Analysis of Diffusion-weighted MR Images Based on a Gamma Distribution Model to Differentiate Prostate Cancers with Different Gleason Score

Gamma distribution model of diffusion MRI for evaluating the isocitrate dehydrogenase mutation status of glioblastomas

OBJECTIVE

To determine whether the γ distribution (GD) model of diffusion MRI is useful in the evaluation of the isocitrate dehydrogenase (IDH) mutation status of glioblastomas.

METHODS

12 patients with IDH-mutant glioblastomas and 54 patients with IDH-wildtype glioblastomas were imaged with diffusion-weighted imaging using 13 b-values from 0 to 1000 s/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) < 1.0×10-3 mm2/s; f2, D > 1.0×10-3 and <3.0×10-3 mm2/s; f3, D > 3.0 × 10-3 mm2/s. The GD model-derived parameters measured in gadolinium-enhancing lesions were compared between the IDH-mutant and IDH-wildtype groups. Receiver operating curve analyses were performed to assess the parameters' diagnostic performances.

RESULTS

The IDH-mutant group's f1 (0.474  ±  0.143) was significantly larger than the IDH-wildtype group's (0.347  ±  0.122, p = 0.0024). The IDH-mutant group's f2 (0.417  ±  0.131) was significantly smaller than the IDH-wildtype group's (0.504  ±  0.126, p = 0.036). The IDH-mutant group's f3 (0.109  ±  0.060) was significantly smaller than the IDH-wildtype group's (0.149  ±  0.063, p = 0.0466). The f1 showed the best diagnostic performance among the GD model-derived parameters with the area under the curve value of 0.753.

CONCLUSION

The GD model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and was useful in the differentiation of these tumors.

ADVANCES IN KNOWLEDGE

Diffusion MRI based on the γ distribution model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and its use enabled the significant differentiation of these tumors. The γ distribution model may contribute to the non-invasive identification of the IDH mutation status based on histological viewpoint.

The relationship between diffusion heterogeneity and microstructural changes in high-grade gliomas using Monte Carlo simulations

PURPOSE

Diffusion-weighted imaging (DWI) may aid accurate tumor grading. Decreased diffusivity and increased diffusion heterogeneity measures have been observed in high-grade gliomas using the non-monoexponential models for DWI. However, DWI measures concerning tissue characteristics in terms of pathophysiological and structural changes are yet to be established. Thus, this study aims to investigate the relationship between the diffusion measurements and microstructural changes in the presence of high-grade gliomas using a three-dimensional Monte Carlo simulation with systematic changes of microstructural parameters.

METHODS

Water diffusion was simulated in a microenvironment along with changes associated with the presence of high-grade gliomas, including increases in cell density, nuclear volume, extracellular volume (VF_ex), and extracellular tortuosity (λ_ex), and changes in membrane permeability (P_mem). DWI signals were simulated using a pulsed gradient spin-echo sequence. The sequence parameters, including the maximum gradient strength and diffusion time, were set to be comparable to those of clinical scanners and advanced human MRI systems. The DWI signals were fitted using the gamma distribution and diffusional kurtosis models with b-values up to 6000 and 2500 s/mm², respectively.

RESULTS

The diffusivity measures (apparent diffusion coefficients (ADC), D_gamma of the gamma distribution model and D_app of the diffusional kurtosis model) decreased with increases in cell density and λ_ex, and a decrease in P_mem. These diffusivity measures increased with increases in nuclear volume and VF_ex. The diffusion heterogeneity measures (σ_gamma of the gamma distribution model and K_app of the diffusional kurtosis model) increased with increases in cell density or nuclear volume at the low P_mem, and a decrease in P_mem. Increased σ_gamma was also associated with an increase in VF_ex.

CONCLUSION

Among simulated microstructural changes, only increases in cell density at low P_mem or decreases in P_mem corresponded to both the decreased diffusivity and increased diffusion heterogeneity measures. The results suggest that increases in cell density at low P_mem or decreases in P_mem may be associated with the diffusion changes observed in high-grade gliomas.

Value of MRI texture analysis for predicting new Gleason grade group

OBJECTIVES

To explore the potential value of multiparametric magnetic resonance imaging (mpMRI) texture analysis (TA) to predict new Gleason Grade Group (GGG).

METHODS

Fifty-eight lesions of fifty patients who underwent mpMRI scanning, including T₂-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters were obtained by the postprocessing software, and each lesion was assigned to its corresponding GGG. TA parameters derived from T2WI and DWI were statistically analyzed in detail.

RESULTS

Energy, inertia, and correlation derived from apparent diffusion coefficient (ADC) maps and T2WI had a statistically significant difference among the five groups. Kurtosis, energy, inertia, correlation on ADC maps and Energy, inertia on T2WI were moderately related to the GGG trend. ADC-energy and T2-energy were significant independent predictors of the GGG trend. ADC-energy, T2WI-energy, and T2WI-correlation had a statistically significant difference between GGG1 and GGG2-5. ADC-energy were significant independent predictors of the GGG1. ADC-energy, T2WI-energy, and T2WI-correlation showed satisfactory diagnostic efficiency of GGG1 (area under the curve (AUC) 84.6, 74.3, and 83.5%, respectively), and ADC-energy showed excellent sensitivity and specificity (88.9 and 95.1%, respectively).

CONCLUSION

TA parameters ADC-energy and T2-energy played an important role in predicting GGG trend. Both ADC-energy and T2-correlation produced a high diagnostic power of GGG1, and ADC-energy was perfect predictors of GGG1.

ADVANCES IN KNOWLEDGE

TA parameters were innovatively used to predict new GGG trend, and the predictive factors of GGG1 were screen out.

Gamma distribution model of diffusion MRI for the differentiation of primary central nerve system lymphomas and glioblastomas

The preoperative imaging-based differentiation of primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBs) is of high importance since the therapeutic strategies differ substantially between these tumors. In this study, we investigate whether the gamma distribution (GD) model is useful in this differentiation of PNCSLs and GBs. Twenty-seven patients with PCNSLs and 57 patients with GBs were imaged with diffusion-weighted imaging using 13 b-values ranging from 0 to 1000 sec/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) <1.0×10-3 mm2/sec; f2, D >1.0×10-3 and <3.0×10-3 mm2/sec; f3, D >3.0 × 10-3 mm2/sec. The GD model-derived parameters were compared between PCNSLs and GBs. Receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. The correlations with intravoxel incoherent motion (IVIM)-derived parameters were evaluated. The PCNSL group's κ (2.26 ± 1.00) was significantly smaller than the GB group's (3.62 ± 2.01, p = 0.0004). The PCNSL group's f1 (0.542 ± 0.107) was significantly larger than the GB group's (0.348 ± 0.132, p<0.0001). The PCNSL group's f2 (0.372 ± 0.098) was significantly smaller than the GB group's (0.508 ± 0.127, p<0.0001). The PCNSL group's f3 (0.086 ± 0.043) was significantly smaller than the GB group's (0.144 ± 0.062, p<0.0001). The combination of κ, f1, and f3 showed excellent diagnostic performance (area under the curve, 0.909). The f1 had an almost perfect inverse correlation with D. The f2 and f3 had very strong positive correlations with D and f, respectively. The GD model is useful for the differentiation of GBs and PCNSLs.