Development and evaluation of deep learning models for estimating the organ at-risk dose constraint from two-dimensional cine magnetic resonance imaging scans during irradiation.

ABSTRACT:

Purpose

Two-dimensional (2D) cine magnetic resonance imaging (MRI), available with a MR-linear accelerator (MR-Linac), allows real-time visualization of anatomical information during irradiation. The present study aimed to develop and evaluate a deep learning model that can estimate the organ-at-risk (OAR) dose constraints (mainly bladder V37Gy) from 2D cine MRI.

Methods

The present study enrolled 91 prostate cancer patients treated with MR-Linac. From 381 treatment fractions, sagittal images at the start and end of the 2D cine MRI were extracted. Additionally, 3D MRI data acquired pre- and post-irradiation were collected, from which bladder V37Gy was calculated. We designed the deep learning model to predict the end-of-irradiation bladder V37Gy value based on the bladder im

SUBMITTER: Tanaka S 

PROVIDER: S-EPMC12660054 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature