Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning.

ABSTRACT:

Background and purpose

Automation in radiotherapy treatment planning aims to improve both the quality and the efficiency of the process. The aim of this study was to report on a clinical implementation of a Deep Learning (DL) auto-planning model for left-sided breast cancer.

Materials and methods

The DL model was developed for left-sided breast simultaneous integrated boost treatments under deep-inspiration breath-hold. Eighty manual dose distributions were revised and used for training. Ten patients were used for model validation. The model was then used to design 17 clinical auto-plans. Manual and auto-plans were scored on a list of clinical goals for both targets and organs-at-risk (OARs). For validation, predicted and mimicked dose (PD and MD, respectively) percent error

SUBMITTER: Zeverino M 

PROVIDER: S-EPMC10534254 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature