Dataset Information

Evaluation of the diagnostic performance of magnetic resonance spectroscopy in brain tumors: a systematic review and meta-analysis.

ABSTRACT:

Object

The aim of this study was to determine the suitability of magnetic resonance spectroscopy (MRS) for screening brain tumors, based on a systematic review and meta-analysis of published data on the diagnostic performance of MRS.

Methods

The PubMed and PHMC databases were systematically searched for relevant studies up to December 2013. The sensitivities and specificities of MRS in individual studies were calculated and the pooled diagnostic accuracies, with 95% confidence intervals (CI), were assessed under a fixed-effects model.

Results

Twenty-four studies were included, comprising a total of 1013 participants. Overall, no heterogeneity of diagnostic effects was observed between studies. The pooled sensitivity and specificity of MRS were 80.05% (95% CI?=?75.97%-83.59%) and 78.46% (95% CI: 73.40%-82.78%), respectively. The area under the summary receiver operating characteristic curve was 0.78. Stratified meta analysis showed higher sensitivity and specificity in child than adult. CSI had higher sensitivity and SV had higher specificity. Higher sensitivity and specificity were obtained in short TE value.

Conclusion

Although the qualities of the studies included in the meta-analysis were moderate, current evidence suggests that MRS may be a valuable adjunct to magnetic resonance imaging for diagnosing brain tumors, but requires selection of suitable technique and TE value.

SUBMITTER: Wang W

PROVIDER: S-EPMC4231038 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of the diagnostic performance of magnetic resonance spectroscopy in brain tumors: a systematic review and meta-analysis.

Wang Wenzhi W Hu Yumin Y Lu Peiou P Li Yingci Y Chen Yunfu Y Tian Mohan M Yu Lijuan L

PloS one 20141113 11

<h4>Object</h4>The aim of this study was to determine the suitability of magnetic resonance spectroscopy (MRS) for screening brain tumors, based on a systematic review and meta-analysis of published data on the diagnostic performance of MRS.<h4>Methods</h4>The PubMed and PHMC databases were systematically searched for relevant studies up to December 2013. The sensitivities and specificities of MRS in individual studies were calculated and the pooled diagnostic accuracies, with 95% confidence int ...[more]

PMID: 25393009

Similar Datasets

Project description:Background & aimsMagnetic resonance elastography (MRE) is a noninvasive tool for staging liver fibrosis. We conducted a meta-analysis of individual participant data collected from published studies to assess the diagnostic accuracy of MRE for staging liver fibrosis in patients with chronic liver diseases (CLD).MethodsThrough a systematic literature search of multiple databases (2003-2013), we identified studies on diagnostic performance of MRE for staging liver fibrosis in patients with CLD with native anatomy, using liver biopsy as the standard. We contacted study authors to collect data on each participant's age, sex, body mass index (BMI), liver stiffness (measured by MRE), fibrosis stage, staging system used, degree of inflammation, etiology of CLD, and interval between MRE and biopsy. Through a pooled analysis, we calculated cluster-adjusted area under the receiver-operating curve, sensitivity, and specificity of MRE for any fibrosis (≥stage 1), significant fibrosis (≥stage 2), advanced fibrosis (≥stage 3), and cirrhosis (stage 4).ResultsWe analyzed data from 12 retrospective studies, comprising 697 patients (mean age, 55 ± 13 y; 59.4% male; mean BMI, 26.9 ± 6.7 kg/m(2); 92.1% with <1 year interval between MRE and biopsy; and 47.1% with hepatitis C). Overall, 19.5%, 19.4%, 15.5%, 15.9%, and 29.7% patients had stage 0, 1, 2, 3, and 4 fibrosis, respectively. The mean area under the receiver-operating curve values (and 95% confidence intervals) for the diagnosis of any (≥stage 1), significant (≥stage 2), advanced fibrosis (≥stage 3), and cirrhosis, were as follows: 0.84 (0.76-0.92), 0.88 (0.84-0.91), 0.93 (0.90-0.95), and 0.92 (0.90-0.94), respectively. A similar diagnostic performance was observed in stratified analysis based on sex, obesity, and etiology of CLD. The overall rate of failure of MRE was 4.3%.ConclusionsBased on a pooled analysis of data from individual participants, MRE has a high accuracy for the diagnosis of significant or advanced fibrosis and cirrhosis, independent of BMI and etiology of CLD. Prospective studies are warranted to better understand the diagnostic performance of MRE.

Project description:Brain tumors are a diverse group of neoplasms that are challenging to detect and classify due to their varying characteristics. Deep learning techniques have proven to be effective in tumor classification. However, there is a lack of studies that compare these techniques using a common methodology. This work aims to analyze the performance of convolutional neural networks in the classification of brain tumors. We propose a network consisting of a few convolutional layers, batch normalization, and max-pooling. Then, we explore recent deep architectures, such as VGG, ResNet, EfficientNet, or ConvNeXt. The study relies on two magnetic resonance imaging datasets with over 3000 images of three types of tumors -gliomas, meningiomas, and pituitary tumors-, as well as images without tumors. We determine the optimal hyperparameters of the networks using the training and validation sets. The training and test sets are used to assess the performance of the models from different perspectives, including training from scratch, data augmentation, transfer learning, and fine-tuning. The experiments are performed using the TensorFlow and Keras libraries in Python. We compare the accuracy of the models and analyze their complexity based on the capacity of the networks, their training times, and image throughput. Several networks achieve high accuracy rates on both datasets, with the best model achieving 98.7% accuracy, which is on par with state-of-the-art methods. The average precision for each type of tumor is 94.3% for gliomas, 93.8% for meningiomas, 97.9% for pituitary tumors, and 95.3% for images without tumors. VGG is the largest model with over 171 million parameters, whereas MobileNet and EfficientNetB0 are the smallest ones with 3.2 and 5.9 million parameters, respectively. These two neural networks are also the fastest to train with 23.7 and 25.4 seconds per epoch, respectively. On the other hand, ConvNext is the slowest model with 58.2 seconds per epoch. Our custom model obtained the highest image throughput with 234.37 images per second, followed by MobileNet with 226 images per second. ConvNext obtained the smallest throughput with 97.35 images per second. ResNet, MobileNet, and EfficientNet are the most accurate networks, with MobileNet and EfficientNet demonstrating superior performance in terms of complexity. Most models achieve the best accuracy using transfer learning followed by a fine-tuning step. However, data augmentation does not contribute to increasing the accuracy of the models in general.

Dataset Information

Evaluation of the diagnostic performance of magnetic resonance spectroscopy in brain tumors: a systematic review and meta-analysis.

Object

Methods

Results

Conclusion

Publications

Evaluation of the diagnostic performance of magnetic resonance spectroscopy in brain tumors: a systematic review and meta-analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets