Opsoclonus-Myoclonus-Ataxia Syndrome in Children: Clinical Characteristics and Treatment Response

2016 ◽  
Vol 24 (4) ◽  
pp. 251-256
Author(s):  
조재소 ◽  
김수연 ◽  
최선아 ◽  
채종희 ◽  
임병찬 ◽  
...  
2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1210.2-1210
Author(s):  
S. Zheng ◽  
P. Y. Lee ◽  
Y. Huang ◽  
Q. Huang ◽  
S. Chen ◽  
...  

Background:The incidence of juvenile gout is increasing in China. The clinical manifestations of juvenile gout and treatment strategies to reduce uric acid levels in children are not well described due to the limited number of cases in the past.Objectives:We aim to describe the clinical characteristic of children with gout and study the treatment response to febuxostat.Methods:These studies were approved by the Institutional Review Board of Guangdong Second provincial General Hospital. We performed a retrospective analysis on 98 juvenile gout patients (age ≤ 18 years) evaluated in our hospital from Jan 2016 to Dec 2019. We analyzed clinical parameters, laboratory data and treatment response.Results:The average age of disease onset in children with gout was 15.2 ± 2.0 years and the youngest patient was 9 years old. The majority of patients were male (94/98) and mean serum uric acid (sUA) level were 705.8 ± 145.7 μmol/L (reference range <420 μmol/L). More than half of the cohort had normal body mass index (mean 24.7 ± 4.7 kg/m2; range 14.9 to 36.1 kg/m2). Renal function was generally normal in these children (serum creatinine 96.9 ± 17.8 μmol/L). In terms of joint manifestations, juvenile gout preferentially affected finger joints (29%), ankles (28%) and metatarsal joints (MTP; 20%). The most frequent sites of initial gout attack were ankles (45%), MTP (39%) and fingers (6%). In addition, tophi can occur in pediatric patients and typically develop in the finger joints (54%). Tophi was observed in about 25% of juvenile gout patients, typically within the first two years of disease onset (mean duration 1.7 ± 0. 9 years). We have found tophi in children as young as 10 years of age.For treatment for chronic hyperuricemia, 32 patients (32.7%) were started on febuxostat and 5 patients (5.1%) received allopurinol. A decrease in sUA was observed in both groups after the first month of treatment (febuxostat: baseline 690.4 ± 99.7 μmol/L to 482.7 ± 140.8 μmol/L vs. allopurinol: baseline 728.8 ±112.8 μmol/L to 565.0 ± 116.7 μmol/L, P=0.477). Serum uric acid of 6 patients in the febuxostat group (none in the allopurinol group) dropped below 360 μmol/L. There were no statistical differences in Cr, AST and ALT between the groups. During follow-up after 3 months, further decline in sUA level were observed in patients treated with febuxostat (409.5 ± 83.4, compared with baseline P<0.001).Conclusion:Juvenile gout has a different pattern of joint involvement and is less associated with elevated BMI compared to gout in adults. We show that febuxostat is effective in reducing uric acid levels in juvenile gout. These findings will help clinicians better understand the clinical manifestations and treatment response in juvenile gout.Figure 1Compared treatment response with allopurinol and febuxostatReferences:[1]Kishimoto K, Kobayashi R, Hori D, et al. Febuxostat as a Prophylaxis for Tumor Lysis Syndrome in Children with Hematological Malignancies. Anticancer Res. 2017 Oct;37(10):5845-5849.[2]Lu, C.C., et al. Clinical characteristics of and relationship between metabolic components and renal function among patients with early-onset juvenile tophaceous gout. J Rheumatol, 2014. 41(9): p. 1878-83.Disclosure of Interests:None declared


2011 ◽  
Vol 26 (2) ◽  
pp. 96-106 ◽  
Author(s):  
Robert M. Post ◽  
Gabriele S. Leverich ◽  
Lori L. Altshuler ◽  
Mark A. Frye ◽  
Trisha Suppes ◽  
...  

1994 ◽  
Vol 31 (2) ◽  
pp. 97-109 ◽  
Author(s):  
Sheri L. Johnson ◽  
Scott Monroe ◽  
Anne Simons ◽  
Michael E. Thase

2015 ◽  
Vol 30 (11) ◽  
pp. 1440-1447 ◽  
Author(s):  
Sheffali Gulati ◽  
Puneet Jain ◽  
Lakshminarayanan Kannan ◽  
Rachna Sehgal ◽  
Biswaroop Chakrabarty

2020 ◽  
Vol 145 (2) ◽  
pp. AB138
Author(s):  
Bruce Lanser ◽  
Stephanie Leonard ◽  
Noelle Griffin ◽  
Andrea Vereda ◽  
Alex Smith ◽  
...  

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2671-2671
Author(s):  
Vit Prochazka ◽  
Tomas Papajik ◽  
Patrik Flodr ◽  
Pavla Latalova ◽  
Zuzana Prouzova ◽  
...  

Abstract Abstract 2671 Introduction: Primary mediastinal diffuse large B-cell lymphoma (PMBCL) is an uncommon disease with an aggressive clinical course and potential curability. Growing evidence suggests that host antitumor immunity suppression may play a role in resistant cases. The most studied candidate molecules are ligands PD-L1 (CD 274) and PD-L2 (CD 273) expressed by lymphoma cells, which effectively suppress host T cells. The PD ligand genes are located on chromosome 9p24.1 close to Janus kinase 2 (JAK2) gene. The clinical impact of PD-L1/PD-L2 protein expression has not been described in PMBCL. Methods: Tumor samples of 27 previously untreated patients were analyzed. Clinical characteristics were as follows: median age at diagnosis 35 years (20–74), female-to-male ratio 1.7:1, most patients (70%) had limited mediastinal disease and a mean tumor diameter of 10.7 cm. The IPI and aaIPI scores were low in 67% and 37%, intermediate-low in 26% and 41% and intermediate-high in 7% and 22%, respectively. No patients were assigned to a high risk group. All patients were treated with anthracycline-based chemotherapy 15% with CHOP and 85% with third-generation intensive regimen. Therapy was intensified in 70% of the cases with high-dose therapy and autologous stem cell transplantation. Most of the patients (70%) received rituximab and 15% were also treated with IF radiotherapy. Formalin-fixed and paraffin-embedded tissues were processed in routine tissue sections (approx. 5 micrometers) and placed on plus slides. After antigen retrieval with the use of the enzymatic or microwave oven processes, indirect immunohistochemistry with commercially available primary antibodies in optimized dilution was performed: CD20 clone L27, CD23 clone 1B12, CD30 clone Ber-H2, CD10 clone G27-P, Bcl-2 clone 100, Bcl-6 clone PGB6p, MUM1/IRF4 clone MUM1p, CD274 polyclonal, CD273 polyclonal, and HLADR clone TAL.1B5. For visualization, a secondary antibody with the standard avidin-biotin (ABC) method was applied. Results were expressed as a percentage of positive tumor cells and H-score (product of percentage of positive cells and staining intensity). Cytogenetic analysis with a locus-specific FISH probe (9p24) and arrayCGH was carried out in 15 (56%) of the patients. Results: Final treatment response was assessed in 26 (96%) patients (1 patient did not passed restaging procedures yet), CR was achieved in 22 (85%), PR in three and one patient progressed. After a median follow-up of 73 months (6.1 yrs), 22/26 (84%) patients are alive in the 1st CR, and only three patients died. Five-year PFS was 82.6% (95% CI 0.67–0.98) and five-year overall survival was 90.9% (95% CI 0.79–1.00). All samples expressed PD-L2 in (a median of) 80% of tumor cells with a median H-score of 90. PD-L2 protein expression was very low - six cases were negative and in positive cases, median expression was only 5% (H-score 5). HLA-DR expression was detected in all cases with a median positivity of 70% (H-score 140). Cytogenetic analysis detected amplification of 9p24.1 in 8/15 (53%) of the cases. When analyzing clinical characteristics, only correlation of high HLA-DR expression with limited clinical stage (p=0.04) and low IPI (p<0.01) was found. There was no correlation between treatment response quality and HLA-DR or PD-L2 expression, but high PD-L1 expression (above the median) correlated with non-CR status after treatment (p=0.07). Due to a low number of relapses, there was no relationship between protein expression and survival. No difference was found between cases with or without JAK-2 copy gain in terms of PD-1L expression (71% vs. 73%, p=0.92) or PD-L1 H-score (80 vs. 73, p=0.55); expression of PD-2L was higher (4% vs. 9%, p=0.19) in cases with JAK-2 amplification. Conclusion: Frontline intensive therapy is very effective in PMBCL patients. This is why no clear prognostic impact of protein expression of PD ligands or HLA expression was observed. There was constant high PD-L1 protein expression in PMBCL, low PD-2L expression and a high proportion of cases with JAK-2 gene amplification. Preliminary data show relationship between tumor immunogenicity (HLA-DR expression) and lymphoma aggressiveness. High PD-1L protein expression may probably influence treatment response quality. Further analyses are needed to clarify correlation between 9p24.1 amplification and PD-L protein expression. Supported by grants: MZ ÈR IGA NT 11103, LF-2012-007 and MSM 6198959205. Disclosures: Prochazka: Roche: Travel grants Other.


2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv1-iv1
Author(s):  
Markand Patel ◽  
Jinfeng Zhan ◽  
Kal Natarajan ◽  
Robert Flintham ◽  
Nigel Davies ◽  
...  

Abstract Aims Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma. Method The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model. Results Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy). Conclusion Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.


2019 ◽  
Vol 156 (6) ◽  
pp. S-1017
Author(s):  
Benjamin L. Elsbernd ◽  
Geoffrey McCrossan ◽  
Kerry B. Dunbar ◽  
Haekyung Jeon-Slaughter ◽  
Anh D. Nguyen

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