Julio Arevalo-Perez1, Amanuel A Kebede1, Kyung K Peck1,2, Eli Diamond3, Andrei I Holodny1,4, Marc Rosenblum5, Jennifer Rubel1, Joshua Gaal1, Vaios Hatzoglou1,4. 1. Department of Radiology, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, New York, NY. 2. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY. 3. Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY. 4. Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY. 5. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY.
Abstract
BACKGROUND AND PURPOSE: Low-grade and anaplastic oligodendrogliomas are often difficult to differentiate on the basis of conventional MR imaging characteristics. Dynamic contrast-enhanced (DCE) MRI can assess tumor microvasculature and has demonstrated utility for predicting glioma grade and prognosis in primary brain tumors. The aim of our study was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (K(trans) ) derived from DCE MRI in differentiating between grade II and grade III oligodendrogliomas. MATERIALS AND METHODS: Twenty-four consecutive patients with pathologically confirmed oligodendroglioma (World Health Organization grade II, n = 14 and grade III, n = 10) were retrospectively assessed. Pretreatment DCE MRI was performed and regions of interest were manually drawn around the entire tumor volume to calculate Vp and K(trans) . The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were performed to compare pharmacokinetic parameters between the 2 groups. RESULTS: The Vpmean values for grade III oligodendrogliomas were significantly higher (P = .03) than those for grade II oligodendrogliomas. The K(trans) mean values were higher in grade III lesions, but the difference between the 2 groups was not statistically significant (P > .05). Based on ROC analysis, the Vpmean (area under curve = .757, SD = .1) cut-off value that provided the best combination of high sensitivity and specificity to distinguish between grade II and III oligodendrogliomas was 2.35 (P < .03). CONCLUSION: The results of our study suggest the DCE MRI parameter Vpmean can noninvasively differentiate between grade II and grade III oligodendrogliomas.
BACKGROUND AND PURPOSE: Low-grade and anaplastic oligodendrogliomas are often difficult to differentiate on the basis of conventional MR imaging characteristics. Dynamic contrast-enhanced (DCE) MRI can assess tumor microvasculature and has demonstrated utility for predicting glioma grade and prognosis in primary brain tumors. The aim of our study was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (K(trans) ) derived from DCE MRI in differentiating between grade II and grade III oligodendrogliomas. MATERIALS AND METHODS: Twenty-four consecutive patients with pathologically confirmed oligodendroglioma (World Health Organization grade II, n = 14 and grade III, n = 10) were retrospectively assessed. Pretreatment DCE MRI was performed and regions of interest were manually drawn around the entire tumor volume to calculate Vp and K(trans) . The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were performed to compare pharmacokinetic parameters between the 2 groups. RESULTS: The Vpmean values for grade III oligodendrogliomas were significantly higher (P = .03) than those for grade II oligodendrogliomas. The K(trans) mean values were higher in grade III lesions, but the difference between the 2 groups was not statistically significant (P > .05). Based on ROC analysis, the Vpmean (area under curve = .757, SD = .1) cut-off value that provided the best combination of high sensitivity and specificity to distinguish between grade II and III oligodendrogliomas was 2.35 (P < .03). CONCLUSION: The results of our study suggest the DCE MRI parameter Vpmean can noninvasively differentiate between grade II and grade III oligodendrogliomas.
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: M Vittoria Spampinato; J Keith Smith; Lester Kwock; Matthew Ewend; John D Grimme; Daniel L A Camacho; Mauricio Castillo Journal: AJR Am J Roentgenol Date: 2007-01 Impact factor: 3.959
Authors: Julio Arevalo-Perez; Kyung K Peck; Robert J Young; Andrei I Holodny; Sasan Karimi; John K Lyo Journal: J Neuroimaging Date: 2015-04-13 Impact factor: 2.486
Authors: S Fellah; D Caudal; A M De Paula; P Dory-Lautrec; D Figarella-Branger; O Chinot; P Metellus; P J Cozzone; S Confort-Gouny; B Ghattas; V Callot; N Girard Journal: AJNR Am J Neuroradiol Date: 2012-12-06 Impact factor: 3.825
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: T Sugahara; Y Korogi; M Kochi; I Ikushima; T Hirai; T Okuda; Y Shigematsu; L Liang; Y Ge; Y Ushio; M Takahashi Journal: AJR Am J Roentgenol Date: 1998-12 Impact factor: 3.959
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: 2014-06-19 Impact factor: 3.825
Authors: Harrison Kim; Mina Mousa; Patrick Schexnailder; Robert Hergenrother; Mark Bolding; Bernard Ntsikoussalabongui; Vinoy Thomas; Desiree E Morgan Journal: Med Phys Date: 2017-08-12 Impact factor: 4.071
Authors: Hyun Jung Yoon; Kook Jin Ahn; Song Lee; Jin Hee Jang; Hyun Seok Choi; So Lyung Jung; Bum Soo Kim; Shin Soo Jeun; Yong Kil Hong Journal: Neuroradiology Date: 2017-05-26 Impact factor: 2.804
Authors: Martin D Holland; Andres Morales; Sean Simmons; Brandon Smith; Samuel R Misko; Xiaoyu Jiang; David A Hormuth; Chase Christenson; Roy P Koomullil; Desiree E Morgan; Yufeng Li; Junzhong Xu; Thomas E Yankeelov; Harrison Kim Journal: Med Phys Date: 2021-12-10 Impact factor: 4.506
Authors: Stefan Hindel; Anika Söhner; Marc Maaß; Wolfgang Sauerwein; Dorothe Möllmann; Hideo Andreas Baba; Martin Kramer; Lutz Lüdemann Journal: PLoS One Date: 2017-01-31 Impact factor: 3.240
Authors: Vaios Hatzoglou; Jamie Tisnado; Alpesh Mehta; Kyung K Peck; Mariza Daras; Antonio M Omuro; Kathryn Beal; Andrei I Holodny Journal: Cancer Med Date: 2017-03-17 Impact factor: 4.452