PURPOSE: To augment the analysis of dynamic susceptibility contrast material-enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. MATERIALS AND METHODS: Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. RESULTS: The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. CONCLUSION: Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication.
PURPOSE: To augment the analysis of dynamic susceptibility contrast material-enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. MATERIALS AND METHODS: Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. RESULTS: The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. CONCLUSION: Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication.
Authors: Eric L Chang; Serap Akyurek; Tedde Avalos; Neal Rebueno; Chris Spicer; John Garcia; Robin Famiglietti; Pamela K Allen; K S Clifford Chao; Anita Mahajan; Shiao Y Woo; Moshe H Maor Journal: Int J Radiat Oncol Biol Phys Date: 2007-02-15 Impact factor: 7.038
Authors: H J Aronen; I E Gazit; D N Louis; B R Buchbinder; F S Pardo; R M Weisskoff; G R Harsh; G R Cosgrove; E F Halpern; F H Hochberg Journal: Radiology Date: 1994-04 Impact factor: 11.105
Authors: Spyridon Bakas; Hamed Akbari; Jared Pisapia; Maria Martinez-Lage; Martin Rozycki; Saima Rathore; Nadia Dahmane; Donald M O'Rourke; Christos Davatzikos Journal: Clin Cancer Res Date: 2017-04-20 Impact factor: 12.531
Authors: Luke Macyszyn; Hamed Akbari; Jared M Pisapia; Xiao Da; Mark Attiah; Vadim Pigrish; Yingtao Bi; Sharmistha Pal; Ramana V Davuluri; Laura Roccograndi; Nadia Dahmane; Maria Martinez-Lage; George Biros; Ronald L Wolf; Michel Bilello; Donald M O'Rourke; Christos Davatzikos Journal: Neuro Oncol Date: 2015-07-16 Impact factor: 12.300
Authors: SoHyun Han; Radka Stoyanova; Hansol Lee; Sean D Carlin; Jason A Koutcher; HyungJoon Cho; Ellen Ackerstaff Journal: Magn Reson Med Date: 2017-07-20 Impact factor: 4.668