Kambiz Nael1, Adam H Bauer2, Adilia Hormigo3, Michael Lemole4, Isabelle M Germano5, Josep Puig6, Baldassarre Stea4. 1. 1 Department of Radiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl, Box 1234, New York, NY 10029. 2. 2 Department of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA. 3. 3 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY. 4. 4 Departments of Neurosurgery and Radiation Oncology, University of Arizona, Tucson, AZ. 5. 5 Departments of Neurosurgery and Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY. 6. 6 Department of Radiology, Hospital Universitari Dr. Josep Trueta, University of Girona, Girona, Spain.
Abstract
OBJECTIVE: Differentiation of radiation necrosis (RN) from recurrent tumor (RT) in treated patients with glioblastoma remains a diagnostic challenge. The purpose of this study is to evaluate the diagnostic performance of multiparametric MRI in distinguishing RN from RT in patients with glioblastoma, with the use of a combination of MR perfusion and diffusion parameters. MATERIALS AND METHODS: Patients with glioblastoma who had a new enhancing mass develop after completing standard treatment were retrospectively evaluated. Apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), and relative cerebral blood volume (rCBV) values were calculated from the MR images on which the enhancing lesions first appeared. Repeated measure of analysis, logistic regression, and ROC analysis were performed. RESULTS: Of a total of 70 patients evaluated, 46 (34 with RT and 12 with RN) met our inclusion criteria. Patients with RT had significantly higher mean rCBV (p < 0.001) and Ktrans (p = 0.006) values and lower ADC values (p = 0.004), compared with patients with RN. The overall diagnostic accuracy was 85.8% for rCBV, 75.5% for Ktrans, and 71.3% for ADC values. The logistic regression model showed a significant contribution of rCBV (p = 0.024) and Ktrans (p = 0.040) as independent imaging classifiers for differentiation of RT from RN. Combined use of rCBV and Ktrans at threshold values of 2.2 and 0.08 min-1, respectively, improved the overall diagnostic accuracy to 92.8%. CONCLUSION: In patients with treated glioblastoma, rCBV outperforms ADC and Ktrans as a single imaging classifier to predict recurrent tumor versus radiation necrosis; however, the combination of rCBV and Ktrans may be used to improve overall diagnostic accuracy.
OBJECTIVE:Differentiation of radiation necrosis (RN) from recurrent tumor (RT) in treated patients with glioblastoma remains a diagnostic challenge. The purpose of this study is to evaluate the diagnostic performance of multiparametric MRI in distinguishing RN from RT in patients with glioblastoma, with the use of a combination of MR perfusion and diffusion parameters. MATERIALS AND METHODS:Patients with glioblastoma who had a new enhancing mass develop after completing standard treatment were retrospectively evaluated. Apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), and relative cerebral blood volume (rCBV) values were calculated from the MR images on which the enhancing lesions first appeared. Repeated measure of analysis, logistic regression, and ROC analysis were performed. RESULTS: Of a total of 70 patients evaluated, 46 (34 with RT and 12 with RN) met our inclusion criteria. Patients with RT had significantly higher mean rCBV (p < 0.001) and Ktrans (p = 0.006) values and lower ADC values (p = 0.004), compared with patients with RN. The overall diagnostic accuracy was 85.8% for rCBV, 75.5% for Ktrans, and 71.3% for ADC values. The logistic regression model showed a significant contribution of rCBV (p = 0.024) and Ktrans (p = 0.040) as independent imaging classifiers for differentiation of RT from RN. Combined use of rCBV and Ktrans at threshold values of 2.2 and 0.08 min-1, respectively, improved the overall diagnostic accuracy to 92.8%. CONCLUSION: In patients with treated glioblastoma, rCBV outperforms ADC and Ktrans as a single imaging classifier to predict recurrent tumor versus radiation necrosis; however, the combination of rCBV and Ktrans may be used to improve overall diagnostic accuracy.
Authors: Carrie M Carr; John C Benson; David R DeLone; Felix E Diehn; Dong Kun Kim; Kenneth W Merrell; Alex A Nagelschneider; Ajay A Madhavan; Derek R Johnson Journal: Neuroradiology Date: 2021-01-04 Impact factor: 2.804
Authors: F Kuo; N N Ng; S Nagpal; E L Pollom; S Soltys; M Hayden-Gephart; G Li; D E Born; M Iv Journal: AJNR Am J Neuroradiol Date: 2022-04-28 Impact factor: 4.966