ESTRO 2024 - Abstract Book

S4443

Physics - Machine learning models and clinical applications

ESTRO 2024

Conclusion:

We developed a deep learning-based method for the optimization of the isocenters positions and jaws apertures for TMI/TMLI. The generated field geometries were clinically acceptable and adequate, even for an MP with high level of expertise in TMI/TMLI. Incorporating the knowledge of the MPs into the development cycle was crucial for optimizing the models, especially in this scenario with limited data. MPs revisions are still necessary to adjust inconsistencies and refine the models output. However, these adjustments are subject to inter-observer variability and depend on the MP experience in TMI/TMLI.

This study was part of the AuToMI project and funded by the Italian Ministry of Health GR-2019-12370739.

Keywords: deep learning, autoplanning, TMI/TMLI

References:

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[2] Lambri N, Dei D, Hernandez V, et al. Automatic planning of the lower extremities for total marrow irradiation using volumetric modulated arc therapy. Strahlenther Onkol. 2023;199(4):412-419. doi:10.1007/s00066-022-02014-0