COVID-19 in pediatric cancer: Where are the brain tumors?
Bibliographic record
Abstract
Recently, Carai et al published a report highlighting the risk of delayed diagnosis of pediatric central nervous system (CNS) tumors during the novel coronavirus disease 2/SARS-CoV-2 2019 (COVID-19) virus pandemic.1 In this article and others,2 the authors highlight the fragility of pediatric patients with CNS tumors and the importance of early diagnosis and optimal care delivery, which may have been impacted during the pandemic. CNS tumors are the largest group of solid tumors within pediatric oncology patients, accounting for 20%-25% of all childhood cancers. Treatment is variable in its intensity and potential for immunosuppression primarily depends on the tumor type. The risk of COVID-19 to this tumor group is not clear from current publications and here we reviewed the literature to identify pediatric CNS tumors patients reported to have COVID-19 and their outcomes. Through review of PubMed, Google Scholar, and the International Society for Pediatric Oncology (SIOP) and St. Jude Children’s Research Hospital COVID-19 website, we identified 18 published studies describing COVID-19 cases in pediatric oncology patients worldwide (Table 1). Within these studies, there are 551 reported cases of COVID-19 in pediatric oncology patients of which 31 (5.6%) are patients with CNS tumors. Within all cases, reported rate of severe illness or hospitalization requirement ranged from 5% to 84%. Within these hospitalized cases, 40%-86% were already hospitalized for planned oncologic therapy. Within the 551 total cases, there were 26 deaths, of which 6 were attributed to COVID-19, and the remaining 20 the authors attributed to cancer or oncologic therapy-related mortality. No deaths were reported within the patients with brain tumors in these publications. Reported COVID-19 Infections in Pediatric Oncology Patients Abbreviations: B-ALL, B-cell acute lymphoblastic leukemia; CNS, central nervous system; HSCT, hematopoietic stem cell transplantation; ICU, intensive care unit. aSix adult patients were included in this cohort. The authors do not specify whether these patients had brain tumors. bDuplicate cases within. cSee Supplementary Material. Reported COVID-19 Infections in Pediatric Oncology Patients Abbreviations: B-ALL, B-cell acute lymphoblastic leukemia; CNS, central nervous system; HSCT, hematopoietic stem cell transplantation; ICU, intensive care unit. aSix adult patients were included in this cohort. The authors do not specify whether these patients had brain tumors. bDuplicate cases within. cSee Supplementary Material. Based on these publications, children with cancer do not seem to have a higher rate of mortality from COVID-19 than immunocompetent children. This finding is difficult to fully understand in this population with such significant comorbidities, which may independently result in death. The mortality rate from COVID-19 for these combined reports was 1.1%. Recently, Vijenthira et al summarized 34 reports that included predominantly adult oncology patients with concurrent COVID-19 infections. A small proportion of these patients were children in which the rate of mortality attributed to COVID-19 was 4.0%.3 CNS tumors are significantly underrepresented within published cases of COVID-19 in children with cancer. The reasons for this are unclear. This may reflect that most therapies for CNS tumor patients are not significantly myelosuppressive with hospital admissions due to complications occurring less often than in other pediatric oncology patients.4 To support this hypothesis, medulloblastoma, which is usually treated with inpatient chemotherapy, is the most common tumor within patients with comorbid COVID-19. Conversely, there are no reported cases of COVID-19 in children with low-grade gliomas in the current literature. This finding may also reflect hospital COVID-19 testing practices which identify more asymptomatic patients who are admitted for scheduled oncology therapy. Within CNS tumors in the studies included in this review, there were no COVID-19-related deaths reported. Steroids are a mainstay of therapy for severe COVID-19 pneumonitis and improve respiratory symptoms in infected patients. It is not clear whether the frequent use of concurrent steroid therapy in patients with CNS tumors may have an impact on rates and severity of COVID-19 illness. No denominator exists with which to assess the proportion of pediatric patients with CNS tumors within the authors’ practices or specific country. As well, published data may not reflect global COVID-19 cases and the influence of the ongoing global vaccination program on COVID-19 in pediatric oncology patients is not known. SIOP, in collaboration with St. Jude Children’s Research Hospital, has developed a global registry of cases of laboratory-confirmed COVID-19 in children with cancer, and analysis of this data is currently underway.5 Early observations from this registry suggest a higher proportion of asymptomatic patients in the CNS tumor population compared to other malignancies (St. Jude Registry, accessed: May 22, 2021). This registry may provide more comprehensive, large-scale data describing COVID-19 in oncology patients, which is needed to understand the rates and risk of infection in this population. Furthermore, more robust clinical data will allow us to gain insight into the effect on diagnosis and delivery of oncology care. Conflict of interest statement. The authors have no conflicts of interest to disclose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.059 | 0.050 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".