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Open access Sep 2026

Interpretable graph-conditioned CNNs for dose-volume histogram prediction in radiotherapy.

A novel deep learning framework that directly predicts full DVHs for multiple organs-at-risk (OARs) from computed tomography (CT) and structure contours and a graph neural network (GNN) that models each DVH as a structured graph, enabling sequential dose-volume dependencies to be learned explicitly.

Xiao-Ling Zhu, C. Ling, Hao Xu et al. · 0 citations
Open access Aug 2026

Real‐world clinical impact of implementing and updating a deep learning‐based automatic contouring system in rectal cancer radiotherapy

Implementing an automatic contouring system provided crucial guidance for clinical practice and its iterative update significantly reduced workload and inter‐observer variation while enhancing contouring efficiency and quality.

Ningyu Wang, Tong-Zhen Xu, Yu-Jie Kang et al. · 0 citations

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