Comparison of DWI based on monoexponential, biexponential and stretched-exponential models in differentiating tumor recurrence and pseudoprogression of glioblastoma / 中国医学影像技术
Chinese Journal of Medical Imaging Technology
;
(12): 1450-1455, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-861193
ABSTRACT
Objective:
To investigate the value of parameters derived from monoexponential, biexponential and stretched exponential intravoxel incoherent motion (IVIM) DWI models in differentiating tumor recurrence and pseudoprogression in patients with glioblastoma.Methods:
Totally 38 patients with pathologically confirmed glioblastoma after surgery, chemotherapy and radiation therapy were enrolled and divided into tumor recurrence group (n=20) and pseudoprogression group (n=18) according to follow-up or reoperation results. IVIM DWI was performed, and the parameters of monoexponential model (ADC), biexponential model (true molecular diffusion coefficient [D], pseudo-diffusion coefficient [D*], perfusion fraction [f]) and stretched exponential model (distributed diffusion coefficient [DDC], stretched index [α]) were measured and compared between the 2 groups. ROC curve was used to analyze the efficacy of these parameters in differentiating tumor progression and pseudoprogression.Results:
ADC, D, DDC and α values were significantly lower in tumor recurrence group than those in pseudoprogression group, while D* and f were significantly higher than those in pseudoprogression group (all P<0.05). ROC curve analysis showed AUC of ADC, D, D*, f, DDC and α value for differentiating tumor recurrence and pseudoprogression was 0.908, 0.925, 0.804, 0.743, 0.901 and 0.961, respectively.Conclusion:
Parameters derived from monoexponential, biexponential and stretched exponential IVIM models have great value in differentiating tumor recurrence from pseudoprogression in patients with glioblastoma, and α from stretched exponential model has higher efficacy.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Prognostic study
Language:
Chinese
Journal:
Chinese Journal of Medical Imaging Technology
Year:
2019
Type:
Article
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