PURPOSE: To assess the utility of both minimum apparent diffusion coefficients (ADCs) and ADC difference values for grading astrocytic tumors at magnetic resonance imaging. MATERIALS AND METHODS: The hospital's institutional review board approved this retrospective study and waived informed consent. Fifty patients (23 male patients, 27 female patients; median age, 53 years) with newly diagnosed astrocytic tumors were evaluated. Two observers blinded to clinical information independently measured the ADCs by manually placing three to five regions of interest (40-60 mm(2)) within the solid tumor either with or without contrast material-enhanced components and calculated the average ADC. Minimum and maximum ADCs were selected, and the difference between them was recorded as the ADC difference value. These ADC values were used as the parameters for tumor grading and were compared by using the Kruskal-Wallis test and receiver operating characteristic (ROC) curve analysis. RESULTS: According to ROC analyses for distinguishing tumor grade, minimum ADCs showed the largest areas under the ROC curve. Minimum ADCs optimally helped distinguish grade 1 from higher-grade tumors at a cutoff value of 1.47 x 10(-3) mm(2)/sec and grade 4 from lower-grade tumors at a cutoff value of 1.01 x 10(-3) mm(2)/sec (P < .001 for both). ADC difference values helped distinguish grade 2 from grade 3 tumors at a cutoff value of 0.31 x 10(-3) mm(2)/sec (P < .001). When tumors were graded by using the combined minimum ADC and ADC difference cutoff values mentioned above (the two-parameter method), the following positive predictive values were obtained: grade 1 tumors, 73% (eight of 11); grade 2 tumors, 100% (five of five); grade 3 tumors, 67% (eight of 12); and grade 4 tumors, 91% (20 of 22). CONCLUSION: Using a combination of minimum ADCs and ADC difference values (the two-parameter method) facilitates the accurate grading of astrocytic tumors.
PURPOSE: To assess the utility of both minimum apparent diffusion coefficients (ADCs) and ADC difference values for grading astrocytic tumors at magnetic resonance imaging. MATERIALS AND METHODS: The hospital's institutional review board approved this retrospective study and waived informed consent. Fifty patients (23 male patients, 27 female patients; median age, 53 years) with newly diagnosed astrocytic tumors were evaluated. Two observers blinded to clinical information independently measured the ADCs by manually placing three to five regions of interest (40-60 mm(2)) within the solid tumor either with or without contrast material-enhanced components and calculated the average ADC. Minimum and maximum ADCs were selected, and the difference between them was recorded as the ADC difference value. These ADC values were used as the parameters for tumor grading and were compared by using the Kruskal-Wallis test and receiver operating characteristic (ROC) curve analysis. RESULTS: According to ROC analyses for distinguishing tumor grade, minimum ADCs showed the largest areas under the ROC curve. Minimum ADCs optimally helped distinguish grade 1 from higher-grade tumors at a cutoff value of 1.47 x 10(-3) mm(2)/sec and grade 4 from lower-grade tumors at a cutoff value of 1.01 x 10(-3) mm(2)/sec (P < .001 for both). ADC difference values helped distinguish grade 2 from grade 3 tumors at a cutoff value of 0.31 x 10(-3) mm(2)/sec (P < .001). When tumors were graded by using the combined minimum ADC and ADC difference cutoff values mentioned above (the two-parameter method), the following positive predictive values were obtained: grade 1 tumors, 73% (eight of 11); grade 2 tumors, 100% (five of five); grade 3 tumors, 67% (eight of 12); and grade 4 tumors, 91% (20 of 22). CONCLUSION: Using a combination of minimum ADCs and ADC difference values (the two-parameter method) facilitates the accurate grading of astrocytic tumors.
Authors: Sen Ma; Christopher T Nguyen; Fei Han; Nan Wang; Zixin Deng; Nader Binesh; Franklin G Moser; Anthony G Christodoulou; Debiao Li Journal: Magn Reson Med Date: 2019-11-25 Impact factor: 4.668
Authors: Chih-Chun Wu; Rajan Jain; Lucidio Neto; Seema Patel; Laila M Poisson; Jonathan Serrano; Victor Ng; Sohil H Patel; Dimitris G Placantonakis; David Zagzag; John Golfinos; Andrew S Chi; Matija Snuderl Journal: Neuroradiology Date: 2019-05-27 Impact factor: 2.804
Authors: M Essig; N Anzalone; S E Combs; À Dörfler; S-K Lee; P Picozzi; A Rovira; M Weller; M Law Journal: AJNR Am J Neuroradiol Date: 2011-10-20 Impact factor: 3.825