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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.

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dc.contributor.authorKim, HS-
dc.contributor.authorKim, JH-
dc.contributor.authorKim, SH-
dc.contributor.authorCho, KG-
dc.contributor.authorKim, SY-
dc.date.accessioned2011-05-02T04:57:38Z-
dc.date.available2011-05-02T04:57:38Z-
dc.date.issued2010-
dc.identifier.issn0033-8419-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/2502-
dc.description.abstractPURPOSE: 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.
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dc.language.isoen-
dc.subject.MESHAdolescent-
dc.subject.MESHAdult-
dc.subject.MESHAged-
dc.subject.MESHBrain Neoplasms-
dc.subject.MESHCerebrovascular Circulation-
dc.subject.MESHContrast Media-
dc.subject.MESHFemale-
dc.subject.MESHGlioma-
dc.subject.MESHHumans-
dc.subject.MESHImage Interpretation, Computer-Assisted-
dc.subject.MESHLogistic Models-
dc.subject.MESHMagnetic Resonance Imaging-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHNeoplasm Recurrence, Local-
dc.subject.MESHROC Curve-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHSensitivity and Specificity-
dc.titlePosttreatment 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.-
dc.typeArticle-
dc.identifier.pmid20634429-
dc.identifier.urlhttp://radiology.rsnajnls.org/cgi/pmidlookup?view=long&pmid=20634429-
dc.contributor.affiliatedAuthor김, 장희-
dc.contributor.affiliatedAuthor김, 세혁-
dc.contributor.affiliatedAuthor조, 경기-
dc.contributor.affiliatedAuthor김, 선용-
dc.type.localJournal Papers-
dc.identifier.doi10.1148/radiol.10091461-
dc.citation.titleRadiology-
dc.citation.volume256-
dc.citation.number3-
dc.citation.date2010-
dc.citation.startPage906-
dc.citation.endPage915-
dc.identifier.bibliographicCitationRadiology, 256(3). : 906-915, 2010-
dc.identifier.eissn1527-1315-
dc.relation.journalidJ000338419-
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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