Evaluation of glioblastomas and lymphomas with whole-brain CT perfusion: Comparison between a delay-invariant singular-value decomposition algorithm and a Patlak plot

Akio Hiwatashi, Osamu Togao, Koji Yamashita, Kazufumi Kikuchi, Koji Yoshimoto, Masahiro Mizoguchi, Satoshi O. Suzuki, Takashi Yoshiura, Hiroshi Honda

研究成果: Contribution to journalArticle査読

6 被引用数 (Scopus)

抄録

Objective: Correction of contrast leakage is recommended when enhancing lesions during perfusion analysis. The purpose of this study was to assess the diagnostic performance of computed tomography perfusion (CTP) with a delay-invariant singular-value decomposition algorithm (SVD+) and a Patlak plot in differentiating glioblastomas from lymphomas. Materials and methods: This prospective study included 17 adult patients (12 men and 5 women) with pathologically proven glioblastomas (n = 10) and lymphomas (n = 7). CTP data were analyzed using SVD+ and a Patlak plot. The relative tumor blood volume and flow compared to contralateral normal-appearing gray matter (rCBV and rCBF derived from SVD+, and rBV and rFlow derived from the Patlak plot) were used to differentiate between glioblastomas and lymphomas. The Mann-Whitney U test and receiver operating characteristic (ROC) analyses were used for statistical analysis. Results: Glioblastomas showed significantly higher rFlow (3.05 ± 0.49, mean ± standard deviation) than lymphomas (1.56 ± 0.53; P < 0.05). There were no statistically significant differences between glioblastomas and lymphomas in rBV (2.52 ± 1.57 vs. 1.03 ± 0.51; P > 0.05), rCBF (1.38 ± 0.41 vs. 1.29 ± 0.47; P > 0.05), or rCBV (1.78 ± 0.47 vs. 1.87 ± 0.66; P > 0.05). ROC analysis showed the best diagnostic performance with rFlow (Az = 0.871), followed by rBV (Az = 0.771), rCBF (Az = 0.614), and rCBV (Az = 0.529). Conclusion: CTP analysis with a Patlak plot was helpful in differentiating between glioblastomas and lymphomas, but CTP analysis with SVD+ was not.

本文言語英語
ページ(範囲)266-272
ページ数7
ジャーナルJournal of Neuroradiology
43
4
DOI
出版ステータス出版済み - 7 1 2016

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

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