title: Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas.
abstract: OBJECTIVE
  Tumor grading is very important both in treatment decision and evaluation of prognosis. While tissue samples are obtained as part of most therapeutic approaches, factors that may result in inaccurate grading due to sampling error (namely, heterogeneity in tissue sampling, as well as tumor-grade heterogeneity within the same tumor specimen), have led to a desire to use imaging better to ascertain tumor grade. The purpose in our study was to evaluate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and accuracy of diffusion-weighted MR imaging (DWI), proton MR spectroscopic imaging (MRSI) or both in grading primary cerebral gliomas.
  
  
  METHODS
  We performed conventional MR imaging (MR), DWI, and MRSI in 74 patients with newly diagnosed brain gliomas: 59 patients had histologically verified high-grade gliomas: 37 glioblastomas multiform (GBM) and 22 anaplastic astrocytomas (AA), and 15 patients had low-grade gliomas. Apparent diffusion coefficient (ADC) values of tumor and peritumoral edema, and ADC ratios (ADC in tumor or peritumoral edema to ADC of contralateral white matter, as well as ADC in tumor to ADC in peritumoral edema) were determined from three regions of interest. The average of the mean, maximum, and minimum for ADC variables was calculated for each patient. The metabolite ratios of Cho/Cr and Cho/NAA at intermediate TE were assessed from spectral maps in the solid portion of tumor, peritumoral edema and contralateral normal-appearing white matter. Tumor grade determined with the two methods was then compared with that from histopathologic grading. Logistic regression and receiver operating characteristic (ROC) curve analysis were performed to determine optimum thresholds for tumor grading. Measures of diagnostic examination performance, such as sensitivity, specificity, PPV, NPV, AUC, and accuracy for identifying high-grade gliomas were also calculated.
  
  
  RESULTS
  Statistical analysis demonstrated a threshold minimum ADC tumor value of 1.07 to provide sensitivity, specificity, PPV, and NPV of 79.7%, 60.0%, 88.7%, and 42.9% respectively, in determining high-grade gliomas. Threshold values of 1.35 and 1.78 for peritumoral Cho/Cr and Cho/NAA metabolite ratios resulted in sensitivity, specificity, PPV, and NPV of 83.3%, 85.1%, 41.7%, 97.6%, and 100%, 57.4%, 23.1% and 100% respectively for determining high-grade gliomas. Significant differences were noted in the ADC tumor values and ratios, peritumoral Cho/Cr and Cho/NAA metabolite ratios, and tumoral Cho/NAA ratio between low- and high-grade gliomas. The combination of mean ADC tumor value, maximum ADC tumor ratio, peritumoral Cho/Cr and Cho/NAA metabolite ratios resulted in sensitivity, specificity, PPV, and NPV of 91.5%, 100%, 100% and 60% respectively.
  
  
  CONCLUSIONS
  Combining DWI and MRSI increases the accuracy of preoperative imaging in the determination of glioma grade. MRSI had superior diagnostic performance in predicting glioma grade compared with DWI alone. The predictive values are helpful in the clinical decision-making process to evaluate the histologic grade of tumors, and provide a means of guiding treatment.
authors: Section of Neuroradiology, Department of Radiology and Nuclear Medicine, Oslo University Hospital-Ullevaal and University of Oslo, Kirkeveien 166, NO-0407 Oslo, Norway. a.s.alonso@medisin.uio.no
authors: Andrés
authors: A
authors: Server
authors: Bettina
authors: B
authors: Kulle
authors: Øystein B
authors: ØB
authors: Gadmar
authors: Roger
authors: R
authors: Josefsen
authors: Theresa
authors: T
authors: Kumar
authors: Per H
authors: PH
authors: Nakstad
mesh_terms: D019540:Area Under Curve; D001224:Aspartic Acid / Q000031:analogs & derivatives / Q000378:metabolism; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D002794:Choline / Q000378:metabolism; D003401:Creatine / Q000378:metabolism; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D005910:Glioma / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016015:Logistic Models; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D011237:Predictive Value of Tests; D011522:Protons; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D011522:Protons; D001224:Aspartic Acid; C000179:N-acetylaspartate; D003401:Creatine; D002794:Choline