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 risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy.

Materials And Methods: The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS).

Are offline ART decisions for NSCLC impacted by the type of dose calculation algorithm?

Introduction: Decisions for plan-adaptations may be impacted by a transitioning from one dose-calculation algorithm to another. This study examines the impact on dosimetric-triggered offline adaptation in LA-NSCLC in the context of a transition from superposition/convolution dose calculation algorithm (Type-B) to linear Boltzmann equation solver dose calculation algorithms (Type-C).

Materials & Methods: Two dosimetric-triggered offline adaptive treatment workflows are compared in a retrospective planning study on 30 LA-NSCLC patients.

Deep learning based MLC aperture and monitor unit prediction as a warm start for breast VMAT optimisation.

. Automated treatment planning today is focussed on non-exact, two-step procedures. Firstly, dose-volume histograms (DVHs) or 3D dose distributions are predicted from the patient anatomy.

Treatment plan prediction for lung IMRT using deep learning based fluence map generation.

Purpose: Recently, it has been shown that automated treatment planning can be executed by direct fluence prediction from patient anatomy using convolutional neural networks. Proof of principle publications utilise a fixed dose prescription and fixed collimator (0°) and gantry angles. The goal of this work is to further develop these principles for the challenging lung cancer indication with variable dose prescriptions, collimator and gantry angles.

Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy.

Purpose/objective(s): Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively impacting treatment outcomes. The aim of this study is to evaluate the performance and efficiency of segmentation of CTVs in planning CT images of breast cancer patients using a 3D convolutional neural network (CNN) compared to the manual process.