Malte Knutsson, Tim Salomonsson, Faris Durmo, Emelie Ryd Johansson, Anina Seidemo, Jimmy Lätt, Anna Rydelius, Sara Kinhult, Elisabet Englund, Johan Bengzon, Peter C M van Zijl, Linda Knutsson, Pia C Sundgren
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引用次数: 0
Abstract
Objectives: Early diagnostic separation between glioblastoma (GBM) and solitary metastases (MET) is important for patient management but remains challenging when based on imaging only. The objective of this study was to assess whether amide proton transfer weighted (APTw) MRI alone or combined with dynamic susceptibility contrast (DSC) MRI parameters, including cerebral blood volume (CBV), cerebral blood flow (CBF), and leakage parameter (K2) measurements, can differentiate GBM from MET.
Methods: APTw MRI and DSC-MRI were performed on 18 patients diagnosed with GBM (N = 10) or MET (N = 8). Quantitative parameter maps were calculated, and regions-of-interest (ROIs) were placed in whole tumor, contrast-enhanced tumor (ET), edema, necrosis and normal-appearing white matter (NAWM). The mean and max of the APTw signal, CBF, leakage-corrected CBV and K2 were obtained from each ROI. Except for K2, all were normalized to NAWM (nAPTwmean/max, nCBFmean/max, ncCBVmean/max,). Receiver Operating Characteristic (ROC) curves and area-under-the-curve (AUC) were assessed for different parameter combinations. Statistical analyses were performed using Mann-Whitney U test.
Results: When comparing GBM to MET, nAPTmax, nCBFmax, ncCBVmax and ncCBVmean were significantly increased (p < 0.05) in ET with AUC being 0.81, 0.83, 0.85, and 0.83, respectively. Combinations of nAPTwmax + ncCBVmax, nAPTwmean + ncCBVmean, nAPTwmax + nCBFmax, nAPTwmax + K2max and nAPTwmax + ncCBVmax + K2max in ET showed significant prediction in differentiating GBM and MET (AUC = 0.92, 0.82, 0.92, 0.85, and 0.92 respectively).
Conclusion: When assessed in Gd-enhanced tumor areas, nAPTw MRI signal intensity alone or combined with DSC-MRI parameters, was an excellent predictor for differentiating GBM and MET. However, the small cohort warrants future studies.
期刊介绍:
Neural Technology is devoted to the convergence between neurobiology and quantum-, nano- and micro-sciences. In our vision, this interdisciplinary approach should go beyond the technological development of sophisticated methods and should contribute in generating a genuine change in our discipline.