ArticlePhysics and imaging in radiation oncology2022
Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy.
Article in Physics and imaging in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Clinical edge-case stress testing of two Asia-developed AI auto-contouring systems using prostate radiotherapy planning CT images.Radiological physics and technology · 2026Article
- Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning.Physics and imaging in radiation oncology · 2023Article
- Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.Journal of applied clinical medical physics · 2023Article
- How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images.Frontiers in oncology · 2023Article
- Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography.Physics and imaging in radiation oncology · 2023Article
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Authors and funding
5 authors.
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Abstract
Background and purpose: Autosegmentation techniques are emerging as time-saving means for radiation therapy (RT) contouring, but the understanding of their performance on different datasets is limited. The aim of this study was to determine agreement between rectal volumes by an existing autosegmentation algorithm and manually-delineated rectal volumes in prostate cancer RT. We also investigated contour quality by different-sized training datasets and consistently-curated volumes for retrained versions of this same algorithm. Materials and methods: Single-institutional data from 624 prostate cancer patients treated to 50-70 Gy were used. Manually-delineated clinical rectal volumes ( Results: Volumes by Conclusion: Using small single-institutional RT datasets with consistently-defined rectal volumes when training autosegmentation algorithms created contours of similar quality as the same algorithm trained on large multi-institutional datasets.
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