{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:14:04Z","timestamp":1793319244592,"version":"4.5.0"},"reference-count":17,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","funder":[{"DOI":"10.13039\/100007892","name":"Faculty of Arts, Ryerson University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007892","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009268","name":"Alport Syndrome Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Gouvernement du Canada, Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Kidney360"],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"\n Key Points<\/jats:title>\n \n \n \n A software tool was developed to perform glomerular and patient-level classification on the basis of clinically relevant biomarkers.<\/jats:p>\n <\/jats:list-item>\n \n Ten biomarkers were used for glomerular and patient-level classification that obtained 77% and 87% accuracies, respectively.<\/jats:p>\n <\/jats:list-item>\n \n In the future, these tools can be applied to clinical datasets for glomerular biomarker discovery and for insights into disease mechanisms.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n \n \n Pathologists use multiple microscopy modalities to assess renal biopsy specimens. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisual clues, provide pathogenetic insight, and link to clinical outcomes. To this end, a proof-of-principle study is presented demonstrating that explainable biomarkers through machine learning can distinguish between glomerular disorders at the light-microscopy level.<\/jats:p>\n The proposed system used image analysis techniques and extracted 233 explainable biomarkers related to color, morphology, and microstructural texture. Traditional machine learning was then used to classify minimal change disease (MCD), membranous nephropathy (MN), and thin basement membrane nephropathy (TBMN) diseases on a glomerular and patient-level basis.<\/jats:p>\n The final model combined the Gini feature importance set and linear discriminant analysis classifier. Six morphologic (nuclei-to-glomerular tuft area, nuclei-to-glomerular area, glomerular tuft thickness greater than ten, glomerular tuft thickness greater than three, total glomerular tuft thickness, and glomerular circularity) and four microstructural texture features (luminal contrast using wavelets, nuclei energy using wavelets, nuclei variance using color vector LBP, and glomerular correlation using GLCM) were, together, the best performing biomarkers. Accuracies of 77% and 87% were obtained for glomerular and patient-level classification, respectively.<\/jats:p>\n Computational methods, using explainable glomerular biomarkers, have diagnostic value and are compatible with our existing knowledge of disease pathogenesis. Furthermore, this algorithm can be applied to clinical datasets for novel prognostic and mechanistic biomarker discovery.<\/jats:p>\n <\/jats:sec>","DOI":"10.34067\/kid.0005102021","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T12:00:15Z","timestamp":1639051215000},"page":"534-545","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":7,"title":["Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification"],"prefix":"10.34067","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0313-3111","authenticated-orcid":false,"given":"Matthew Nicholas","family":"Basso","sequence":"first","affiliation":[{"name":"Image Analysis in Medicine Lab (IAMLAB), Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, 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