Quantifying the spatial information loss of Dose-Volume Histograms in Gamma Knife radiosurgery via unsupervised 3D dose geometry embedding

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....

No actionable change for audiologists; this is a radiation physics methods paper with indirect relevance to acoustic neuroma radiosurgery planning, but the findings are too technical and preliminary to alter clinical audiology practice.

Improving the spatial accuracy of radiosurgery dose analysis could eventually reduce cochlear radiation exposure in acoustic neuroma treatment, with downstream implications for hearing preservation outcomes.

1. 01Dose-Volume Histograms used in Gamma Knife planning lose meaningful 3D spatial dose information.
2. 02A new unsupervised 3D dose geometry embedding method was developed to recover that lost spatial detail.
3. 03The approach was validated in the context of Gamma Knife radiosurgery planning.
4. 04Better spatial dose representation may support improved organ-at-risk sparing (e.g., cochlea).
5. 05Study published in Radiological Physics and Technology (Radiol Phys Technol).

PMID

42474981

DOI

10.1007/s12194-026-01093-3.

Journal

Radiological Physics and Technology

Publication type

research_article

Evidence level

4

Population

Gamma Knife radiosurgery treatment plans (dosimetric/computational study; not human clinical subjects)

Intervention

Unsupervised 3D dose geometry embedding to quantify spatial information loss in Dose-Volume Histograms

Comparator

Standard Dose-Volume Histogram representation