Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy

Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy

Background and Purpose:: With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the...

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Bibliographic Details
Main Authors: Liesbeth Vandewinckele, Chahrazad Benazzouz, Laurence Delombaerde, Laure Pape, Truus Reynders, Aline Van der Vorst, Dylan Callens, Jan Verstraete, Adinda Baeten, Caroline Weltens, Wouter Crijns
Format: Article
Language:English
Published: Elsevier 2024-10-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Risk analysis
FMEA
In-house tool
Automated breast planning
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631624001477
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