T B Nguyen1, G O Cron2, J F Mercier3, C Foottit4, C H Torres2, S Chakraborty2, J Woulfe5, G H Jansen5, J M Caudrelier2, J Sinclair6, M J Hogan7, R E Thornhill2, I G Cameron8. 1. From the Departments of Diagnostic Imaging (T.B.N., G.O.C., C.H.T., R.E.T., I.G.C., S.C., J.M.C.) thnguyen@toh.on.ca. 2. From the Departments of Diagnostic Imaging (T.B.N., G.O.C., C.H.T., R.E.T., I.G.C., S.C., J.M.C.). 3. Department of Radiology (J.F.M.), Hôpital de Hull, Gatineau, Québec, Canada. 4. Medical Physics (C.F., I.G.C.). 5. Pathology (G.H.J., J.W.). 6. Surgery, Division of Neurosurgery (J.S.). 7. Medicine, Division of Neurology (M.J.H.), The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada. 8. From the Departments of Diagnostic Imaging (T.B.N., G.O.C., C.H.T., R.E.T., I.G.C., S.C., J.M.C.) Medical Physics (C.F., I.G.C.).
Abstract
BACKGROUND AND PURPOSE: The prognostic value of dynamic contrast-enhanced MR imaging-derived plasma volume obtained in tumor and the contrast transfer coefficient has not been well-established in patients with gliomas. We determined whether plasma volume and contrast transfer coefficient in tumor correlated with survival in patients with gliomas in addition to other factors such as age, type of surgery, preoperative Karnofsky score, contrast enhancement, and histopathologic grade. MATERIALS AND METHODS: This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. The contrast transfer coefficient and plasma volume obtained in tumor maps were calculated directly from the signal-intensity curve without T1 measurements, and values were obtained from multiple small ROIs placed within tumors. Survival curve analysis was performed by dichotomizing patients into groups of high and low contrast transfer coefficient and plasma volume. Univariate analysis was performed by using dynamic contrast-enhanced parameters and clinical factors. Factors that were significant on univariate analysis were entered into multivariate analysis. RESULTS: For all patients with gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). In subgroups of high- and low-grade gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). Univariate analysis showed that factors associated with lower survival were age older than 50 years, low Karnofsky score, biopsy-only versus resection, marked contrast enhancement versus no/mild enhancement, high contrast transfer coefficient, and high plasma volume obtained in tumor (P < .05). In multivariate analysis, a low Karnofsky score, biopsy versus resection in combination with marked contrast enhancement, and a high contrast transfer coefficient were associated with lower survival rates (P < .05). CONCLUSIONS: In patients with glioma, those with a high contrast transfer coefficient have lower survival than those with low parameters.
BACKGROUND AND PURPOSE: The prognostic value of dynamic contrast-enhanced MR imaging-derived plasma volume obtained in tumor and the contrast transfer coefficient has not been well-established in patients with gliomas. We determined whether plasma volume and contrast transfer coefficient in tumor correlated with survival in patients with gliomas in addition to other factors such as age, type of surgery, preoperative Karnofsky score, contrast enhancement, and histopathologic grade. MATERIALS AND METHODS: This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. The contrast transfer coefficient and plasma volume obtained in tumor maps were calculated directly from the signal-intensity curve without T1 measurements, and values were obtained from multiple small ROIs placed within tumors. Survival curve analysis was performed by dichotomizing patients into groups of high and low contrast transfer coefficient and plasma volume. Univariate analysis was performed by using dynamic contrast-enhanced parameters and clinical factors. Factors that were significant on univariate analysis were entered into multivariate analysis. RESULTS: For all patients with gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). In subgroups of high- and low-grade gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). Univariate analysis showed that factors associated with lower survival were age older than 50 years, low Karnofsky score, biopsy-only versus resection, marked contrast enhancement versus no/mild enhancement, high contrast transfer coefficient, and high plasma volume obtained in tumor (P < .05). In multivariate analysis, a low Karnofsky score, biopsy versus resection in combination with marked contrast enhancement, and a high contrast transfer coefficient were associated with lower survival rates (P < .05). CONCLUSIONS: In patients with glioma, those with a high contrast transfer coefficient have lower survival than those with low parameters.
Authors: Benjamin M Ellingson; Taryar Zaw; Timothy F Cloughesy; Kourosh M Naeini; Shadi Lalezari; Sandy Mong; Albert Lai; Phioanh L Nghiemphu; Whitney B Pope Journal: J Magn Reson Imaging Date: 2012-01-26 Impact factor: 4.813
Authors: T B Nguyen; G O Cron; J F Mercier; C Foottit; C H Torres; S Chakraborty; J Woulfe; G H Jansen; J M Caudrelier; J Sinclair; M J Hogan; R E Thornhill; I G Cameron Journal: AJNR Am J Neuroradiol Date: 2012-03-22 Impact factor: 3.825
Authors: Rajan Jain; Laila Poisson; Jayant Narang; David Gutman; Lisa Scarpace; Scott N Hwang; Chad Holder; Max Wintermark; Rivka R Colen; Justin Kirby; John Freymann; Daniel J Brat; Carl Jaffe; Tom Mikkelsen Journal: Radiology Date: 2012-12-13 Impact factor: 11.105
Authors: J L Boxerman; D E Prah; E S Paulson; J T Machan; D Bedekar; K M Schmainda Journal: AJNR Am J Neuroradiol Date: 2012-02-09 Impact factor: 3.825
Authors: Michael A Badruddoja; Hendrikus G J Krouwer; Scott D Rand; Kelly J Rebro; Arvind P Pathak; Kathleen M Schmainda Journal: Neuro Oncol Date: 2003-10 Impact factor: 12.300
Authors: T Hirai; R Murakami; H Nakamura; M Kitajima; H Fukuoka; A Sasao; M Akter; Y Hayashida; R Toya; N Oya; K Awai; K Iyama; J-i Kuratsu; Y Yamashita Journal: AJNR Am J Neuroradiol Date: 2008-06-12 Impact factor: 3.825
Authors: Brendan J McCullough; Valerie Ader; Brian Aguedan; Xu Feng; Daniel Susanto; Tara L Benkers; John W Henson; Marc Mayberg; Charles S Cobbs; Ryder P Gwinn; Stephen J Monteith; David W Newell; Johnny Delashaw; Sarah J Fouke; Steven Rostad; Bart P Keogh Journal: J Neurooncol Date: 2017-11-02 Impact factor: 4.130
Authors: Y S Choi; S S Ahn; H-J Lee; J H Chang; S-G Kang; E H Kim; S H Kim; S-K Lee Journal: AJNR Am J Neuroradiol Date: 2017-06-22 Impact factor: 3.825
Authors: Y S Choi; D W Kim; S-K Lee; J H Chang; S-G Kang; E H Kim; S H Kim; T H Rim; S S Ahn Journal: AJNR Am J Neuroradiol Date: 2015-09-03 Impact factor: 3.825
Authors: Julio Arevalo-Perez; Amanuel A Kebede; Kyung K Peck; Eli Diamond; Andrei I Holodny; Marc Rosenblum; Jennifer Rubel; Joshua Gaal; Vaios Hatzoglou Journal: J Neuroimaging Date: 2015-12-26 Impact factor: 2.486
Authors: Hee Soo Kim; Se Lee Kwon; Seung Hong Choi; Inpyeong Hwang; Tae Min Kim; Chul-Kee Park; Sung-Hye Park; Jae-Kyung Won; Il Han Kim; Soon Tae Lee Journal: Eur Radiol Date: 2020-01-17 Impact factor: 5.315