ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2023
Educative Impact of Automatic Delineation Applied to Head and Neck Cancer Patients on Radiation Oncology Residents.
Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 5 citations in OpenAlex.
- A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer.Physics and imaging in radiation oncology · 2023Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To evaluate the educational impact on radiation oncology residents in training when introducing an automatic segmentation software in head and neck cancer patients regarding organs at risk (OARs) and prophylactic cervical lymph node level (LNL) volumes. Two cases treated by exclusive intensity-modulated radiotherapy were delineated by an expert radiation oncologist and were considered as reference. Then, these cases were delineated by residents divided into two groups: group 1 (control group), experienced residents delineating manually, group 2 (experimental group), young residents on their first rotation trained with automatic delineation, delineating manually first (M -) and then after using the automatic system (M +). The delineation accuracy was assessed using the Overlap Volume (OV). Regarding the OARs, mean OV was 0.62 (SD = 0.05) for group 1, 0.56 (SD = 0.04) for group 2 M - , and 0.61 (SD = 0.03) for group 2 M + . Mean OV was higher in group 1 compared to group 2 M - (p = 0.01). There was no OV difference between group 1 and group 2 M + (p = 0.67). Mean OV was higher in the group 2 M + compared to group 2 M - (p < 0.003). Regarding LNL, mean OV was 0.53 (SD = 0.06) in group 1, 0.54 (SD = 0.03) in group 2 M - , and 0.58 (SD = 0.04) in group 2 M + . Mean OV was higher in group 2 M + for 11 of the 12 analysed structures compared to group 2 M - (p = 0.016). Prior use of the automatic delineation software reduced the average contouring time per case by 34 to 40%. Prior use of atlas-based automatic segmentation reduces the delineation duration, and provides reliable OARs and LNL delineations.
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