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OBJECTIVES: Still's disease is a rare autoinflammatory disorder characterized by systemic inflammation, fever, rash, and arthritis. The term "Still's disease" covers the pediatric subtype systemic Juvenile Idiopathic Arthritis (sJIA) and adult-onset Still's disease (AOSD), which affects adults. Biological drugs, including anti-interleukin-1 agents anakinra, canakinumab, rilonacept, and the interleukin-6 antagonist tocilizumab, are used in the management of Still's disease. METHODS: We conducted a systematic review and network meta-analysis of randomized controlled trials, and the study protocol was registered in PROSPERO (CRD42023450442). MEDLINE, EMBASE, and CENTRAL were screened from inception until September 17, 2023. We included patients with Still's disease who received placebo or biological drugs: anakinra, canakinumab, rilonacept, or tocilizumab. The primary efficacy and safety outcomes were achievement of ACR50 response and occurrence of serious adverse events, respectively. The interventions were ranked using rankograms and SUCRA values. RESULTS: Nine trials with 430 patients were included. All biological drugs were associated with greater odds of ACR50 response compared with placebo. There was no statistically significant association between biological drugs and serious adverse events. The multivariate meta-analysis found no difference between biological drugs. As per SUCRA rankings, anakinra was the most effective and safe option with respect to ACR50 response and occurrence of serious adverse events. CONCLUSION: This is the first systematic review and meta-analysis to assess the efficacy and safety of biological drugs in pediatric and adult patients with Still's disease. Biological drugs were effective in achieving ACR response and demonstrated a low adverse event profile in the management of Still's disease.
RESUMO
BACKGROUND: Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. PURPOSE: To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. MATERIAL AND METHODS: A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. RESULTS: Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. CONCLUSION: Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.
