Glomerular Segmentation, Classification, and Pathomic Feature-based Prediction of Clinical Outcomes in Minimal Change Disease and Focal Segmental Glomerulosclerosis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".