Glomerular Segmentation, Classification, and Pathomic Feature-based Prediction of Clinical Outcomes in Minimal Change Disease and Focal Segmental Glomerulosclerosis
Year: 2025
Type: preprint
Abstract: Background: Conventional assessment of Focal Segmental Glomerulosclerosis and Minimal Change Disease focuses on the presence/extent of segmental (SS) and global (GS) glomerulosclerosis. While SS and GS represent ongoing and terminal process, encoded in non-SS/GS glomeruli is prognostic information that can be extracted before structural changes are visually discernable. This study applies computational image analysis to (a) automate the segmentation and classification of glomeruli into GS, SS and non-GS/SS, (b) extract subvisual pathomic characteristics from non-GS/SS glomeruli, and (c) assess their clinical relevance. Methods: Leveraging the NEPTUNE/CureGN Periodic acid Schiff-stained whole slide images, we (i) developed deep learning (DL) models for the segmentation and classification of glomeruli into GS, SS and non-GS/SS; (ii) compared the association with disease progression and proteinuria remission of DL-derived percent of GS and SS vs. human scoring; (iii) extracted pathomic features from non-GS/SS; (iv) assessed their prognostic value using ridge-penalized Cox regression, with pathomic features ranked by Maximum Relevance Minimum Redundancy algorithm; and (v) estimated associations between selected pathomic features and clinical outcomes using Cox proportional hazard models. Results: Agreement between computer-aided and visual scoring was good for %GS (ICC = 0.889) and moderate for %SS (ICC = 0.592). The prognostic performance of Cox models of computer-aided visual scoring approaches was comparable (iAUCs 0.779 vs. 0.776 for disease progression and 0.811 vs. 0.817 for complete proteinuria remission, respectively). For non-GS/SS glomeruli, 3 and 4 pathomic features were selected and demonstrated modest prognostic performance for disease progression (iAUC = 0.684) and proteinuria remission (iAUC = 0.661), respectively. After adjusting for demographics, clinical characteristics, %GS and %SS, 2 pathomic features remained statistically significantly associated with proteinuria remission. Conclusion: Computational pathology allows for automatic quantification of SS/GS glomeruli that is comparable to manual assessment for outcome prediction, and the uncovering of previously under-recognized clinically useful information from non-GS/SS glomeruli.
Source: medRxiv
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: University of Miami, University of Pennsylvania, Ohio State University, Emory University, Northwestern University, Texas Children's Hospital, Wake Forest University, Harvard University, University of Minnesota, Washington University in St. Louis, Cleveland Clinic, University of Washington, Johns Hopkins University, University of South Carolina, Temple University, York University, National Institutes of Health, University of North Carolina at Chapel Hill, Anschutz Medical Campus, University of Colorado, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Research Resources
Awards: G100871905, RadxTools for assessing tumor treatment response on imaging, G1266336578, G1478154455, G1817069843, G2228475719, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, G3137332008, G338730684, G3564444776, G3720244932, G406353596, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, G5758079931, G6503697038, G7124118669, G761790451, G9000000780, G9000021175, G9000023716, G9000064711, G9000122515, G9000153031, G9000317679, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, G942918344
OpenAlex ID: W4414909173