Using Molecular Phenotyping to Guide Improvements in the Histologic Diagnosis of T Cell–Mediated Rejection
Bibliographic record
Abstract
Recognition that some lesions typical of T cell–mediated rejection (TCMR) also occur in antibody-mediated rejection requires revision of the histologic TCMR definition. To guide this process, we assessed the relative importance of various lesions and the performance of new histology diagnostic algorithms, using molecular TCMR scores as histology-independent estimates of true TCMR. In 703 indication biopsies, random forest analysis and logistic regression indicated that interstitial infiltrate (i-lesions) and tubulitis (t-lesions) were the key histologic predictors of molecular TCMR, with arteritis (v-lesions) having less importance. Histology predicted molecular TCMR more accurately when diagnoses were assigned by strictly applying the Banff rules to the lesion scores and redefining isolated v-lesion TCMR. This improved prediction from area under the curve (AUC) 0.70 with existing rules to AUC 0.80. Further improvements were achieved by introducing more categories to reflect inflammation (AUC 0.84), by summing the lesion scores (AUC 0.85) and by logistic regression (AUC 0.90). We concluded that histologic assessment of TCMR can be improved by placing more emphasis on i- and t-lesions and incorporating new algorithms for diagnosis. Nevertheless, some discrepancies between histologic and molecular diagnoses persist, partially due to the inherent nonspecificity of i- and t-lesions, and molecular methods will be required to help resolve these cases. Recognition that some lesions typical of T cell–mediated rejection (TCMR) also occur in antibody-mediated rejection requires revision of the histologic TCMR definition. To guide this process, we assessed the relative importance of various lesions and the performance of new histology diagnostic algorithms, using molecular TCMR scores as histology-independent estimates of true TCMR. In 703 indication biopsies, random forest analysis and logistic regression indicated that interstitial infiltrate (i-lesions) and tubulitis (t-lesions) were the key histologic predictors of molecular TCMR, with arteritis (v-lesions) having less importance. Histology predicted molecular TCMR more accurately when diagnoses were assigned by strictly applying the Banff rules to the lesion scores and redefining isolated v-lesion TCMR. This improved prediction from area under the curve (AUC) 0.70 with existing rules to AUC 0.80. Further improvements were achieved by introducing more categories to reflect inflammation (AUC 0.84), by summing the lesion scores (AUC 0.85) and by logistic regression (AUC 0.90). We concluded that histologic assessment of TCMR can be improved by placing more emphasis on i- and t-lesions and incorporating new algorithms for diagnosis. Nevertheless, some discrepancies between histologic and molecular diagnoses persist, partially due to the inherent nonspecificity of i- and t-lesions, and molecular methods will be required to help resolve these cases.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".