1. Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution
- Author
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Andrada Turcas, Daniel Leucuta, Cristina Balan, Enrico Clementel, Cristina Gheara, Alex Kacso, Sarah M. Kelly, Delia Tanasa, Dana Cernea, and Patriciu Achimas-Cadariu
- Subjects
Radiotherapy ,Artificial Intelligence ,Radiation Oncology ,Brain tumors ,Delineation/contouring ,Treatment planning ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Background and purpose: Normal tissue sparing in radiotherapy relies on proper delineation. While manual contouring is time consuming and subject to inter-observer variability, auto-contouring could optimize workflows and harmonize practice. We assessed the accuracy of a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation. Materials and methods: Thirty adult brain tumor patients were retrospectively manually recontoured. Two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours). For 15 selected cases, identical plans were optimized for each structure set. We used Dice Similarity Coefficient (DSC) and mean surface-distance (MSD) for geometric comparison and gamma analysis and dose-volume-histogram comparison for dose metrics evaluation. Wilcoxon signed-ranks test was used for paired data, Spearman coefficient(ρ) for correlations and Bland–Altman plots to assess level of agreement. Results: Auto-contouring was significantly faster than manual (1.1/20 min, p
- Published
- 2023
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