in radiotherapy.
  • It effectively minimizes contouring time and enhances time-saving efficiency.
  • While agreement varies by OAR, the tool offers significant benefits for radiotherapy treatment planning.