PURPOSE: To determine whether semiquantitative histogram analysis of the normalized cerebral blood volume (CBV) for an entire contrast material-enhanced lesion could be used to predict the volume fraction of posttreatment high-grade glioma recurrence compared with posttreatment change. MATERIALS AND METHODS: The institutional review board approved this retrospective study. Informed consent was obtained. Thirty-nine patients with pathologically proved predominant tumor recurrence (tumor recurrence group, tumor fraction > or =50% [n = 14]), mixed tumor and posttreatment change (mixed group, tumor fraction > or =20% and <50% [n = 10]), and predominant posttreatment change (treatment change group, tumor fraction <20% [n = 15]) were evaluated. Histogram parameters of normalized CBV-histogram width, peak height position (PHP), and maximum value (MV)-were measured in entire contrast-enhanced lesions and used as discriminative indexes. Ordered logistic regression was used to determine independent factors for predicting the diseases of posttreatment contrast-enhanced lesions. Leave-one-out cross-validation was used to determine diagnostic accuracy. RESULTS: PHP was an independent predictive factor (P = .003) for differentiating contrast-enhanced lesions in patients with posttreatment gliomas. According to receiver operating characteristic curve analyses, PHP provided sensitivity of 90.2% and specificity of 91.1% for differentiating tumor recurrence from mixed and treatment change groups at an optimum threshold of 1.7 by using leave-one-out cross-validation. MV helped distinguish treatment change group from tumor recurrence and mixed groups at an optimum threshold of 2.6 (sensitivity, 96.5%; specificity, 93.1%). CONCLUSION: PHP can be used to predict the volume fraction of posttreatment high-grade glioma recurrence. (c) RSNA, 2010.
PURPOSE: To determine whether semiquantitative histogram analysis of the normalized cerebral blood volume (CBV) for an entire contrast material-enhanced lesion could be used to predict the volume fraction of posttreatment high-grade glioma recurrence compared with posttreatment change. MATERIALS AND METHODS: The institutional review board approved this retrospective study. Informed consent was obtained. Thirty-nine patients with pathologically proved predominant tumor recurrence (tumor recurrence group, tumor fraction > or =50% [n = 14]), mixed tumor and posttreatment change (mixed group, tumor fraction > or =20% and <50% [n = 10]), and predominant posttreatment change (treatment change group, tumor fraction <20% [n = 15]) were evaluated. Histogram parameters of normalized CBV-histogram width, peak height position (PHP), and maximum value (MV)-were measured in entire contrast-enhanced lesions and used as discriminative indexes. Ordered logistic regression was used to determine independent factors for predicting the diseases of posttreatment contrast-enhanced lesions. Leave-one-out cross-validation was used to determine diagnostic accuracy. RESULTS: PHP was an independent predictive factor (P = .003) for differentiating contrast-enhanced lesions in patients with posttreatment gliomas. According to receiver operating characteristic curve analyses, PHP provided sensitivity of 90.2% and specificity of 91.1% for differentiating tumor recurrence from mixed and treatment change groups at an optimum threshold of 1.7 by using leave-one-out cross-validation. MV helped distinguish treatment change group from tumor recurrence and mixed groups at an optimum threshold of 2.6 (sensitivity, 96.5%; specificity, 93.1%). CONCLUSION: PHP can be used to predict the volume fraction of posttreatment high-grade glioma recurrence. (c) RSNA, 2010.
Authors: Katrin Haegler; Martin Wiesmann; Christian Böhm; Jessica Freiherr; Oliver Schnell; Hartmut Brückmann; Jörg-Christian Tonn; Jennifer Linn Journal: Neuroradiology Date: 2011-12-14 Impact factor: 2.804
Authors: J Cha; S T Kim; H-J Kim; B-J Kim; Y K Kim; J Y Lee; P Jeon; K H Kim; D-S Kong; D-H Nam Journal: AJNR Am J Neuroradiol Date: 2014-03-27 Impact factor: 3.825
Authors: Norbert Galldiks; Veronika Dunkl; Gabriele Stoffels; Markus Hutterer; Marion Rapp; Michael Sabel; Guido Reifenberger; Sied Kebir; Franziska Dorn; Tobias Blau; Ulrich Herrlinger; Peter Hau; Maximilian I Ruge; Martin Kocher; Roland Goldbrunner; Gereon R Fink; Alexander Drzezga; Matthias Schmidt; Karl-Josef Langen Journal: Eur J Nucl Med Mol Imaging Date: 2014-11-20 Impact factor: 9.236
Authors: S Wang; M Martinez-Lage; Y Sakai; S Chawla; S G Kim; M Alonso-Basanta; R A Lustig; S Brem; S Mohan; R L Wolf; A Desai; H Poptani Journal: AJNR Am J Neuroradiol Date: 2015-10-08 Impact factor: 3.825