OBJECTIVE: To assess the value of spectroscopic and perfusion MRI for glioma grading and for distinguishing glioblastomas from metastases and from CNS lymphomas. METHODS: The authors examined 79 consecutive patients with first detection of a brain neoplasm on nonenhanced CT scans and no therapy prior to evaluation. Spectroscopic MRI; arterial spin-labeling MRI for measuring cerebral blood flow (CBF); first-pass dynamic, susceptibility-weighted, contrast-enhanced MRI for measuring cerebral blood volume; and T1-weighted dynamic contrast-enhanced MRI were performed. Receiver operating characteristic analysis was performed, and optimum thresholds for tumor classification and glioma grading were determined. RESULTS: Perfusion MRI had a higher diagnostic performance than spectroscopic MRI. Because of a significantly higher tumor blood flow in glioblastomas compared with CNS lymphomas, a threshold value of 1.2 for CBF provided sensitivity of 97%, specificity of 80%, positive predictive value (PPV) of 94%, and negative predictive value (NPV) of 89%. Because CBF was significantly higher in peritumoral nonenhancing T2-hyperintense regions of glioblastomas compared with metastases, a threshold value of 0.5 for CBF provided sensitivity, specificity, PPV, and NPV of 100%, 71%, 94%, and 100%. Glioblastomas had the highest tumor blood flow values among all other glioma grades. For discrimination of glioblastomas from grade 3 gliomas, sensitivity was 97%, specificity was 50%, PPV was 84%, and NPV was 86% (CBF threshold value of 1.4), and for discrimination of glioblastomas from grade 2 gliomas, sensitivity was 94%, specificity was 78%, PPV was 94%, and NPV was 78% (CBF threshold value of 1.6). CONCLUSION: Perfusion MRI is predictive in distinguishing glioblastomas from metastases, CNS lymphomas and other gliomas vs MRI and magnetic resonance spectroscopy.
OBJECTIVE: To assess the value of spectroscopic and perfusion MRI for glioma grading and for distinguishing glioblastomas from metastases and from CNS lymphomas. METHODS: The authors examined 79 consecutive patients with first detection of a brain neoplasm on nonenhanced CT scans and no therapy prior to evaluation. Spectroscopic MRI; arterial spin-labeling MRI for measuring cerebral blood flow (CBF); first-pass dynamic, susceptibility-weighted, contrast-enhanced MRI for measuring cerebral blood volume; and T1-weighted dynamic contrast-enhanced MRI were performed. Receiver operating characteristic analysis was performed, and optimum thresholds for tumor classification and glioma grading were determined. RESULTS: Perfusion MRI had a higher diagnostic performance than spectroscopic MRI. Because of a significantly higher tumor blood flow in glioblastomas compared with CNS lymphomas, a threshold value of 1.2 for CBF provided sensitivity of 97%, specificity of 80%, positive predictive value (PPV) of 94%, and negative predictive value (NPV) of 89%. Because CBF was significantly higher in peritumoral nonenhancing T2-hyperintense regions of glioblastomas compared with metastases, a threshold value of 0.5 for CBF provided sensitivity, specificity, PPV, and NPV of 100%, 71%, 94%, and 100%. Glioblastomas had the highest tumor blood flow values among all other glioma grades. For discrimination of glioblastomas from grade 3 gliomas, sensitivity was 97%, specificity was 50%, PPV was 84%, and NPV was 86% (CBF threshold value of 1.4), and for discrimination of glioblastomas from grade 2 gliomas, sensitivity was 94%, specificity was 78%, PPV was 94%, and NPV was 78% (CBF threshold value of 1.6). CONCLUSION: Perfusion MRI is predictive in distinguishing glioblastomas from metastases, CNS lymphomas and other gliomas vs MRI and magnetic resonance spectroscopy.
Authors: K Yamashita; A Hiwatashi; O Togao; K Kikuchi; R Hatae; K Yoshimoto; M Mizoguchi; S O Suzuki; T Yoshiura; H Honda Journal: AJNR Am J Neuroradiol Date: 2015-09-24 Impact factor: 3.825
Authors: K Yamashita; T Yoshiura; H Arimura; F Mihara; T Noguchi; A Hiwatashi; O Togao; Y Yamashita; T Shono; S Kumazawa; Y Higashida; H Honda Journal: AJNR Am J Neuroradiol Date: 2008-04-03 Impact factor: 3.825
Authors: Richard G Abramson; Lori R Arlinghaus; Adrienne N Dula; C Chad Quarles; Ashley M Stokes; Jared A Weis; Jennifer G Whisenant; Eduard Y Chekmenev; Igor Zhukov; Jason M Williams; Thomas E Yankeelov Journal: Magn Reson Imaging Clin N Am Date: 2016-02 Impact factor: 2.266