Authors
Janan Arslan, Tamara Künzle, Yeşim Güner, Agustí Alentorn
Published in
Computational and structural biotechnology journal. Volume 35. Issue 1. Pages 0238. Epub Oct 19, 2026.
Abstract
Radiomic features provide a quantitative description of tissue organization in histopathology images. QuPath, a widely used open-source platform for whole-slide image analysis, offers morphology, intensity, and Haralick texture measurements but no standardized radiomic feature set such as that of PyRadiomics. Researchers must therefore export data to external Python pipelines, interrupting their QuPath workflow. QuRad extracts radiomic features from cell detections and annotation regions directly inside QuPath. The extension and equivalent Groovy script implement 119 features: 103 two-dimensional features enabled by default and 16 optional legacy shape descriptors. We compared QuRad 0.4 with PyRadiomics 3.0.1 on identical grayscale images and pixel masks. All 103 default features met a 10-9 absolute-or-relative agreement tolerance on a breast-cancer benchmark tile (410 cells). On 20 melanoma tiles (8,644 nuclei), 97 of 103 features met this tolerance and 6 polygon-derived shape features differed from PyRadiomics' raster-derived mesh (minimum concordance correlation coefficient 0.942). The 22 synthetic regions accepted by PyRadiomics reproduced the expected intensity and texture values. A single-threaded server benchmark reached about 1,900 nuclei per second, and processing the 184,242 cells of a whole-slide image took 2.7 min on a laptop. A random forest distinguished tumor cells from lymphocytes in melanoma (pooled macro-F1 0.853) and classified 197 tissue regions of 6 breast-cancer slides into 3 compartments (0.696). QuRad exports the features with object coordinates and extraction settings for use in QuPath or downstream analyses.
PMID:
42898725
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.
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