Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.

ABSTRACT: Accurate dose distribution prediction in head and neck radiotherapy remains complex due to anatomical variability and proximity of critical organs. This study presents a meta-analysis of convolutional neural networks (CNNs) and related architectures to evaluate their predictive efficacy. The MEDLINE (PubMed), Embase (Elsevier and Ovid), Scopus, and Scholar databases were searched using a structured search string. EndNote software was used for screening. Meta-analysis was conducted using the effect size of the absolute prediction error (MAE) for D95 in the planning target volume (PTV) and Dmax in the spinal cord (as a representative organ at risk, OAR). The PROBAST tool was used to assess methodological quality. Missing training parameters were retrieved from supplementary sources, and upda

SUBMITTER: Zamanian M 

PROVIDER: S-EPMC12756222 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature