Use of a Tissue Microarray To Assess the Accuracy of Lymphoma Classification by Needle Biopsy.
Notice bibliographique
Résumé
Abstract Appropriate management of lymphoma patients relies on accurate pathological diagnosis. Pathological criteria, such as those outlined in the WHO classification, are based on relatively large, excisional biopsies. However, pathologists are increasingly asked to render definitive diagnoses based on much smaller, needle biopsy specimens. To the best of our knowledge, the accuracy of lymphoma diagnoses based on needle biopsies relative to “gold standard” excisional biopsies has not been formally determined. In this study, the core samples in a tissue microarray (TMA) were used as needle biopsy surrogates in order to determine the accuracy of diagnoses based on small samples. The array contained randomly arranged pairs of 0.6 mm tissue cores punched from paraffin-embedded, excisional specimens. Slides from the original specimens were reviewed and these diagnoses served as a gold standard. Represented on the TMA were lymphoid hyperplasia (LH, 17 cases), classical Hodgkin lymphoma (cHL, 13 cases), marginal zone lymphoma (MZL, 11 cases), diffuse large B-cell lymphoma (DLBCL, 9 cases), grade 1 or 2 follicular lymphoma (FL, 7 cases), grade 3 follicular lymphoma (FL3, 3 cases), peripheral T-cell lymphoma (PTCL, 7 cases), mantle cell lymphoma (MCL, 6 cases), B-chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL, 6 cases), T-lymphoblastic lymphoma (TLL, 3 cases), nodular lymphocyte predominance Hodgkin lymphoma (NLPHL, 1 case) and anaplastic large cell lymphoma (ALCL, 1 case). TMA sections were stained for morphology and 16 immunomarkers used routinely in lymphoma diagnosis. Three pathologists evaluated the sections independently in a “blinded” manner. They recorded their preferred diagnoses and assigned a “confidence score”, from 0 to 3, with 3 indicating a diagnosis upon which clinical decisions might reliably be based. The results were analyzed based on 252 (i.e., 3 X 84) pathologist-case encounters. A confidence score of 3 was assigned in 118 cases (47%). Not surprisingly, diagnostic accuracy increased with increasing confidence scores: confidence 0, 0% (0/3 cases); confidence 1, 65% (35/54 cases); confidence 2, 79% (61/77 cases); and confidence 3, 92% (108/118 cases). Nine of the 10 errors made at “confidence 3” involved misclassification of lymphomas (DLBCL, ALCL, or MZL misclassified as cHL, 4 cases; MZL misclassified as CLL, 3 cases; FL misclassified as DLBCL, 1 case; and PTCL misclassified as DLBCL, 1 case); the tenth error involved misclassifying MZL as LH. Accurate, “confidence 3” diagnoses were most consistently rendered in TLL, 89% (8/9 cases); MCL, 67% (12/18 cases); cHL, 62% (24/39 cases); and, CLL, 56% (10/18 cases). Conversely, “confidence 3” diagnoses on TMA cores were always accurate in MCL (12 cases), FL, TLL, and PTCL (8 cases each). MZL was diagnostically most problematic. No case of (benign) LH was misdiagnosed as lymphoma at level 3 confidence, although 3 cases were misclassified at level 2 as DLBCL, FL or MZL. Our findings indicate that the pathological diagnosis and classification of lymphomas may be accomplished in a substantial subset of cases using tissue samples of sizes smaller than those typically obtained by needle core biopsy. Accuracy is most reliably achieved in lymphoma types associated with characteristic cytological appearances or immunophenotypic profiles.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».