Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data.

ABSTRACT:

Purpose

A three-dimensional deep generative adversarial network (GAN) was used to predict dose distributions for locally advanced head and neck cancer radiotherapy. Given the labor- and time-intensive nature of manual planning target volume (PTV) and organ-at-risk (OAR) segmentation, we investigated whether dose distributions could be predicted without the need for fully segmented datasets.

Materials and methods

GANs were trained/validated/tested using 320/30/35 previously segmented CT datasets and treatment plans. The following input combinations were used to train and test the models: CT-scan only (C); CT+PTVboost/elective (CP); CT+PTVs+OARs+body structure (CPOB); PTVs+OARs+body structure (POB); PTVs+body structure (PB). Mean absolute errors (MAEs) for the predicted dose d

SUBMITTER: Gu X 

PROVIDER: S-EPMC10565853 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature