Brain Tumor Characterization Using Multibiometric Evaluation of MRI.

Pia Sundgren,Anna Rydelius,Silke Engelholm,Faris Durmo,Markus Nilsson,Jimmy Lätt,Sara Kinhult,Thomas Chenevert,Linda Knutsson,Johan Bengzon,Elisabet Englund,Krister Askaner,Isabella Björkman-Burtscher

doi:10.18383/j.tom.2017.00020

Abstract

The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) between LGG and HGG, and when these metrics were combined, HGG could be distinguished from LGG with a sensitivity and specificity of 100%. The only metric to distinguish HGG from MET was the normalized-ADC-E with a sensitivity of 68.8% and a specificity of 80%. LGG can be distinguished from MET by combining edema volume (Vol-E), Vol-E/tumor volume (Vol-T), nADC-T, nCBF-T, nCBV-T, and nADC-E with a sensitivity of 93.3% and a specificity of 100%. The present study confirms the usability of a multibiometric approach including volume, perfusion, and diffusion metrics in differentially diagnosing brain tumors in preoperative patients and adds to the growing body of evidence in the clinical field in need of validation and standardization.

Highlights

Glioblastomas are the most common malignant neoplasms of the brain and together with metastatic tumors comprise half of all the malignant tumors of the brain [1]
The aim of this study is to evaluate the sensitivity and specificity of advanced magnetic resonance (MR) imaging metrics for diffusion-weighted imaging (DWI), perfusion-weighted magnetic resonance imaging (MRI) and tumor and edema volume for tumor type differentiation in a cohort of patients with high-grade gliomas (HGG), low-grade gliomas (LGG), and MET
After excluding patients with meningioma, lesions at the skull base, and those that had limited preoperative MR examination, the final cohort consisted of 43 patients; 18 HGG (15 glioblastomas, grade IV; 2 oligoastrocytomas, grade III; 1 anaplastic oligoastrocytoma, grade III), 10 LGG (3 diffuse astrocytomas, grade II; 4 astrocytomas, grade II; 2 oligodendrogliomas, grade II; and 1 oligoastrocytoma, grade II), and 15 MET (9 adenocarcinomas with gastrointestinal, lung, or breast origin; 4 malignant melanomas; 1 invasive lobular breast carcinoma; and 1 anaplastic thyroid cancer)

Summary

Introduction

Glioblastomas are the most common malignant neoplasms of the brain and together with metastatic tumors comprise half of all the malignant tumors of the brain [1]. The recent published 2016 World Health Organization (WHO) classification of the central nervous system tumors incorporates, for the first time, molecular parameters in addition to histology to define brain tumors [2]. The 2016 WHO central nervous system tumor classification divides glioblastoma tumors into [1] glioblastoma isocitrate dehydrogenase (IDH)-wild type (90% of cases, presenting de novo in elderly patients), [2] glioblastoma IDH-mutant (10% of cases, the so-called secondary glioblastoma as the tumor often progresses from a low-grade tumor, predominately seen in younger patients), and [3] glioblastoma not-otherwisespecified tumor, in which complete IDH evaluation and histopathology cannot be performed or is inconclusive [2]. Grading presumes biological behavior or phenotype of a lesion and is, together with molecular testing, of high clinical The risk to develop a glioma of a certain grade increases with certain mutations [3].

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