Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study

Abstract

Background: To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging models in differentiating tumour progression from pseudoprogression in glioblastoma patients.

Methods: Forty patients with pathologically confirmed glioblastoma exhibiting enhancing lesions after completion of chemoradiation therapy were enrolled in the study, which were then classified as tumour progression and pseudoprogression. All patients underwent conventional and multi-b diffusion-weighted MRI. The apparent diffusion coefficient (ADC) from a monoexponential model, the true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched-exponential model were compared between tumour progression and pseudoprogression groups. Receiver operating characteristic curves (ROC) analysis was used to investigate the diagnostic performance of different DWI parameters. Interclass correlation coefficient (ICC) was used to evaluate the consistency of measurements.

Results: The values of ADC, D, DDC, and α values were lower in tumour progression patients than that in pseudoprogression patients (p < 0.05). The values of D* and f were higher in tumour progression patients than that in pseudoprogression patients (p < 0.05). Diagnostic accuracy for differentiating tumour progression from pseudoprogression was highest for α(AUC = 0.94) than that for ADC (AUC = 0.91), D (AUC = 0.92), D* (AUC = 0.81), f (AUC = 0.75), and DDC (AUC = 0.88).

Conclusions: Multi-b DWI is a promising method for differentiating tumour progression from pseudoprogression with high diagnostic accuracy. In addition, the α derived from stretched-exponential model is the most promising DWI parameter for the prediction of tumour progression in glioblastoma patients.

Keywords: Diffusion-weighted imaging; Glioblastoma; MRI; Pseudoprogression; Tumour progression.

Fig. 1

Flow chart of the study population selection

Fig. 2

Bland–Altman plots with 95% CIs show moderate to good interobserver agreement for ADC, D, D*, f, DDC, and α values derived from the monoexponential, biexponential, and stretched-exponential DWI models

Fig. 3

Representative images of a patient with tumour progression. Axial FLAIR (a) and contrast-enhanced T1WI (b) demonstrated a newly enhanced lesion in the right frontal lobe. The lesion grew after six cycles of temozolomide chemoradiation, implying that the lesion had progressed. The ADC (c), D (d), D* (e), f (f), DDC (g) and α (h) maps were calculated automatically from the MADC software

Fig. 4

Representative images of a patient with pseudoprogression. Axial FLAIR (a) and contrast-enhanced T1WI (b) showed a necrotic contrast-enhancing lesion in the right temporal lobe. The lesion disappeared after 6 cycles of temozolomide chemoradiation, which was defined as pseudoprogression. The ADC (c), D (d), D* (e), f (f), DDC (g) and α (h) maps were calculated automatically from the MADC software

Fig. 5

Histogram plots of ADC, D, D*, f, DDC, and α values derived from multi-b DWI to distinguish the tumour progression and pseudoprogression groups. The ADC, D, DDC, and α values were significantly lower in the tumour progression group than in the pseudoprogression group (all p < 0.05). The f and D* values were significantly higher in the tumour progression group than in the pseudoprogression group (all p < 0.05)

Fig. 6

ROC curves show the diagnostic performance of ADC, D, D*, f, DDC, and α values derived from multi-b DWI in distinguishing tumour progression from pseudoprogression in patients with glioblastoma