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Posttreatment high-grade glioma: usefulness of peak height position with semiquantitative MR perfusion histogram analysis in an entire contrast-enhanced lesion for predicting volume fraction of recurrence.

Authors
Kim, HS; Kim, JH; Kim, SH; Cho, KG; Kim, SY
Citation
Radiology, 256(3):906-915, 2010
Journal Title
Radiology
ISSN
0033-84191527-1315
Abstract
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.
MeSH terms
AdolescentAdultAgedBrain Neoplasms/pathology*Cerebrovascular CirculationContrast MediaFemaleGlioma/pathology*HumansImage Interpretation, Computer-AssistedLogistic ModelsMagnetic Resonance Imaging/methods*MaleMiddle AgedNeoplasm Recurrence, Local/pathologyROC CurveRetrospective StudiesSensitivity and Specificity
DOI
10.1148/radiol.10091461
PMID
20634429
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Pathology
Journal Papers > School of Medicine / Graduate School of Medicine > Neurosurgery
Journal Papers > School of Medicine / Graduate School of Medicine > Radiology
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