{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T08:41:45Z","timestamp":1792399305645,"version":"4.4.0"},"reference-count":25,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"1","funder":[{"DOI":"10.13039\/100018694","name":"HORIZON EUROPE Marie Sklodowska-Curie Actions","doi-asserted-by":"publisher","award":["Grant Agreement No. 101081674"],"award-info":[{"award-number":["Grant Agreement No. 101081674"]}],"id":[{"id":"10.13039\/100018694","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Agence Nationale de la Recherche","award":["ANR-23-CE17-0027-01 (JCJC LOCImm)"],"award-info":[{"award-number":["ANR-23-CE17-0027-01 (JCJC LOCImm)"]}]},{"name":"France 2030 PEPR Sant\u00e9 Num\u00e9rique programme","award":["2025-PEPR-121554 (BRAINTWIN)"],"award-info":[{"award-number":["2025-PEPR-121554 (BRAINTWIN)"]}]},{"name":"Institut national du cancer","award":["OSIRIS25 (MultiPOLA)"],"award-info":[{"award-number":["OSIRIS25 (MultiPOLA)"]}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:p>\n                    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\n                    <jats:sup>\u22129<\/jats:sup>\n                    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\u2019 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.\n                  <\/jats:p>","DOI":"10.34133\/csbj.0238","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T13:10:05Z","timestamp":1791292205000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["QuRad: Radiomics Feature Extraction for QuPath"],"prefix":"10.34133","volume":"35","author":[{"given":"Janan","family":"Arslan","sequence":"first","affiliation":[{"name":"Sorbonne Universit\u00e9, Institut du Cerveau\u2014Paris Brain Institute\u2014ICM, CNRS, Inria, Inserm, AP-HP, \rH\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re, Paris, France."},{"name":"Centre for Eye Research Australia, University of Melbourne, \rRoyal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7663-6819","authenticated-orcid":true,"given":"Tamara","family":"K\u00fcnzle","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Institut du Cerveau\u2014Paris Brain Institute\u2014ICM, CNRS, Inria, Inserm, AP-HP, \rH\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re, Paris, France."},{"name":"LIX, \u00c9cole Polytechnique","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye\u015fim","family":"G\u00fcner","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Institut du Cerveau\u2014Paris Brain Institute\u2014ICM, CNRS, Inria, Inserm, AP-HP, \rH\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re, Paris, France."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7220-2686","authenticated-orcid":false,"given":"Agust\u00ed","family":"Alentorn","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Institut du Cerveau\u2014Paris Brain Institute\u2014ICM, CNRS, Inria, Inserm, AP-HP, \rH\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re, Paris, France."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,10,19]]},"reference":[{"issue":"21","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"Griethuysen JJM","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts HJWL. 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