Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.

ABSTRACT:

Background

In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.

Purpose

To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements.

Methods

A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models we

SUBMITTER: Gronberg MP 

PROVIDER: S-EPMC10947338 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature