Neural network dose prediction for rectal spacer stratification in dose-escalated prostate radiotherapy.

ABSTRACT:

Purpose

To develop a knowledge-based decision-support system capable of stratifying patients for rectal spacer (RS) insertion based on neural network predicted rectal dose, reducing the need for time- and resource-intensive radiotherapy (RT) planning.

Methods

Forty-four patients treated for prostate cancer were enrolled into a clinical trial (NCT03238170). Dose-escalated prostate RT plans were manually created for 30 patients with simulated boost volumes using a conventional treatment planning system (TPS) and used to train a hierarchically dense 3D convolutional neural network to rapidly predict RT dose distributions. The network was used to predict rectal doses for 14 unseen test patients, with associated toxicity risks calculated according to published data. All metrics o

SUBMITTER: Thomas C 

PROVIDER: S-EPMC9311720 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature