J H Ma1, H S Kim, N-J Rim, S-H Kim, K-G Cho. 1. Department of Diagnostic Radiology, Ajou University School of Medicine, Mt. 5 Woncheon-dong, Yeongtong-gu, Suwon, Gyeonggi-do, Korea.
Abstract
BACKGROUND AND PURPOSE: The histogram method has been shown to demonstrate heterogeneous morphologic features of tumor vascularity. This study aimed to determine whether whole-tumor histogram analysis of the normalized CBV for contrast-enhancing lesions and perienhancing lesions can differentiate among GBMs, SMTs, and lymphomas. MATERIALS AND METHODS: Fifty-nine patients with histopathologically confirmed GBMs (n = 28), SMTs (n = 22), or lymphomas (n = 12) underwent conventional MR imaging and dynamic susceptibility contrast-enhanced imaging before surgery. Histogram distribution of the normalized CBV was obtained from whole-tumor voxels in contrast-enhancing lesions and perienhancing lesions. The HW, PHP, and MV were determined from histograms. One-way ANOVA was used initially to test the overall equality of mean values for each type of tumor. Subsequently, posttest multiple comparisons were performed. RESULTS: For whole-tumor histogram analyses for contrast-enhancing lesions, only PHP could differentiate among GBMs (4.79 ± 1.31), SMTs (3.32 ± 1.10), and lymphomas (2.08 ± 0.54). The parameters HW and MV were not significantly different between GBMs and SMTs, whereas the 2 histogram parameters were significantly higher in GBMs and SMTs compared with lymphomas. For the analyses of perienhancing lesions, only MV could differentiate among GBMs (1.90 ± 0.26), SMTs (0.80 ± 0.21), and lymphomas (1.27 ± 0.34). HW and PHP were not significantly different between SMTs and lymphomas. CONCLUSIONS: Using a whole-tumor histogram analysis of normalized CBV for contrast-enhancing lesions and perienhancing lesions facilitates differentiation of GBMs, SMTs and lymphomas.
BACKGROUND AND PURPOSE: The histogram method has been shown to demonstrate heterogeneous morphologic features of tumor vascularity. This study aimed to determine whether whole-tumor histogram analysis of the normalized CBV for contrast-enhancing lesions and perienhancing lesions can differentiate among GBMs, SMTs, and lymphomas. MATERIALS AND METHODS: Fifty-nine patients with histopathologically confirmed GBMs (n = 28), SMTs (n = 22), or lymphomas (n = 12) underwent conventional MR imaging and dynamic susceptibility contrast-enhanced imaging before surgery. Histogram distribution of the normalized CBV was obtained from whole-tumor voxels in contrast-enhancing lesions and perienhancing lesions. The HW, PHP, and MV were determined from histograms. One-way ANOVA was used initially to test the overall equality of mean values for each type of tumor. Subsequently, posttest multiple comparisons were performed. RESULTS: For whole-tumor histogram analyses for contrast-enhancing lesions, only PHP could differentiate among GBMs (4.79 ± 1.31), SMTs (3.32 ± 1.10), and lymphomas (2.08 ± 0.54). The parameters HW and MV were not significantly different between GBMs and SMTs, whereas the 2 histogram parameters were significantly higher in GBMs and SMTs compared with lymphomas. For the analyses of perienhancing lesions, only MV could differentiate among GBMs (1.90 ± 0.26), SMTs (0.80 ± 0.21), and lymphomas (1.27 ± 0.34). HW and PHP were not significantly different between SMTs and lymphomas. CONCLUSIONS: Using a whole-tumor histogram analysis of normalized CBV for contrast-enhancing lesions and perienhancing lesions facilitates differentiation of GBMs, SMTs and lymphomas.
Authors: Soonmee Cha; Edmond A Knopp; Glyn Johnson; Stephan G Wetzel; Andrew W Litt; David Zagzag Journal: Radiology Date: 2002-04 Impact factor: 11.105
Authors: Michael H Lev; Yelda Ozsunar; John W Henson; Amjad A Rasheed; Glenn D Barest; Griffith R Harsh; Markus M Fitzek; E Antonio Chiocca; James D Rabinov; Andrew N Csavoy; Bruce R Rosen; Fred H Hochberg; Pamela W Schaefer; R Gilberto Gonzalez Journal: AJNR Am J Neuroradiol Date: 2004-02 Impact factor: 3.825
Authors: S Cha; J M Lupo; M-H Chen; K R Lamborn; M W McDermott; M S Berger; S J Nelson; W P Dillon Journal: AJNR Am J Neuroradiol Date: 2007 Jun-Jul Impact factor: 3.825
Authors: X Li; D Wang; S Liao; L Guo; X Xiao; X Liu; Y Xu; J Hua; J J Pillai; Y Wu Journal: AJNR Am J Neuroradiol Date: 2020-03-05 Impact factor: 3.825