From rugged ridges to radiotherapy ROIs: Translating topographical metrics to Surface-Guided Radiation Therapy regions of Interest in radiotherapy.

Abstract

To evaluate whether geography-derived topographical metrics (e.g., slope, aspect, elevation change and ruggedness) provide a quantitative, reproducible description of SGRT ROI surface quality. We pre-specified feasibility criteria: (i) monotonic, directionally consistent changes with controlled smoothing on synthetic surfaces; and (ii) separation of distributions between clinically distinct ROIs (breast size; full- vs limited-face). Quantitative topographical metrics were identified for investigation including Slope, Aspect, Vector Ruggedness Measure (VRM), Topographic Position Index (TPI) and Terrain Ruggedness Index (TRI). First, synthetic breast-like and face-like surfaces were generated in Python using Perlin noise. Each surface was progressively smoothed and analysed for metric response to surface complexity. Second, three surface captures were exported from the AlignRT SGRT system: a small breast, a large breast and a face, which was cropped to produce a limited-face and a full-face surface. Histograms and 3D maps visualized metric distributions for each ROI. Slope, Aspect, TPI, and TRI effectively captured surface variations in both synthetic and patient data, identifying useful topographical features for SGRT. VRM remained low, relative to typical rugged geological terrain, indicating limited value for smooth skin surfaces. For the synthetic surfaces, increased smoothing compressed slope values toward zero, narrowed Aspect spreads, and lowered TRI/TPI variability. For patient/volunteer surfaces, the small-breast ROI showed fewer slope and aspect regions, and the large-breast ROI had broader slope and aspect ranges, and higher TRI/TPI, reflecting more pronounced local folds. Full-face ROIs exhibited wider slope/TRI/TPI ranges than limited-face ROIs. Geography-derived metrics quantify ROI surface variation and meet pre-specified feasibility criteria and may help personalise and optimise ROI selection for individual patient anatomy. These results provide a quantitative foundation for ROI design and training; prospective studies are required to link metric thresholds to setup and intrafraction performance.