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Enregistrement W3134902410 · doi:10.1111/bjd.20048

Incidence of mycosis fungoides and Sézary syndrome in the Netherlands between 2000 and 2020

2021· letter· en· W3134902410 sur OpenAlexaboutno aff
Rosanne Ottevanger, Digna T. de Bruin, Rein Willemze, Patty M. Jansen, Marcel W. Bekkenk, E Haas, Barbara Horváth, Michelle M. van Rossum, C.J.G. Sanders, J. C. J. M. Veraart, Maarten H. Vermeer, Koen D. Quint

Notice bibliographique

RevueBritish Journal of Dermatology · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueCutaneous lymphoproliferative disorders research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMycosis fungoidesConceptualizationLibrary scienceMedicineComputer sciencePathology

Résumé

récupéré en direct d'OpenAlex

Dear Editor, Cutaneous T-cell lymphomas (CTCLs) are a heterogeneous group of non-Hodgkin lymphomas that differ greatly in clinical presentation and prognosis. Mycosis fungoides (MF) is the most frequent subtype of cutaneous lymphomas. Sézary syndrome (SS), characterized by the triad of erythroderma, lymphadenopathy and blood involvement, is less prevalent, and has a much more unfavourable prognosis.1 Previous studies have shown that the incidence of CTCL has tripled between 1970 and 2000.2 However, studies from the USA and Canada suggest that the incidence since then has stabilized.2, 3 In 2000 we described a cohort of 309 patients with MF who were included in the Dutch Cutaneous Lymphoma Registry (DCLR) between October 1985 and May 1997.4 However, data on the incidence of MF and SS in the Netherlands have never been published. Given the reported increasing incidence of CTCL, the aim of the present study was to estimate the changes in incidence of newly diagnosed MF and SS in the Netherlands over the last 20 years. Annual incidence rates were retrieved from the DCLR. Between January 2000 and December 2019, 1044 patients with MF, including 238 patients with folliculotropic MF (FMF), and 93 patients with SS were included in the DCLR (Figure 1). In all cases, the diagnosis was based on the clinicopathological criteria of the World Health Organization–European Organisation for Research and Treatment of Cancer classification and confirmed by an expert panel of dermatologists and pathologists at a periodical meeting of the Dutch Cutaneous Lymphoma Working Group.5 Referral centres have remained the same throughout the study period and cover all geographical areas in the Netherlands. A total of 30 patients with MF were diagnosed in the year 2000 and 79 in 2019. This was a 2·6-fold increase in the last two decades, with an average increase of 7·9% (SD 0·222) per year. A 1·9-fold increase was seen between 2000 and 2010. Less increase (1·4-fold) was seen between 2010 and 2019. For patients with SS, two patients were diagnosed in the year 2000 and 13 in 2019. This was a 6·5-fold increase. Furthermore, we calculated the number of registered patients with MF and SS per 100 000 persons, corrected for the size of the Dutch population as registered by the Central Bureau of Statistics.6 In 2000, the corrected number of cases of MF was 0·19 per 100 000 persons, while this was 0·35 per 100 000 in 2010 and 0·46 per 100 000 persons in 2019. This means that a 2·42-fold increase, corrected for Dutch population growth, was seen between 2000 and 2019 for the incidence of MF in the Netherlands. In 2000, 2010 and 2019 the corrected incidences for SS were 0·013, 0·018 and 0·075 per 100 000 persons, respectively. The overall increase was 6·0-fold corrected for the Dutch population between 2000 and 2019. There was no clinically relevant change in the age of diagnosis in classical MF and FMF between the first decade (2000–2009) and second decade (2010–2019). However, in patients with SS, there was a significant difference in age [years (SD)] of diagnosis in the first and second decade [65·2 (10·1) vs. 71·8 (9·9), P = 0·004)]. In short, the number of patients with MF and SS registered in the DCLR in the Netherlands has kept increasing annually over the last two decades, in contrast to previous reports from North America where the incidence stabilized over the last 10 years.2, 3 Several explanations can be given for the rise in the number of patients with MF and SS in the DCLR. The most likely explanation is that dermatologists and pathologists working outside of academic university hospitals are more aware of the Dutch Cutaneous Lymphoma Working Group resulting in more referrals and more inclusions in the DCLR. The diagnostic criteria of MF and SS have not changed over the last 30 years in the Netherlands and offer no explanation for the increased incidence. A true increase in the incidence of MF and SS cannot be excluded completely, but causative factors are unknown. Ghazawi et al. suggest that environmental or industrial exposures contribute to the pathogenesis of CTCL.3 This should be explored in further studies. In summary, a significant increase of patients with classical MF, FMF and SS included in the DCLR was seen over the past 20 years. In contrast to previous studies that suggest a stabilization since 2000, this study shows that the incidence of patients with MF and SS in the Netherlands increased 2·42-fold over the past two decades. This effect is probably caused by the increased awareness of dermatologists working outside of academic university hospitals. Rosanne Ottevanger: Conceptualization (equal); Data curation (lead); Formal analysis (lead); Funding acquisition (supporting); Investigation (supporting); Methodology (lead); Project administration (lead); Resources (equal); Validation (lead); Visualization (lead); Writing-original draft (lead); Writing-review & editing (equal). Digna de Bruin: Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (supporting); Writing-original draft (supporting); Writing-review & editing (supporting). Rein Willemze: Conceptualization (lead); Data curation (lead); Formal analysis (supporting); Funding acquisition (supporting); Investigation (supporting); Methodology (supporting); Project administration (supporting); Resources (equal); Supervision (lead); Visualization (supporting); Writing-review & editing (lead). Patty Jansen: Data curation (equal); Investigation (supporting); Project administration (supporting); Writing-review & editing (equal). Marcel Bekkenk: Data curation (equal); Investigation (supporting); Project administration (supporting); Writing-review & editing (equal). Ellen R.M. de Haas: Data curation (equal); Investigation (supporting); Project administration (supporting); Writing-review & editing (equal). Barbara Horvath: Data curation (equal); Investigation (supporting); Project administration (supporting); Writing-review & editing (equal). Michelle van Rossum: Data curation (equal); Investigation (supporting); Project administration (supporting); Writing-review & editing (equal). cornelus sanders: Data curation (equal); Investigation (supporting); Project administration (supporting); Writing-review & editing (equal). Joep Veraart: Data curation (equal); Investigation (supporting); Project administration (supporting); Writing-review & editing (equal). Maarten H. Vermeer: Conceptualization (equal); Data curation (equal); Formal analysis (supporting); Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Project administration (supporting); Resources (equal); Supervision (lead); Visualization (supporting); Writing-review & editing (lead). Koen D. Quint: Conceptualization (lead); Data curation (equal); Formal analysis (supporting); Funding acquisition (supporting); Investigation (equal); Methodology (equal); Project administration (equal); Resources (equal); Supervision (lead); Visualization (supporting); Writing-original draft (supporting); Writing-review & editing (lead).

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Étude de cas · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,550
Score d'incertitude au seuil0,867

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,280
Écart entre enseignants0,263 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeÉtude de cas
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations24
Publié2021
Routes d'admission1
Résumé présentoui

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