Clinical Relevance of Computationally Derived Attributes of Arteries and Arterioles in focal segmental glomerulosclerosis and minimal change disease
Bibliographic record
Abstract
Background: The current semi-qualitative methods used to score sclerosis and hyalinosis in arteries and arterioles in clinical practice are limited in standardization and reproducibility. We developed a computational pipeline designed to accurately and consistently quantify prognostic arterial and arteriolar characteristics in digital kidney biopsies of patients with focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) through segmentation and pathomic feature extraction. Methods: We utilized one trichrome-stained WSI from 225 participants in the NEPTUNE/CureGN studies, comprising 127 cases of focal segmental glomerulosclerosis (FSGS) and 98 cases of minimal change disease (MCD). We developed, validated, and quality-controlled deep learning models to segment muscular vessels and their internal compartments (lumen, intima, media, and hyalinosis), including (i) arcuate arteries, (ii) interlobular arteries, and (iii) arterioles with two muscle layers. Arterioles, interlobular, and arcuate arteries were visually scored for sclerosis and hyalinosis on a scale of 0 to 3. Area- and thickness-based pathomic feature extraction was performed on each compartment (lumen, intima, media, and hyalinosis) through radial sampling and ray casting. A correlation study was performed between pathomic and visual semiquantitative visual scores, and the association of both visual scores and pathomic features with disease progression (40% eGFR decline or renal failure) was assessed. Summary statistics (maximum, median, and 75th percentile) were computed for each WSI and analyzed using LASSO-regularized Cox proportional hazards models, adjusted for clinical and demographic factors. Results: A total of 1,499 arterioles, 686 interlobular arteries, and 131 arcuate arteries were segmented. Statistically significant correlations were found between pathologists visual scores and the average intima-media thickness ratio (Spearman ρ = 0.27, p < 0.001 for arterioles; ρ = 0.69, p < 0.001 for interlobular arteries; and ρ = 0.80, p < 0.001 for arcuate arteries) and arteriolar hyalinosis (ρ = 0.46, p < 0.001). Incorporating pathomic features from trichrome-stained WSIs improved the prediction of disease progression, enhancing the concordance index from 0.70 to 0.75 in arterioles and from 0.69 to 0.74 in arcuate arteries, compared to using demographics and clinical characteristics alone. Conclusion: Our computational approach offers a novel and reliable method for segmenting and analyzing the pathomic features of sclerosis and hylalinosis in arteries and arterioles. This technique has demonstrated potential as a valuable tool for enhancing the clinical assessment performed by pathologists.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".