The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives

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Abstract

The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.

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Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme.

Evangelia Zacharaki, Dong Hwang, Sanjeev Chawla … (2009)

The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer-assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including region-of-interest definition, feature extraction, feature selection, and classification. The extracted features include tumor shape and intensity characteristics, as well as rotation invariant texture features. Feature subset selection is performed using support vector machines with recursive feature elimination. The method was applied on a population of 102 brain tumors histologically diagnosed as metastasis (24), meningiomas (4), gliomas World Health Organization grade II (22), gliomas World Health Organization grade III (18), and glioblastomas (34). The binary support vector machine classification accuracy, sensitivity, and specificity, assessed by leave-one-out cross-validation, were, respectively, 85%, 87%, and 79% for discrimination of metastases from gliomas and 88%, 85%, and 96% for discrimination of high-grade (grades III and IV) from low-grade (grade II) neoplasms. Multiclass classification was also performed via a one-vs-all voting scheme. (c) 2009 Wiley-Liss, Inc.

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Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging.

Meng Law, Stanley Yang, Hao-Hao Wang … (2015)

Sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of conventional MR imaging in predicting glioma grade are not high. Relative cerebral blood volume (rCBV) measurements derived from perfusion MR imaging and metabolite ratios from proton MR spectroscopy are useful in predicting glioma grade. We evaluated the sensitivity, specificity, PPV, and NPV of perfusion MR imaging and MR spectroscopy compared with conventional MR imaging in grading primary gliomas. One hundred sixty patients with a primary cerebral glioma underwent conventional MR imaging, dynamic contrast-enhanced T2*-weighted perfusion MR imaging, and proton MR spectroscopy. Gliomas were graded as low or high based on conventional MR imaging findings. The rCBV measurements were obtained from regions of maximum perfusion. Metabolite ratios (choline [Cho]/creatine [Cr], Cho/N-acetylaspartate [NAA], and NAA/Cr) were measured at a TE of 144 ms. Tumor grade determined with the three methods was then compared with that from histopathologic grading. Logistic regression and receiver operating characteristic analyses were performed to determine optimum thresholds for tumor grading. Sensitivity, specificity, PPV, and NPV for identifying high-grade gliomas were also calculated. Sensitivity, specificity, PPV, and NPV for determining a high-grade glioma with conventional MR imaging were 72.5%, 65.0%, 86.1%, and 44.1%, respectively. Statistical analysis demonstrated a threshold value of 1.75 for rCBV to provide sensitivity, specificity, PPV, and NPV of 95.0%, 57.5%, 87.0%, and 79.3%, respectively. Threshold values of 1.08 and 1.56 for Cho/Cr and 0.75 and 1.60 for Cho/NAA provided the minimum C2 and C1 errors, respectively, for determining a high-grade glioma. The combination of rCBV, Cho/Cr, and Cho/NAA resulted in sensitivity, specificity, PPV, and NPV of 93.3%, 60.0%, 87.5%, and 75.0%, respectively. Significant differences were noted in the rCBV and Cho/Cr, Cho/NAA, and NAA/Cr ratios between low- and high-grade gliomas (P <.0001,.0121,.001, and.0038, respectively). The rCBV measurements and metabolite ratios both individually and in combination can increase the sensitivity and PPV when compared with conventional MR imaging alone in determining glioma grade. The rCBV measurements had the most superior diagnostic performance (either with or without metabolite ratios) in predicting glioma grade. Threshold values can be used in a clinical setting to evaluate tumors preoperatively for histologic grade and provide a means for guiding treatment and predicting postoperative patient outcome.

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Author and article information

Contributors

Patricia Svolos

Evanthia Kousi

Eftychia Kapsalaki

Kyriaki Theodorou

Ioannis Fezoulidis

Constantin Kappas

Ioannis Tsougos

Journal

Journal ID (nlm-ta): Cancer Imaging

Journal ID (iso-abbrev): Cancer Imaging

Title: Cancer Imaging

Publisher: BioMed Central

ISSN (Print): 1740-5025

ISSN (Electronic): 1470-7330

Publication date Collection: 2014

Publication date (Electronic): 29 April 2014

Volume: 14

Issue: 1

Page: 20

Affiliations

[1 ]Medical Physics Department, University of Thessaly, Biopolis, 41110 Larissa, Greece

[2 ]Department of Radiology, University Hospital of Larissa, Biopolis, 41110 Larissa, Greece

Article

Publisher ID: 1470-7330-14-20

DOI: 10.1186/1470-7330-14-20

PMC ID: 4331825

PubMed ID: 25609475

SO-VID: 439ea6e4-bf94-46b6-8f35-7df708ab879a

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives

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Electron Channelling Contrast Imaging (ECCI)

Most cited references 105

Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme.

Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging.

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