The Application of Digital Pathology to Improve Accuracy in Glomerular Enumeration in Renal Biopsies
Bibliographic record
Abstract
BACKGROUND: In renal biopsy reporting, quantitative measurements, such as glomerular number and percentage of globally sclerotic glomeruli, is central to diagnostic accuracy and prognosis. The aim of this study is to determine the number of glomeruli and percent globally sclerotic in renal biopsies by means of registration of serial tissue sections and manual enumeration, compared to the numbers in pathology reports from routine light microscopic assessment. DESIGN: We reviewed 277 biopsies from the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository, enumerating 9,379 glomeruli by means of whole slide imaging. Glomerular number and the percentage of globally sclerotic glomeruli are values routinely recorded in the official renal biopsy pathology report from the 25 participating centers. Two general trends in reporting were noted: total number per biopsy or average number per level/section. Both of these approaches were assessed for their accuracy in comparison to the analogous numbers of annotated glomeruli on WSI. RESULTS: The number of glomeruli annotated was consistently higher than those reported (p<0.001); this difference was proportional to the number of glomeruli. In contrast, percent globally sclerotic were similar when calculated on total glomeruli, but greater in FSGS when calculated on average number of glomeruli (p<0.01). The difference in percent globally sclerotic between annotated and those recorded in pathology reports was significant when global sclerosis is greater than 40%. CONCLUSIONS: Although glass slides were not available for direct comparison to whole slide image annotation, this study indicates that routine manual light microscopy assessment of number of glomeruli is inaccurate, and the magnitude of this error is proportional to the total number of 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 | 0.046 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".