Incidence of mycosis fungoides and Sézary syndrome in the Netherlands between 2000 and 2020
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".