MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta-analysis

2019 ◽  
Vol 50 (2) ◽  
pp. 560-572 ◽  
Author(s):  
Chong Hyun Suh ◽  
Ho Sung Kim ◽  
Seung Chai Jung ◽  
Ji Eun Park ◽  
Choong Gon Choi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Central Nervous System ◽  
Nervous System ◽  
Meta Analysis ◽  
Central Nervous System Lymphoma ◽  
Diagnostic Biomarker

Author response for "Rituximab in Primary Central Nervous System Lymphoma – A Systematic Review and Meta‐analysis"

2019 ◽  
Author(s):  
Andreas M. Schmitt ◽  
Amanda K. Herbrand ◽  
Christopher P. Fox ◽  
Katerina Bakunina ◽  
Jacoline E.C. Bromberg ◽  
...  
Keyword(s):  
Systematic Review ◽  
Central Nervous System ◽  
Nervous System ◽  
Meta Analysis ◽  
Central Nervous System Lymphoma ◽  
Author Response

scholarly journals Efficacy and Safety of Autologous Stem-Cell Transplantation as Part of First-Line Treatment for Newly Diagnosed Primary Central Nervous System Lymphoma: A Systematic Review and Meta-Analysis

2022 ◽  
Vol 11 ◽  
Author(s):  
Jing Liu ◽  
Jiayuan Guo ◽  
Xuefei Sun ◽  
Yuanbo Liu ◽  
Chunji Gao
Keyword(s):  
Systematic Review ◽  
Central Nervous System ◽  
Nervous System ◽  
Stem Cell ◽  
Stem Cell Transplantation ◽  
Prospective Studies ◽  
Meta Analysis ◽  
Central Nervous System Lymphoma ◽  
Newly Diagnosed ◽  
Efficacy And Safety

ObjectiveThe reviewed literature supports a treatment regimen for primary central nervous system lymphoma (PCNSL) that includes induction chemotherapy, followed by one consolidation therapy. High-dose chemotherapy supported by autologous stem-cell transplantation (ASCT) is the most studied option, but its effects are controversial. The aim of this study was to evaluate the efficacy and safety of ASCT for newly diagnosed PCNSL by means of a meta-analysis.MethodsThe PubMed, Embase, and Cochrane Library databases were systematically searched for studies published until May 20, 2021. Included studies were prospective studies of patients with newly diagnosed PCNSL treated with ASCT. The pooled rates and 95% confidence intervals (CIs) were determined for all outcomes. Subgroup analysis was conducted to compare the relative risk (RR) with 95% CIs for the complete remission (CR) rate and the hazard ratios (HRs) with 95% CIs for progression-free survival (PFS) and overall survival (OS).ResultsThirteen prospective studies including 348 patients were analyzed. The pooled CR rate, overall response rate, and relapse rate were 80% (95% CI, 71–88%, I2 = 67.06%, p = 0.00), 95% (95% CI, 87–100%, I2 = 73.65%, p= 0.00), and 19% (95% CI, 15–24%, I2 = 76.18%, p = 0.00), respectively. The pooled 2- and 5-year PFS and OS rates were 74% (95% CI, 68–80%, I2 = 3.90%), 65% (95% CI, 51–77%, I2 = 74.61%), 80% (95% CI, 72–88%, I2 = 57.54%), and 69% (95% CI, 53–83%, I2 = 83.89%), respectively. Hematological toxicity and infections were more common adverse events above grade 3. The pooled treatment-related mortality was 3% (95% CI, 1–6%, I2 = 28.18%, p = 0.16). In the group analysis of ASCT compared with whole-brain radiotherapy, there were no significant differences in the CR rate (RR, 1.00, 95% CI, 0.88–1.14, p = 0.971), relapse rate (RR, 0.44, 95% CI, 0.06–3.10, p = 0.408), PFS (HR, 1.28, 95% CI, 0.81–2.01, p = 0.29), or OS (HR, 1.62, 95% CI, 0.97–2.69, p = 0.06). Cognitive functions were preserved or improved after ASCT.ConclusionsASCT is a feasible approach for consolidation with good tolerability for newly diagnosed PCNSL patients. High-quality randomized controlled trials are still needed to confirm the effects of ASCT.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42021268422.

scholarly journals Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis

2018 ◽  
Vol 45 (5) ◽  
pp. E5 ◽  
Author(s):  
Anthony V. Nguyen ◽  
Elizabeth E. Blears ◽  
Evan Ross ◽  
Rishi R. Lall ◽  
Juan Ortega-Barnett
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Central Nervous System ◽  
Nervous System ◽  
Predictive Analytics ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Central Nervous System Lymphoma ◽  
Radiological Imaging ◽  
Data Set

OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.METHODSThe authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).RESULTSEight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.CONCLUSIONSFew studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.

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