PURPOSE: To retrospectively evaluate whether the mean, minimum, and maximum apparent diffusion coefficient (ADC) of glioblastomas obtained from pretreatment MR images is of prognostic value in patients with glioblastoma. MATERIALS AND METHODS: The institutional review board approved our study and waived the requirement for informed patient consent. Between February 1998 and January 2006, 33 patients (24 males, 9 females; age range 10-76 years) with supratentorial glioblastoma underwent pretreatment magnetic resonance (MR) imaging. The values of the mean, minimum, and maximum ADC (ADC(mean), ADC(MIN), and ADC(MAX), respectively) of each tumor were preoperatively determined from several regions of interest defined in the tumors. After surgical intervention, all patients underwent irradiation and chemotherapy performed according to our hospital protocol. The patient age, symptom duration, Karnofsky performance scale score, extent of surgery, and ADC were assessed using factor analysis of overall survival. Prognostic factors were evaluated using Kaplan-Meier survival curves, the log-rank test, and multiple regression analysis with the Cox proportional hazards model. RESULTS: Likelihood ratio tests confirmed that ADC(MIN) was the strongest among the three prognostic factors. Total surgical removal was the most important predictive factor for overall survival (P<0.01). ADC(MIN) was also statistically correlated with overall survival (P<0.05) and could be used to classify patients into different prognostic groups. Interestingly, ADC(MIN) was also the strongest prognostic factor (P<0.01) in the group of patients in whom total tumor removal was not possible. CONCLUSION: The ADC(MIN) value obtained from pretreatment MR images is a useful clinical prognostic biomarker in patients with glioblastoma. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.
PURPOSE: To retrospectively evaluate whether the mean, minimum, and maximum apparent diffusion coefficient (ADC) of glioblastomas obtained from pretreatment MR images is of prognostic value in patients with glioblastoma. MATERIALS AND METHODS: The institutional review board approved our study and waived the requirement for informed patient consent. Between February 1998 and January 2006, 33 patients (24 males, 9 females; age range 10-76 years) with supratentorial glioblastoma underwent pretreatment magnetic resonance (MR) imaging. The values of the mean, minimum, and maximum ADC (ADC(mean), ADC(MIN), and ADC(MAX), respectively) of each tumor were preoperatively determined from several regions of interest defined in the tumors. After surgical intervention, all patients underwent irradiation and chemotherapy performed according to our hospital protocol. The patient age, symptom duration, Karnofsky performance scale score, extent of surgery, and ADC were assessed using factor analysis of overall survival. Prognostic factors were evaluated using Kaplan-Meier survival curves, the log-rank test, and multiple regression analysis with the Cox proportional hazards model. RESULTS: Likelihood ratio tests confirmed that ADC(MIN) was the strongest among the three prognostic factors. Total surgical removal was the most important predictive factor for overall survival (P<0.01). ADC(MIN) was also statistically correlated with overall survival (P<0.05) and could be used to classify patients into different prognostic groups. Interestingly, ADC(MIN) was also the strongest prognostic factor (P<0.01) in the group of patients in whom total tumor removal was not possible. CONCLUSION: The ADC(MIN) value obtained from pretreatment MR images is a useful clinical prognostic biomarker in patients with glioblastoma. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.
Authors: Chih-Chun Wu; Rajan Jain; Lucidio Neto; Seema Patel; Laila M Poisson; Jonathan Serrano; Victor Ng; Sohil H Patel; Dimitris G Placantonakis; David Zagzag; John Golfinos; Andrew S Chi; Matija Snuderl Journal: Neuroradiology Date: 2019-05-27 Impact factor: 2.804
Authors: Lei Qin; Xiang Li; Amanda Stroiney; Jinrong Qu; Jeffrey Helgager; David A Reardon; Geoffrey S Young Journal: Neuroradiology Date: 2017-01-09 Impact factor: 2.804
Authors: Jennifer C Ho; Pamela K Allen; Priya R Bhosale; Gaiane M Rauch; Clifton D Fuller; Abdallah S R Mohamed; Michael Frumovitz; Anuja Jhingran; Ann H Klopp Journal: Int J Radiat Oncol Biol Phys Date: 2016-11-17 Impact factor: 7.038