This technology is a cross-platform imaging phenomics toolkit that enables the automated quantitative analysis of 2D and 3D cancer radiographic scans, supporting improved diagnosis, prediction, and clinical decision-making.
Clinical imaging workflows rely heavily on manual interpretation or fragmented analysis tools, which makes quantitative imaging assessment inconsistent across institutions. Existing radiomics platforms often require advanced computational expertise, which limits their practicality for routine clinical or translational research applications. The field lacks an accessible, standardized system that can reliably extract and analyze quantitative imaging features at scale.
This technology is a toolkit that analyzes radiographic cancer images through standardized pre-processing and feature extraction routines applied to 2D and 3D medical scans. Quantitative patterns in intensity, texture, and morphology are computed using integrated imaging libraries, and a streamlined interface enables automated, unbiased processing across subjects without requiring programming expertise. The resulting radiomic patterns reflect the underlying imaging phenotypes, and researchers can evaluate clinical or biological differences by comparing feature profiles across patients, time points, or tumor characteristics.
This technology has been validated with human samples.
