ArticleRadiological physics and technology2026
Quantifying the spatial information loss of Dose-Volume Histograms in Gamma Knife radiosurgery via unsupervised 3D dose geometry embedding.
Article in Radiological physics and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Dose-Volume Histograms (DVH) discard spatial information by summarizing 3D distributions into 1D curves. This study quantifies this loss in Gamma Knife radiosurgery and introduces TopoGK, a novel unsupervised deep learning framework capturing full 3D dose geometry. Ninety-six vestibular schwannoma plans were analyzed. Dose grids were tumor-centered, resampled to [Formula: see text] voxels, and normalized to [Formula: see text]. A 3D Convolutional Variational Autoencoder compressed each dose-mask volume into a 64dimensional spatial embedding. Spatial information loss was quantified by comparing pairwise DVH and latent distances both globally and within volume-stratified subgroups. Physical validation employed hotspot center-of-mass displacement, gradient anisotropy, and a novel Spatial Discordance Index (SDI). Five-fold cross-validation ensured generalizability. A linear PCA baseline was included for comparison. The correlation between DVH and spatial similarity was weak ([Formula: see text] within volume-matched pairs). Among DVH-matched pairs, the hotspot exhibited a median physical displacement of 2.81 mm (90th percentile: 4.69 mm), and 61.8-76.5% exceeded heuristic geometric SDI thresholds. Multivariate regression ([Formula: see text]; volume-adjusted [Formula: see text]) confirmed that the learned embedding is driven by spatial metrics-hotspot displacement ([Formula: see text]) and anisotropy ( [Formula: see text]) rather than DVH ([Formula: see text]). Held-out reconstruction SSIM reached [Formula: see text]. TopoGK outperformed PCA in spatial correlation ( [Formula: see text] 0.451 vs. 0.287). DVH similarity does not guarantee spatial dose equivalence. The proposed framework provides a physically validated spatial fingerprint capturing geometric variations invisible to conventional plan evaluation. As a proof-of-concept in single-fraction vestibular schwannoma radiosurgery, these results motivate further investigation toward spatially-aware quality assurance in stereotactic radiosurgery.
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