Classification of Non-Hodgkin Lymphoma in Seven Geographic Regions Around the World: Review of 4539 Cases from the International Non-Hodgkin Lymphoma Classification Project
Notice bibliographique
Résumé
Abstract INTRODUCTION The distribution of non-Hodgkin lymphoma (NHL) subtypes varies around the world, but a large and systematic comparative study has not been done. This study is first to evaluate the relative frequencies of NHL subtypes in seven regions of the world. METHODS Five expert hematopathologists classified 4848 consecutive cases of NHL from 25 countries in seven regions, including North America, Central/South America, Western Europe, Southeastern Europe, Southern Africa, the Middle East/North Africa, and the Far East, using the WHO classification. Data from the developed world (North America and Western Europe) was compared to the developing world (all other regions combined). RESULTS Among the 4848 cases reviewed, 4539 (93.6%) were confirmed to be NHL, whereas the other 309 (6.4%) had diagnoses other than NHL and were excluded from further analysis. A significantly higher male to female ratio was found in the developing regions (1.4:1) compared to the developed world (1:1; p<0.05). The median age at diagnosis was significantly lower for both low grade (LG) and high grade (HG) B-NHL in the developing regions (59 and 54 yrs, respectively) compared to the developed world (62 and 64 yrs, respectively). The developing regions had a significantly lower frequency of B-NHL (86.6%) and a higher frequency of T-NHL (13.4%) compared to the developed world (90.7% and 9.3%, respectively). Furthermore, the developing regions had significantly more cases of HG B-NHL (58.7%) compared to the developed world (43.9%). Among B-cell lymphomas, diffuse large B-cell lymphoma (42.5%) and Burkitt lymphoma (2.2%) were significantly more common in the developing regions, compared to the developed world (28.9% and 0.8%, respectively). Follicular lymphoma (15.3%), mantle cell lymphoma (3.8%), marginal zone lymphoma of mucosa-associated lymphoid tissue (5.2%), and lymphoplasmacytic lymphoma (0.3%) were significantly less common in the developing regions, compared to the developed world (25.5%, 7.8%, 8.8%, and 1.4%, respectively). Among T-cell lymphomas, precursor T-lymphoblastic lymphoma (2.9%) and nasal NK/T-cell lymphoma (2.2%) were more common in the developing regions, compared to the developed world (1.3% and 0.3%, respectively). CONCLUSION Our study is the first to systematically compare the relative frequencies of NHL subtypes in different regions around the world, and provides new evidence of significant geographic differences. Our findings suggest that differences in etiologic and/or host risk factors are likely responsible, and more detailed epidemiologic studies are needed to better understand these differences. Disclosures Armitage: Celgene: Consultancy; Ziopharm: Consultancy; Tesaro Bio, Inc: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees; Conatus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Spectrum: Consultancy.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».