État des lieux de la formation initiale de l’internat d’oncologie après la quatrième vague pandémie de la COVID-19 : une enquête AERIO-SFJRO

Rousseau, Adrien; Laune, Quentin; Ollivier, Laurence; Naoun, Natacha; Alexandre, Jérôme; Giraud, P.; Azria, David; Delaye, Matthieu

doi:10.1016/j.bulcan.2022.04.004

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Its main goals include radiation oncology (RO) training and working in close collaboration with the french society of Radiation Oncology (SFRO) and the National Board of Oncology Teachers (CNEC). Translational lectures are given several times a year to improve the training of RO residents [ 11 , 12 , 13 , 14 ]. Concerning delineation topics, national courses are organized regularly in radio-anatomy with around 180–200 attendings (Paris 2009, Paris 2012, Paris 2018).…”

Section: Introductionmentioning

confidence: 99%

Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective

Bourbonne

Laville

Wagneur

et al. 2023

Cancers

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Introduction: Segmentation of organs at risk (OARs) and target volumes need time and precision but are highly repetitive tasks. Radiation oncology has known tremendous technological advances in recent years, the latest being brought by artificial intelligence (AI). Despite the advantages brought by AI for segmentation, some concerns were raised by academics regarding the impact on young radiation oncologists’ training. A survey was thus conducted on young french radiation oncologists (ROs) by the SFjRO (Société Française des jeunes Radiothérapeutes Oncologues). Methodology: The SFjRO organizes regular webinars focusing on anatomical localization, discussing either segmentation or dosimetry. Completion of the survey was mandatory for registration to a dosimetry webinar dedicated to head and neck (H & N) cancers. The survey was generated in accordance with the CHERRIES guidelines. Quantitative data (e.g., time savings and correction needs) were not measured but determined among the propositions. Results: 117 young ROs from 35 different and mostly academic centers participated. Most centers were either already equipped with such solutions or planning to be equipped in the next two years. AI segmentation software was mostly useful for H & N cases. While for the definition of OARs, participants experienced a significant time gain using AI-proposed delineations, with almost 35% of the participants saving between 50–100% of the segmentation time, time gained for target volumes was significantly lower, with only 8.6% experiencing a 50–100% gain. Contours still needed to be thoroughly checked, especially target volumes for some, and edited. The majority of participants suggested that these tools should be integrated into the training so that future radiation oncologists do not neglect the importance of radioanatomy. Fully aware of this risk, up to one-third of them even suggested that AI tools should be reserved for senior physicians only. Conclusions: We believe this survey on automatic segmentation to be the first to focus on the perception of young radiation oncologists. Software developers should focus on enhancing the quality of proposed segmentations, while young radiation oncologists should become more acquainted with these tools.

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