BACKGROUND AND PURPOSE: Inclusion of oligodendroglial tumors may confound the utility of MR based glioma grading. Our aim was, first, to assess retrospectively whether a histogram-analysis method of MR perfusion images may both grade gliomas and differentiate between low-grade oligodendroglial tumors with or without loss of heterozygosity (LOH) on 1p/19q and, second, to assess retrospectively whether low-grade oligodendroglial subtypes can be identified in a population of patients with high-grade and low-grade astrocytic and oligodendroglial tumors. MATERIALS AND METHODS: Fifty-two patients (23 women, 29 men; mean age, 52 years; range, 19-78 years) with histologically confirmed gliomas were imaged by using dynamic susceptibility contrast MR imaging at 1.5T. Relative cerebral blood volume (rCBV) maps were created, and 4 neuroradiologists defined the glioma volumes independently. Averaged over the 4 observers, a histogram-analysis method was used to assess the normalized histogram peak height of the glioma rCBV distributions. RESULTS: Of the 52 patients, 22 had oligodendroglial tumors. The histogram method was able to differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) (Mann-Whitney U test, P < .001) and to identify low-grade oligodendroglial subtypes (P = .009). The corresponding intraclass correlation coefficients were 0.902 and 0.801, respectively. The sensitivity and specificity in terms of differentiating low-grade oligodendroglial tumors without LOH on 1p/19q from the other tumors was 100% (6/6) and 91% (42/46), respectively. CONCLUSION: With histology as a reference, our results suggest that histogram analysis of MR imaging-derived rCBV maps can differentiate HGGs from LGGs as well as low-grade oligodendroglial subtypes with high interobserver agreement. Also, the method was able to identify low-grade oligodendroglial tumors without LOH on 1p/19q in a population of patients with astrocytic and oligodendroglial tumors.
BACKGROUND AND PURPOSE: Inclusion of oligodendroglial tumors may confound the utility of MR based glioma grading. Our aim was, first, to assess retrospectively whether a histogram-analysis method of MR perfusion images may both grade gliomas and differentiate between low-grade oligodendroglial tumors with or without loss of heterozygosity (LOH) on 1p/19q and, second, to assess retrospectively whether low-grade oligodendroglial subtypes can be identified in a population of patients with high-grade and low-grade astrocytic and oligodendroglial tumors. MATERIALS AND METHODS: Fifty-two patients (23 women, 29 men; mean age, 52 years; range, 19-78 years) with histologically confirmed gliomas were imaged by using dynamic susceptibility contrast MR imaging at 1.5T. Relative cerebral blood volume (rCBV) maps were created, and 4 neuroradiologists defined the glioma volumes independently. Averaged over the 4 observers, a histogram-analysis method was used to assess the normalized histogram peak height of the glioma rCBV distributions. RESULTS: Of the 52 patients, 22 had oligodendroglial tumors. The histogram method was able to differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) (Mann-Whitney U test, P < .001) and to identify low-grade oligodendroglial subtypes (P = .009). The corresponding intraclass correlation coefficients were 0.902 and 0.801, respectively. The sensitivity and specificity in terms of differentiating low-grade oligodendroglial tumors without LOH on 1p/19q from the other tumors was 100% (6/6) and 91% (42/46), respectively. CONCLUSION: With histology as a reference, our results suggest that histogram analysis of MR imaging-derived rCBV maps can differentiate HGGs from LGGs as well as low-grade oligodendroglial subtypes with high interobserver agreement. Also, the method was able to identify low-grade oligodendroglial tumors without LOH on 1p/19q in a population of patients with astrocytic and oligodendroglial tumors.
Authors: J S Smith; A Perry; T J Borell; H K Lee; J O'Fallon; S M Hosek; D Kimmel; A Yates; P C Burger; B W Scheithauer; R B Jenkins Journal: J Clin Oncol Date: 2000-02 Impact factor: 44.544
Authors: Kathleen M Schmainda; Scott D Rand; Allen M Joseph; Rebecca Lund; B Doug Ward; Arvind P Pathak; John L Ulmer; Michael A Badruddoja; Michael A Baddrudoja; Hendrikus G J Krouwer Journal: AJNR Am J Neuroradiol Date: 2004-10 Impact factor: 3.825
Authors: L S Hu; Z Kelm; P Korfiatis; A C Dueck; C Elrod; B M Ellingson; T J Kaufmann; J M Eschbacher; J P Karis; K Smith; P Nakaji; D Brinkman; D Pafundi; L C Baxter; B J Erickson Journal: AJNR Am J Neuroradiol Date: 2015-09-10 Impact factor: 3.825
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: Gene Young Cho; Linda Moy; Sungheon G Kim; Steven H Baete; Melanie Moccaldi; James S Babb; Daniel K Sodickson; Eric E Sigmund Journal: Eur Radiol Date: 2015-11-28 Impact factor: 5.315