Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets.

Introduction: The development of deep learning (DL) models for auto-segmentation is increasing and more models become commercially available. Mostly, commercial models are trained on external data. To study the effect of using a model trained on external data, compared to the same model trained on in-house collected data, the performance of these two DL models was evaluated.

Clinical evaluation of two AI models for automated breast cancer plan generation.

Background: Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists.

Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer.

Background And Purpose: Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN).

Reduction of heart and lung normal tissue complication probability using automatic beam angle optimization and more generic optimization objectives for breast radiotherapy.

During breast cancer radiotherapy, sparing of healthy tissue is desired. The effect of automatic beam angle optimization and generic dose fall-off objectives on dose and normal tissue complication probabilities was studied. In all patients, dose to lungs and heart showed a mean reduction of 0.

Development and evaluation of radiotherapy deep learning dose prediction models for breast cancer.

Background And Purpose: Treatment planning of radiotherapy is a time-consuming and planner dependent process that can be automated by dose prediction models. The purpose of this study was to evaluate the performance of two machine learning models for breast cancer radiotherapy before possible clinical implementation.

Materials And Methods: An in-house developed model, based on U-net architecture, and a contextual atlas regression forest (cARF) model integrated in the treatment planning software were trained.