Development of a Tissue-Based Classifier of Allograft Inflammation Using Imaging Mass Cytometry
Bibliographic record
Abstract
Background: Molecular phenotyping of allograft inflammation has improved both diagnostic accuracy & our understanding of the heterogeneity of rejection. Current molecular techniques lack histological correlation & spatial dimensionality. Our goal was to use Imaging Mass Cytometry (IMC) to develop a tool to accurately predict the cause of allograft inflammation. Methods: Our cohort included biopsies of rejection, BK nephropathy, pyelonephritis & normal kidneys. Using a panel of 28 markers, IMC images were processed by the Hyperion imaging system. Details of analysis are in Fig 1A. Cell segmentation was performed using Universal StarDist for Qupath. Cell classification was performed based on mean intensity threshold.Figure 1Results: 139 regions of interest (ROI) were processed. Violin plots ensured there were measurable differences in known markers associated with each allograft inflammation category. Distribution of percent positive scoring of immune cells are seen in the heatmap (1B) [e.g.: cellular & mixed rejection cases enriched in CD45+, HLA-DR+ cells and CD4+ memory T cells]. The trained regularized gradient boosting classifier model XGBoost was used to predict the allograft inflammation category for all cells, ROIs and each original histological diagnosis. (Fig 1C &D). The trained model accurately predicted the allograft inflammation category for each cell with an accuracy of 64.3%. When using the mean intensity parameter of each cell, the classifier accuracy improved to 87.8% in predicting the type of renal allograft inflammation, independent of ROI. The accuracy improved to 90.9% when dimension of intracellular spatial features (proximity metrics) were added to the algorithm. Granzyme, CD68 and Vista were the three most important markers in achieving this high accuracy. Conclusions: Using highly multiplexed imaging of renal allograft biopsies with subcellular resolution by IMC we have developed a novel classifier of allograft inflammation, which demonstrates high diagnostic accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".