Glomerular Diseases, IgA Nephropathy (IgAN), Kidney Diseases, Chronic, Membranous Nephropathy, Tubulointerstitial Kidney Diseases
Conditions
Brief summary
This single-center retrospective observational cohort study will utilize digital renal pathology images along with corresponding clinical and laboratory data from patients who underwent kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and March 31, 2026. Deep learning and image analysis techniques will be employed to develop and validate models for the pathological diagnosis and classification of kidney diseases. The study will also assess model performance in evaluating disease activity and chronicity, as well as investigate the association between image-derived features and long-term renal outcomes. Diagnoses will be established by consensus among three senior renal pathologists, who will serve as the reference standard. A minimum of 8,000 eligible cases are planned for inclusion, which will be divided into training, validation, and independent test sets.
Interventions
None listed
Sponsors
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Inclusion criteria
1. Patients with kidney disease confirmed by kidney biopsy. 2. Availability of complete digital renal pathology images, including light microscopy, immunofluorescence microscopy, and/or electron microscopy images, as required for the relevant diagnostic task. 3. Availability of the clinical and laboratory data required for the planned analyses.
Exclusion criteria
1. Renal pathology images of insufficient quality for analysis. 2. Missing key clinical data required for the planned analyses. 3. Kidney allograft biopsy specimens.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic classification accuracy of the deep learning model, measured as the percentage of correctly classified renal pathology cases | At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026. | Diagnostic classification accuracy will be assessed in the independent test set by comparing the renal disease classification predicted by the prespecified deep learning model with the consensus diagnosis established by three senior renal pathologists. Accuracy will be calculated as the number of correctly classified cases divided by the total number of evaluable cases and reported as a percentage (%). The measurement tool will be the prespecified deep learning model evaluated against the expert consensus reference standard. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Agreement between deep learning model predictions and expert consensus diagnoses, measured by the Cohen kappa coefficient | At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026. | Agreement between the renal disease classifications generated by the prespecified deep learning model and the consensus diagnoses established by three senior renal pathologists will be assessed using the Cohen kappa coefficient. The measurement tool will be a prespecified categorical agreement analysis comparing model-predicted classifications with the expert consensus reference standard. The Cohen kappa coefficient is a unitless measure, with higher values indicating greater agreement beyond chance. |
Countries
China
Outcome results
None listed