RESUMO
BACKGROUND: Chronic neck pain is reportedly considered the fourth leading cause of disability. Cervical interlaminar epidural injections are among the commonly administered nonsurgical interventions for managing chronic neck pain, secondary to disc herniation and radiculitis, spinal stenosis, or chronic neck pain of discogenic origin. OBJECTIVES: To systematically review the differences in the effectiveness of cervical epidural injections with local anesthetics with or without steroids for the management of chronic neck pain. STUDY DESIGN: Systematic review and meta-analysis. METHODS: A comprehensive search of the literature of randomized controlled trials (RCTs) that compared epidural injections with local anesthetic with or without steroids was performed, including a search of PubMed, EMBASE, and Cochrane databases for all years up to May 2019. Meta-analysis was done for pain relief based on the Numeric Rating Scale, functional status based on the Neck Disability Index, and opioid intake dosage. RESULTS: Four studies met the inclusion criteria. A total of 370 patients were divided into 2 groups: the experimental group received cervical epidural injection with steroid and local anesthetic, and the control group received injection with local anesthetic only. Regrading pain relief, no significant difference was observed between both groups (weighted mean difference [WMD], -0.006; 95% confidence interval (CI), -0.275 to 0.263; P = 0.963; I² = 0.0% at 12 months). There was also no significant difference in the improvement of the functional status (WMD, 0.159; 95% CI, -1.231 to 1.549; P = 0.823; I² = 9.8% at 12 months). Similarly, there was no significant difference in opioid dosage (WMD, -0.093; 95% CI, -5.952 to 5.766; P = 0.975; I² = 0.0% at 12 months). LIMITATIONS: Only a few studies on this premise were found in the literature. There was also a lack of heterogeneity of the included RCT studies. CONCLUSIONS: The addition of steroids to anesthetic injectates was not associated with better pain and functional score outcomes compared with anesthetic injectate alone in patients with chronic neck pain. KEY WORDS: Chronic neck pain, cervical radiculopathy, cervical disc disease, spinal stenosis, facet joint pathology, cervical epidural injections, steroid injections, local anesthetic injections, systematic review, meta-analysis, randomized control trial.
Assuntos
Anestésicos Locais/administração & dosagem , Dor Crônica/tratamento farmacológico , Cervicalgia/tratamento farmacológico , Manejo da Dor/métodos , Esteroides/administração & dosagem , Dor Crônica/diagnóstico , Quimioterapia Combinada , Humanos , Injeções Epidurais/métodos , Cervicalgia/diagnóstico , Manejo da Dor/efeitos adversos , Radiculopatia/diagnóstico , Radiculopatia/tratamento farmacológico , Resultado do TratamentoRESUMO
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that 'NC versus MCI' showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; 'NC versus AD' showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and 'MCI versus AD' showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.
Assuntos
Doença de Alzheimer/diagnóstico por imagem , Disfunção Cognitiva/diagnóstico por imagem , Aprendizado Profundo , Imageamento por Ressonância Magnética , Modelos Neurológicos , Neuroimagem/normas , Máquina de Vetores de Suporte , Idoso , Idoso de 80 Anos ou mais , Conjuntos de Dados como Assunto , Feminino , Humanos , Imageamento por Ressonância Magnética/normas , Masculino , Sensibilidade e EspecificidadeRESUMO
In recent years, new therapeutic options have become available for prostate cancer (PC) patients, generating an urgent need for better biomarkers to guide the choice of therapy and monitor treatment response. Liquid biopsies, including circulating tumor cells (CTCs), circulating nucleic acids, and exosomes, have been developed as minimally invasive assays allowing oncologists to monitor PC patients with real-time cellular or molecular information. While CTC counts remain the most extensively validated prognostic biomarker to monitor treatment response, recent advances demonstrate that CTC morphology and androgen receptor characterization can provide additional information to guide the choice of treatment. Characterization of cell-free DNA (cfDNA) is another rapidly emerging field with novel technologies capable of monitoring the evolution of treatment relevant alterations such as those in DNA damage repair genes for poly (ADP-ribose) polymerase (PARP) inhibition. In addition, several new liquid biopsy fields are emerging, including the characterization of heterogeneity, CTC RNA sequencing, the culture and xenografting of CTCs, and the characterization of extracellular vesicles (EVs) and circulating microRNAs. This review describes the clinical utilization of liquid biopsies in the management of PC patients and emerging liquid biopsy technologies with the potential to advance personalized cancer therapy.