Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy.

ABSTRACT:

Background and purpose

In order to optimize the radiotherapy treatment and minimize toxicities, organs-at-risk (OARs) and clinical target volume (CTV) must be segmented. Deep Learning (DL) techniques show significant potential for performing this task effectively. The availability of a large single-institute data sample, combined with additional numerous multi-centric data, makes it possible to develop and validate a reliable CTV segmentation model.

Materials and methods

Planning CT data of 1822 patients were available (861 from a single center for training and 961 from 8 centers for validation). A preprocessing step, aimed at standardizing all the images, followed by a 3D-Unet capable of segmenting both right and left CTVs was implemented. The metrics used to evaluate the p

SUBMITTER: Ubeira-Gabellini MG 

PROVIDER: S-EPMC12002654 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature