Molecular diagnosis of kidney allograft rejection based on the Banff Human Organ Transplant gene panel: A multicenter international study
Bibliographic record
Abstract
Transcriptomic analysis of kidney biopsies has demonstrated the potential to improve diagnosis of allograft rejection. Here, we developed a molecular assessment of antibody-mediated rejection (AMR) and T cell-mediated rejection (TCMR) based on the Banff Human Organ Transplant consensus gene panel. Expression assays of formalin-fixed paraffin-embedded kidney biopsies from well-phenotyped cohorts were used to develop prediction models for AMR and TCMR and an automated report of gene expression-based diagnosis. The study population consisted of 950 kidney allograft biopsies from 10 transplantation centers in Europe and North America. The development cohort included 664 renal allograft biopsies split into a training (n = 537) and test set (n = 127), and 2 external validation cohorts (n = 286). We performed gene selection using regularized regression and developed several different base models based on Banff Human Organ Transplant expression data, which were combined into a single ensemble model for each rejection diagnosis. Model performance was assessed in the test set and the 2 external validation cohorts, showing good discriminative abilities (respective areas under the precision-recall curve: AMR = 0.811, 0.891, and 0.832 and TCMR = 0.736, 0.810, and 0.782). We identified challenging biopsies with histology below diagnostic thresholds for which gene expression-based probability can refine rejection diagnosis. This automated molecular diagnostic system shows potential for improving kidney allograft rejection diagnosis in routine practice and clinical trials.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".