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Enregistrement W3135188269 · doi:10.1093/neuonc/noab064

Brainstem gliomas … the devil is in the details

2021· letter· en· W3135188269 sur OpenAlexaff
Adriana Fonseca, Éric Bouffet

Notice bibliographique

RevueNeuro-Oncology · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueGlioma Diagnosis and Treatment
Établissements canadiensSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésBrainstemMedicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

See the article by Patil et al. in this issue, pp. 990–998. Brainstem high-grade gliomas (HCG) account for approximately 10-15% of childhood CNS tumors.1 However, due to their invasion to eloquent structures, histological confirmation has been routinely avoided, and clinical and radiological characteristics have been used for diagnosis. Recent molecular studies unveiled a significant biological heterogeneity among them and demonstrated the prognostic significance of different driver mutations in this population2 thus, explaining the prolonged survival reported in of some patients3 in an otherwise lethal disease. In this issue of Neuro-Oncology, Patil et al.4 provide an overview of the epidemiological landscape of children and adolescents diagnosed with brainstem gliomas in the United States using a large population-based cohort assembled using the Central Brain Tumor Registry of the United States (CBTRUS) and NCI Surveillance, Epidemiology and End Results (SEER) cancer registry over a 17-year period. In a laudable effort, the authors provide comprehensive epidemiological characterization powered by the large number of patients. A total of 4486 patients were included in this study, making it the largest cohort of children and adolescents with brainstem gliomas to date. The overall age-adjusted incidence rate (AAIR) was estimated to be 0.305 per 100 000 population and identified a higher incidence in white children (0.23) and children between the ages of 0-4 and 5-9 years (0.38 and 0.5 × 100 000, respectively). The incidence rates are equivalent to smaller cohorts previously reported in the United States5 although it compares slightly higher than a recent report from the Canadian Pediatric Brain Tumor Consortium (CPBTC) that only included patients with diffuse intrinsic pontine glioma (DIPG) after careful central radiological review. Suggesting the inclusion of non-DIPG in the present study may account for the slightly higher incidence rate.6 This study highlights the historical scarcity of biological samples in brainstem gliomas and its contribution to our poor understanding of the molecular underpinnings and the paucity of accurate epidemiological estimates of this entity. In this study, 70% of the study population lacked histological confirmation,4 leading to the inevitable inclusion of other pathologies and therefore encumbering the interpretation of the results. Furthermore, this study provides an impetus to establish stereotactic biopsies and tissue diagnosis as the standard of care for proper classification and treatment of brainstem gliomas. Accounting for 80% of childhood brainstem tumors, malignant midline gliomas frequently harbor hotspot point mutations in the histone variants H3.1 and H3.32 and remain a disheartening fatal diagnosis. Recent studies demonstrated that brainstem gliomas encompass different entities with distinct oncogenic mechanisms, clinical characteristics, and disparate outcomes.7 Due to the inability to account for the biological heterogeneity of brainstem HCG in the present study, the survival outcomes may seem overestimated and require careful interpretation. Another important confounder of this population-based analysis is the need to rely on the International Classification of Diseases for Oncology, third edition (ICD-O-3) coding, to identify the study subjects. In combination with the absence of histological confirmation, the probable inclusion of low-grade gliomas located in the brainstem may explain the unexpectedly high survival of the entire cohort and particularly in the subgroup of patients diagnosed by radiological criteria. In contrast, the survival of patients with histologically proven HGG is akin to the results as previously described in other registry studies3 and prospective clinical trials.8 Thus, the survival presented illustrates the summation of wide spectrum of diseases as depicted in Figure 1. Schema illustrating some of the different entities among brainstem gliomas, clinical characteristics, most frequent alterations, and overall survival outcomes. Schema illustrating some of the different entities among brainstem gliomas, clinical characteristics, most frequent alterations, and overall survival outcomes. Notably, the identification of age as a prognostic factor is presumably a surrogate for biology as the majority of patients with histone mutated tumors present between 1-9 years of age. Furthermore, the superb survival reported in children younger than 1 year could represent the outcome of low-grade gliomas in which higher mitotic figures are frequently observed and often histologically “upgraded” 9 or the unique biological behavior of infantile gliomas.10 The true epidemiology of brainstem gliomas remains to be elucidated and will require the centralized selection of cases and the incorporation of biological markers for accurate estimation. Disease-specific registries such as the International Diffuse Intrinsic Glioma Registry (IDPGR) (https://dipgregistry.org/) and the European Society for Pediatric Oncology (SIOPE) DIPG network registry (https://www.dipgregistry.eu/) have modeled the acquisition and curation of clinical, radiological, and biological data, and aim to improving the veracity of the information collected and providing reliable estimations. Notwithstanding, the retrospective nature of these registries has its own set of limitations. Patil et al.4 lay a foundation with this remarkable study and highlight the overwhelming importance of incorporating biological data into population-based dataset for proper disease characterization as we evolve into a molecularly driven classification of maladies. AF is supported by the WE LOVE YOU CONNIE FOUNDATION. The text is the sole product of the authors and no third party had input or gave support to its writing.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,029
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,009
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0040,002
Communication savante0,0030,003
Science ouverte0,0010,001
Intégrité de la recherche0,0290,029
Charge utile insuffisante (le modèle a refusé de juger)0,0090,004

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 Opus0,041
Tête enseignante GPT0,305
Écart entre enseignants0,264 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations5
Publié2021
Routes d'admission1
Résumé présentnon

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