Diagnosing Rejection in Renal Transplants: A Comparison of Molecular- and Histopathology-Based Approaches
Notice bibliographique
Résumé
The transcriptome has considerable potential for improving biopsy diagnoses. However, to realize this potential the relationship between the molecular phenotype of disease and histopathology must be established. We assessed 186 consecutive clinically indicated kidney transplant biopsies using microarrays, and built a classifier to distinguish rejection from nonrejection using predictive analysis of microarrays (PAM). Most genes selected by PAM were interferonγ—inducible or cytotoxic T-cell associated, for example, CXCL9, CXCL11, GBP1 and INDO. We then compared the PAM diagnoses to those from histopathology, which are based on the Banff diagnostic criteria. Disagreement occurred in approximately 20% of diagnoses, principally because of idiosyncratic limitations in the histopathology scoring system. The problematic diagnosis of ‘borderline rejection’ was resolved by PAM into two distinct classes, rejection and nonrejection. The diagnostic discrepancies between Banff and PAM in these cases were largely due to the Banff system's requirement for a tubulitis threshold in defining rejection. By examining the discrepancies between gene expression and histopathology, we provide external validation of the main features of the histopathology diagnostic criteria (the Banff consensus system), recommend improvements and outline a pathway for introducing molecular measurements. The transcriptome has considerable potential for improving biopsy diagnoses. However, to realize this potential the relationship between the molecular phenotype of disease and histopathology must be established. We assessed 186 consecutive clinically indicated kidney transplant biopsies using microarrays, and built a classifier to distinguish rejection from nonrejection using predictive analysis of microarrays (PAM). Most genes selected by PAM were interferonγ—inducible or cytotoxic T-cell associated, for example, CXCL9, CXCL11, GBP1 and INDO. We then compared the PAM diagnoses to those from histopathology, which are based on the Banff diagnostic criteria. Disagreement occurred in approximately 20% of diagnoses, principally because of idiosyncratic limitations in the histopathology scoring system. The problematic diagnosis of ‘borderline rejection’ was resolved by PAM into two distinct classes, rejection and nonrejection. The diagnostic discrepancies between Banff and PAM in these cases were largely due to the Banff system's requirement for a tubulitis threshold in defining rejection. By examining the discrepancies between gene expression and histopathology, we provide external validation of the main features of the histopathology diagnostic criteria (the Banff consensus system), recommend improvements and outline a pathway for introducing molecular measurements.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».