Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit, relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion

Correlation between diffusion-weighted image-derived parameters and dynamic contrast-enhanced magnetic resonance imaging-derived parameters in the orofacial region

Background

Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are widely used in the orofacial region. Furthermore, quantitative analyses have proven useful. However, a few reports have described the correlation between DWI-derived parameters and DCE-MRI-derived parameters, and the results have been controversial.

Purpose

To evaluate the correlation among parameters obtained by DWI and DCE-MRI and to compare them between benign and malignant lesions.

Material and Methods

Fifty orofacial lesions were analysed. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) were estimated by DWI. For DCE-MRI, TK model analysis was performed to estimate physiological parameters, for example, the influx forward volume transfer constant into the extracellular-extravascular space (EES) (Ktrans) and fractional volumes of EES and plasma components (ve and vp).

Results

Both ADC and D showed a moderate positive correlation with ve (ρ = 0.640 and 0.645, respectively). Ktrans showed a marginally weak correlation with f (ρ = 0.296), while vp was not correlated with f or D*; therefore, IVIM perfusion-related parameters and TK model perfusion-related parameters were not straightforward. Both D and ve yielded high diagnostic power between benign lesions and malignant tumours with areas under the curve (AUCs) of 0.830 and 0.782, respectively.

Conclusion

Both D and ve were reliable parameters that were useful for the differential diagnosis. In addition, the true diffusion coefficient (D) was affected by the fractional volume of EES.

Characteristic Mean Kurtosis Values in Simple Diffusion Kurtosis Imaging of Dentigerous Cysts

We evaluated the usefulness of simple diffusion kurtosis (SD) imaging, which was developed to generate diffusion kurtosis images simultaneously with an apparent diffusion coefficient (ADC) map for 27 cystic disease lesions in the head and neck region. The mean kurtosis (MK) and ADC values were calculated for the cystic space. The MK values were dentigerous cyst (DC): 0.74, odontogenic keratocyst (OKC): 0.86, ranula (R): 0.13, and mucous cyst (M): 0, and the ADC values were DC: 1364 × 10-6 mm2/s, OKC: 925 × 10-6 mm2/s, R: 2718 × 10-6 mm2/s, and M: 2686 × 10-6 mm2/s. The MK values of DC and OKC were significantly higher than those of R and M, whereas their ADC values were significantly lower. One reason for the characteristic signal values in diffusion-weighted images of DC may be related to content components such as fibrous tissue and exudate cells. When imaging cystic disease in the head and neck region using SD imaging, the maximum b-value setting at the time of imaging should be limited to approximately 1200 s/mm2 for accurate MK value calculation. This study is the first to show that the MK values of DC are characteristically higher than those of other cysts.

Non-Gaussian model-based diffusion-weighted imaging of oral squamous cell carcinoma: associations with Ki-67 proliferation status

OBJECTIVES

To investigate possible associations between diffusion-weighted imaging (DWI) parameters derived from a non-Gaussian model fitting and Ki-67 status in patients with oral squamous cell carcinoma (OSCC).

METHODS

Twenty-four patients with newly diagnosed OSCC were prospectively recruited. DWI was performed using six b-values (0-2500). The diffusion-related parameters of kurtosis value (K), kurtosis-corrected diffusion coefficient (DK), diffusion heterogeneity (α), distributed diffusion coefficient (DDC), slow diffusion coefficient (Dslow), and apparent diffusion coefficient (ADC) were calculated from four diffusion fitting models. Ki-67 status was categorized as low (Ki-67 percentage score < 20%), middle (20-50%), or high (> 50%). Kruskal-Wallis tests were performed between each non-Gaussian diffusion model parameters and Ki-67 grade.

RESULTS

The Kruskal-Wallis tests revealed that multiple parameters (K, ADC, Dk, DDC and Dslow) showed statistically significant differences between the three levels of Ki-67 status (K: p = 0.020, ADC: p = 0.012, Dk: p = 0.027, DDC: p = 0.007 and Dslow: p = 0.026).

CONCLUSIONS

Several non-Gaussian diffusion model parameters and ADC values were significantly associated with Ki-67 status and have potential as promising prognostic biomarkers in patients with OSCC.

A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status

OBJECTIVES

The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas.

METHODS

Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (D_int and D_p) and fractions of strictly limited, intermediate, and perfusion-related diffusion (F_very-slow, F_int, and F_p) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed.

RESULTS

High-grade gliomas (HGGs) had significantly higher F_int, F_very-slow, and D_p values but significantly lower F_p and D_int values than low-grade gliomas (LGGs), and F_int and F_p differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). F_int achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, F_int, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, F_int remained the only differentiator. Additionally, F_int and F_p showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively).

CONCLUSIONS

The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses.

KEY POINTS

• The three-component model enables the estimation of an intermediate diffusion component. • The three-component model performed better than the other models in glioma grading and genotyping. • F_int was useful in evaluating the grade and genotype of gliomas.

Predicting Grade of Esophageal Squamous Carcinoma: Can Stretched Exponential Model-Based DWI Perform Better Than Bi-Exponential and Mono-Exponential Model?

Background

To evaluate and compare the potential performance of various diffusion parameters obtained from mono-exponential model (MEM)-, bi-exponential model (BEM)-, and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in grading of esophageal squamous carcinoma (ESC).

Methods

Eighty-two patients with pathologically confirmed ESC without treatment underwent multi-b-value DWI scan with 13 b values (0~12,00 s/mm²). The apparent diffusion coefficient (ADC) deriving from the MEM; the pure molecular diffusion (ADC_slow), pseudo-diffusion coefficient (ADC_fast), perfusion, and fraction (f) deriving from the BEM; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) deriving from the SEM were calculated and compared between poorly differentiated and well/moderately differentiated ESC, respectively. The prediction parameters and diagnostic efficiency were compared by drawing receiver operating characteristic (ROC) curves.

Results

The ADC, ADC_slow, ADC_fast, and DDC in poorly ESC were significantly lower than those in well/moderately differentiated ones. By using only one parameter, ADC_slow, DDC had the moderate diagnostic efficiency and the areas under the curve (AUC) were 0.758 and 0.813 in differentiating ESC. The DDC had the maximum AUC with sensitivity (88.00%) and specificity (68.42%). Combining ADC with ADC_fast, ADC_slow, and DDC and combining ADC_slow with ADC_fast can provide a higher diagnostic accuracy with AUC ranging from 0.756, 0.771, 0.816, and 0.793, respectively.

Conclusion

Various parameters derived from different DWI models including MEM, BEM, and SEM were potentially helpful in grading ESC. DDC obtained from SEM was the most promising diffusion parameter for predicting the grade of ESC.

A comparison among gamma distribution, intravoxel incoherent motion, and mono-exponential models with turbo spin-echo diffusion-weighted MR imaging in the differential diagnosis of orofacial lesions

OBJECTIVES

To compare the gamma distribution (GD), intravoxel incoherent motion (IVIM), and monoexponential (ME) models in terms of their goodness-of-fit, correlations among the parameters, and the effectiveness in the differential diagnosis of various orofacial lesions.

METHODS

A total of 85 patients underwent turbo spin-echo diffusion-weighted imaging with six b-values. The goodness-of-fit of three models was assessed using Akaike Information Criterion. We analysed the correlations and compared the effectiveness in the differential diagnosis among the parameters of GD model (κ, shape parameter; θ, scale parameter; fractions of diffusion: ƒ₁, cellular component; ƒ₂, extracellular diffusion; ƒ₃, perfusion component), IVIM model (D, true diffusion coefficient; D*, pseudodiffusion coefficient; f, perfusion fraction), and ME model (apparent diffusion coefficient, ADC).

RESULTS

The GD and IVIM models showed a better goodness-of-fit than the ME model (p < 0.05). ƒ₁ had strong negative correlations with D and ADC (ρ = -0.901 and -0.937, respectively), while ƒ₃ had a moderate positive correlation with f (ρ = 0.661). Malignant entity presented significantly higher ƒ₁ and lower D and ADC than benign entity (p < 0.0001). Malignant lymphoma had significantly higher ƒ₁ in comparison to squamous cell carcinoma (p = 0.0007) and granulation (p = 0.0075). The trend in ƒ₁ was opposite to the trend in D. Malignant lymphoma had significant lower ƒ₃ than squamous cell carcinoma (p = 0.005) or granulation (p = 0.0075).

CONCLUSIONS

The strong correlations were found between the GD- and IVIM-derived parameters. Furthermore, the GD model's parameters were useful for characterising the pathological structure in orofacial lesions.

Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry

BACKGROUND/PURPOSE

Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance.

MATERIALS/METHODS

GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, T2 + ADC, T2 + Ktrans, T2 + Ve, all five channels [ALL]) primarily using the Dice similarity coefficient (DSC). False-negative DSC (FND), false-positive DSC, sensitivity, positive predictive value, surface DSC, Hausdorff distance (HD), 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a blinded Turing test using three physician observers.

RESULTS

Models yielded mean DSCs from 0.71 ± 0.12 (ALL) to 0.73 ± 0.12 (T2 + T1). Compared to the T2 model, performance was significantly improved for FND, sensitivity, surface DSC, HD, and 95% HD for the T2 + T1 model (p < 0.05) and for FND for the T2 + Ve and ALL models (p < 0.05). No model demonstrated significant correlations between tumor size and DSC (p > 0.05). Most models demonstrated significant correlations between tumor size and HD or Surface DSC (p < 0.05), except those that included ADC or Ve as input channels (p > 0.05). On average, there were no significant differences between ground truth and DL-generated segmentations for all observers (p > 0.05).

CONCLUSION

DL using mpMRI provides reasonably accurate segmentations of OPC GTVp that may be comparable to ground truth segmentations generated by clinical experts. Incorporating additional mpMRI channels may increase the performance of FND, sensitivity, surface DSC, HD, and 95% HD, and improve model robustness to tumor size.

Multiparametric Analysis of Tumor Morphological and Functional MR Parameters Potentially Predicts Local Failure in Pharynx Squamous Cell Carcinoma Patients

Purpose : To predict local control / failure by a multiparametric approach using magnetic resonance (MR)-derived tumor morphological and functional parameters in pharynx squamous cell carcinoma (SCC) patients. Materials and Methods : Twenty-eight patients with oropharyngeal and hypopharyngeal SCCs were included in this study. Quantitative morphological parameters and intratumoral characteristics on T2-weighted images, tumor blood flow from pseudo-continuous arterial spin labeling, and tumor diffusion parameters of three diffusion models from multi-b-value diffusion-weighted imaging as well as patients' characteristics were analyzed. The patients were divided into local control / failure groups. Univariate and multiparametric analysis were performed for the patient group division. Results : The value of morphological parameter of 'sphericity' and intratumoral characteristic of 'homogeneity' was revealed respectively significant for the prediction of the local control status in univariate analysis. Higher diagnostic performance was obtained with the sensitivity of 0.8, specificity of 0.75, positive predictive value of 0.89, negative predictive value of 0.6 and accuracy of 0.79 by multiparametric diagnostic model compared to results in the univariate analysis. Conclusion : A multiparametric analysis with MR-derived quantitative parameters may be useful to predict local control in pharynx SCC patients. J. Med. Invest. 68 : 354-361, August, 2021.

Preliminary quantitative analysis of vertebral microenvironment changes in type 2 diabetes mellitus using FOCUS IVIM-DWI and IDEAL-IQ sequences

PURPOSE

To explore the application of intravoxel incoherent motion diffusion-weighted imaging(IVIM-DWI) on account of field-of-view optimized and constrained undistorted single shot (FOCUS) and iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation(IDEAL-IQ) sequences in evaluating the vertebral microenvironment changes of type 2 diabetes mellitus(T2DM) patients and the correlation with bone mineral density(BMD).

METHOD

128 T2DM patients (mean age 63.4 ± 5.28 years) underwent both dual-energy X-ray absorptiometry (DEXA) and spine MRI. The FOCUS IVIM-DWI and IDEAL-IQ derived parameters of the vertebral body(L1, L2, L3, L4)were measured on corresponding maps of the lumbar spine. The subjects were divided into 3 groups according to T-scores as follows: normal (n = 37), osteopenia (n = 43), and osteoporosis(n = 48) group.One-way analysis of variance (ANOVA) were used to compare the vertebral parameters(ADC_slow, ADC_fast, f, FF, R2*) among three BMD cohorts.Receiver operating characteristic (ROC) analyses and Spearman's rank correlation were performed to test the diagnostic performance and the correlation between them respectively.

RESULTS

There were significant differences in vertebral ADC_slow, ADC_fast, FF and R2* between the three groups (P < 0.05).Statistically, BMD was moderately negatively correlated with FF (r = -0.584, P < 0.001) and weakly positively with ADC_slow (r = 0.334, P < 0.001), meanwhile moderately positively correlated with R2*(r = 0.509, P < 0.001) and ADC_fast(0.545, P < 0.001).ADC_fast was moderately negatively correlated with FF (r = -0.417, P < 0.001), weakly positively correlated with R2*(0.359, P < 0.001).Compared with the area under the curve (AUC) of ADC_slow, ADC_fast, FF and R2*, the AUC of ADC_fast was higher in identifying between normal and abnormal(osteopenia and osteoporosis), normal from osteopenia, while the AUC of FF was higher in identifying osteopenia from osteoporosis.

CONCLUSIONS

FOCUS IVIM-DWI and IDEAL-IQ of lumbar spine might be useful to evaluate the vertebral microenvironment changes of T2DM patients.

Magnetic resonance image biomarkers improve differentiation of benign and malignant parotid tumors through diagnostic model analysis

OBJECTIVES

To explore the effectiveness of magnetic resonance image (MRI)-based biomarkers for identifying benign and malignant parotid tumors via diagnostic model analysis.

METHODS

This retrospective study included 109 patients (development cohort and validation cohort) who underwent MRI preoperatively, including T1- and T2-weighted images. Parameters based on 2D or 3D texture analysis were extracted from tumor lesions by MaZda software, fisher discriminant and bootstrap method were used to perform parameter reduction, diagnostic models with the selected biomarkers were established along with clinical data, model performance (discrimination and calibration) was furtherly evaluated by internal and external validation, decision curve analysis was applied to measure the improvement of clinical benefits.

RESULTS

S(5,5) Entrop, S(0,1) ASM, WavEnHH (s-4), S(1,1,0) Entropy and Perc.10% were significantly associated with the pathological diagnosis of parotid tumor (benign versus malignancy), when adding these biomarkers to the regression analysis, model performance significantly improved in the development cohort (likelihood-ratio-test; p < 0.05, with an increase of AUC from 0.72 (reference model) to 0.85), and these results were maintained in a small external validation cohort. Decision curve analysis indicated that clinical benefit was greater with the application of MRI-based biomarkers.

CONCLUSIONS

MRI-based texture analysis is proven to be an effective tool in differentiating benign and malignant parotid tumors, preoperative diagnosis was improved with the selected biomarkers compared to the reference model.

Comparative study of imaging and histology of sacroiliac joint in normal rats based on IVIM-DWI and DCE-MRI

Background

Currently, few studies have described the relationship between functional MRI findings and histology of normal sacroiliac joint (SIJ). Besides, due to the difficulties in access to SIJ, authentic animal models are important in providing opportunities for quantitative parameter extraction on imaging.

Aims

This study aimed at exploring the parameters of Intravoxel Incoherent Motion Diffusion-Weighted Imaging (IVIM-DWI) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and comparing them with the histology of the SIJ in normal rats with different ages.

Methods

A total of thirty 7-week-old male Wistar rats were included in the study. The parameters of IVIM-DWI and DCE-MRI in the bone marrow and the joint space of SIJ were measured at 8, 13, 18, 23, 28, and 33 weeks. The histological analysis of the SIJ was examined using light microscopy. One-way ANOVA was used for statistical analysis.

Results

The D values in the sacral and iliac bone marrow of normal rats decreased with an increase in age. One-way ANOVA analysis indicated a significant difference in D values in different age groups (P<0.005). The normal values of D*, f, Fenh (%), Senh (%/s) in the sacral bone marrow, the iliac bone marrow, and the joint space in SIJ of normal rats were obtained. The results showed that in the six groups of rats of different ages, the histology of the SIJ surface was smooth and clear, the cartilage cells were intact, and no thickening or pannus formation was observed.

Conclusions

This study obtained the IVIM-DWI and DCE-MRI parameters of the sacral and iliac bone marrow and the synovial area of the joint space in normal rats. The parameters in normal rats can be used in future research to compare to similar parameters in animal models or patients with SIJ diseases. This study serves as a guide for future research in SIJ diseases.

Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging

The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.

Comparing mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted MR imaging for stratifying non-alcoholic fatty liver disease in a rabbit model

OBJECTIVES

To compare diffusion parameters obtained from mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) in stratifying non-alcoholic fatty liver disease (NAFLD).

METHODS

Thirty-two New Zealand rabbits were fed a high-fat/cholesterol or standard diet to obtain different stages of NAFLD before 12 b-values (0-800 s/mm²) DWI. The apparent diffusion coefficient (ADC) from the mono-exponential model; pure water diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f) from bi-exponential DWI; and distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from stretched-exponential DWI were calculated for hepatic parenchyma. The goodness of fit of the three models was compared. NAFLD severity was pathologically graded as normal, simple steatosis, borderline, and non-alcoholic steatohepatitis (NASH). Spearman rank correlation analysis and receiver operating characteristic curves were used to assess NAFLD severity.

RESULTS

Upon comparison, the goodness of fit chi-square from stretched-exponential fitting (0.077 ± 0.012) was significantly lower than that for the bi-exponential (0.110 ± 0.090) and mono-exponential (0.181 ± 0.131) models (p < 0.05). Seven normal, 8 simple steatosis, 6 borderline, and 11 NASH livers were pathologically confirmed from 32 rabbits. Both α and D increased with increasing NAFLD severity (r = 0.811 and 0.373, respectively; p < 0.05). ADC, f, and DDC decreased as NAFLD severity increased (r = - 0.529, - 0.717, and - 0.541, respectively; p < 0.05). Both α (area under the curve [AUC] = 0.952) and f (AUC = 0.931) had significantly greater AUCs than ADC (AUC = 0.727) in the differentiation of NASH from borderline or less severe groups (p < 0.05).

CONCLUSIONS

Stretched-exponential DWI with higher fitting efficiency performed, as well as bi-exponential DWI, better than mono-exponential DWI in the stratification of NAFLD severity.

KEY POINTS

• Stretched-exponential diffusion model fitting was more reliable than the bi-exponential and mono-exponential diffusion models (p = 0.039 and p < 0.001, respectively). • As NAFLD severity increased, the diffusion heterogeneity index (α) increased, while the perfusion fraction (f) decreased (r = 0.811, - 0.717, p < 0.05). • Both α and f showed superior NASH diagnostic performance (AUC = 0.952, 0.931) compared with ADC (AUC = 0.727, p < 0.05).

The value of diffusion-weighted imaging based on monoexponential and biexponential models for the diagnosis of benign and malignant lung nodules and masses

OBJECTIVES

The objective is to compare the efficacy of diffusion-weighted imaging (DWI) parameters of mean and minimum apparent diffusion coefficient (ADCmean and ADCmin) and intravoxel incoherent motion (IVIM) in the differentiation of benign and malignant lung nodules and masses.

METHODS

Lung lesions measured larger than 1.5 cm on CT were included between August 2015 and September 2018. DWI (10 b-values, 0-1000 s/mm2) scans were performed, and the data were post-processed to derive the ADCmean, ADCmin and IVIM parameters of true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f). An independent sample t-test or Mann-Whitney U test was used to compare benign and malignant parameters. Receiver operating characteristic curves were generated and a Z test was used.

RESULTS

121 patients were finally enrolled, each with one lesion. Examined 121 lesions were malignant in 88 (72.7%) and benign in 33 (27.3%). The ADCmean of malignant pulmonary nodules was significantly lower than that of benign pulmonary nodules (t = 3.156, p = 0.006), whereas the other parameters revealed no significant differences (p = 0.162-0.690). Receiver operating characteristic curve analysis revealed that an ADCmean threshold value of 1.43 × 10-3 mm2/s yielded 88.57% sensitivity and 64.29% specificity. While for lung masses, the ADCmean, ADCmin, D and D* values in malignant pulmonary masses were significantly lower (P﹤0.001-0.011). Among them, the D value exhibited the best diagnostic performance when the threshold of D was 1.23 × 10-3mm2/s, which yielded a sensitivity of 90.57% and a specificity of 89.47% (Z = 2.230, 3.958, 2.877 and p = 0.026, ﹤0.001 and 0.004, respectively).

CONCLUSION

ADC is the most robust parameter to differentiate benign and malignant lung nodules, whereas D is the most robust parameter to differentiate benign and malignant lung masses.

ADVANCES IN KNOWLEDGE

This is the first study to compare all the quantitative parameters of DWI and IVIM mentioned in the literatures for assessing lung lesions; Second, we divided the lesions into lung nodules and lung masses with the size of 3 cm as the boundary.

Diffusion kurtosis imaging in head and neck cancer: A correlation study with dynamic contrast enhanced MRI

PURPOSE

To investigate the biophysical meaning of Diffusion Kurtosis Imaging (DKI) parameters via correlations with the perfusion parameters obtained from a long Dynamic Contrast Enhanced MRI scan, in head and neck (HN) cancer.

METHODS

Twenty two patients with newly diagnosed HN tumor were included in the present retrospective study. Some patients had multiple lesions, therefore a total of 26 lesions were analyzed. DKI was acquired using 5b values at 0, 500, 1000,1500 and 2000 s/mm². DCE-MRI was obtained with 130 dynamic volumes, with a temporal resolution of 5 s, to achieve a long scan time (>10 min). The apparent diffusion coefficient D_app and apparent diffusional kurtosis K_app were calculated voxel-by-voxel, removing the point at b value = 0 to eliminate possible perfusion effects on the parameter estimations. The transfer constants K^trans and K_ep, v_e, and the histogram-based entropy (En) and interquartile range (IQR) of each DCE-MRI parameter were quantified. Correlations between all variables were investigated by the Spearman's Rho correlation test.

RESULTS

Moderate relationships emerged between D_app and K_ep (Rho =  - 0.510, p = 0.009), and between D_app and v_e (Rho = 0.418, p = 0.038). En(K_ep) was significantly related to K_app (Rho = 0.407, p = 0.043), while IQR(K_ep) showed an inverse association with D_app (Rho = -0.422, p = 0.035).

CONCLUSIONS

A weak to intermediate correlation was found between DKI parameters and both K_ep and v_e. The kurtosis was associated to the intratumoral heterogeneity and complexity of the capillary permeability, expressed by En(K_ep).

Evaluation of non-Gaussian model-based diffusion-weighted imaging in oral squamous cell carcinoma: comparison with tumour functional information derived from positron-emission tomography

AIM

To evaluate and compare diffusion-weighted imaging (DWI) parameters derived from a non-Gaussian fitting model and positron-emission tomography (PET) parameters derived from ¹⁸F-fluoromisonidazole-PET (FMISO-PET) in patients with oral squamous cell carcinoma (OSCC).

MATERIALS AND METHODS

Primary sites were evaluated prospectively in 18 patients. DWI was performed using six b-values (0-2,500). Diffusion-related parameters of kurtosis value (K), the kurtosis-corrected diffusion coefficient (D_K), diffusion heterogeneity (α), distributed diffusion coefficient (DDC), the slow diffusion coefficient (D_slow), and the apparent diffusion coefficient (ADC) were calculated from four diffusion-fitting models. Maximal standardised uptake values (SUV_max), mean standardised uptake values (SUV_mean), and the tumour-to-muscle ration (TMR) of the SUV value were calculated for FMISO-PET. Spearman's correlation coefficient was used to evaluate the correlation between each non-Gaussian diffusion model parameters and PET parameter.

RESULTS

There was moderate correlation between FMISO-PET SUV_max and D_slow (ρ=-0.45, p=0.06). In addition, there was good correlation between TMR_max and five non-Gaussian diffusion model parameters (K: ρ=0.65, p=0.004, D_K: ρ=-0.72, p=0.0008, DDC: ρ=-0.75, p=0.0003, ADC: ρ=-0.74, p=0.0005, and D_slow: ρ= -0.65, p=0.003), and between TMR_mean and five non-Gaussian model parameters (K: ρ=0.64, p=0.005, D_K: ρ=-0.61, p=0.007, DDC: ρ=-0.63, p=0.005, ADC: ρ=-0.61, p=0.007, and D_slow: ρ=-0.56, p=0.015).

CONCLUSION

Non-Gaussian diffusion model parameters can be related to tumour hypoxia.

Values of intravoxel incoherent motion diffusion weighted imaging and dynamic contrast-enhanced MRI in evaluating the activity of sacroiliitis in ankylosing spondylitis of rat model

OBJECTIVE

To prospectively evaluate the ability of IVIM-DWI and DCE-MRI in detecting early activity of sacroiliitis in rat model of ankylosing spondylitis by comparing with pathological results.

METHODS

20 wistar male rats were induced by bovine proteoglycan combined with complete/incomplete Freund's adjuvant as model group, and 20 healthy male rats were used as the control group. The parameters of IVIM-DWI and DCE-MRI in synovial regions of SIJ were measured respectively at 7th, 12th, 17th, and 22th weeks after the last induction, and the pathological features of SIJ were taken also, further studying the pathological characteristics of sacroiliac region. Independent sample t-test and one-way ANOVA were used for statistical analysis. The prediction parameters and diagnostic efficiency were compared by ROC curve.

RESULTS

There was no significant difference of image parameters between the model and control groups at the 7th, 12th weeks after the last induction, and there were no positive findings in histopathological examination at the same time. At the 17th week after induction, the f and Fenh%, Senh% between the model and the control groups were statistically significant. At the 22th week, there was a statistically significant increase all the values in model group than those in control group (P < 0.05). Histologic examination confirmed inflmmtorycell infiitrtion at the 17th week and pannus forming of synovium on the surface of cartilage at the 22th week in the model groups. The Fenh%, Senh%, D_slow and f had the moderate diagnostic efficiency and the areas under the curve were 0.77, 0.75, 0.77 and 0.82 respectively. The Senh% demonstrated the highest sensitivity (71.4%) and f demonstrated the highest specificity (95.0%).

CONCLUSION

IVIM-DWI and DCE-MRI can be used as the sensitive imaging methods to detect and accurate diagnosis the early activity of sacroiliitis in AS.

Comparison of Monoexponential, Biexponential, Stretched-Exponential, and Kurtosis Models of Diffusion-Weighted Imaging in Differentiation of Renal Solid Masses

Objective

To compare various models of diffusion-weighted imaging including monoexponential apparent diffusion coefficient (ADC), biexponential (fast diffusion coefficient [D_f], slow diffusion coefficient [D_s], and fraction of fast diffusion), stretched-exponential (distributed diffusion coefficient and anomalous exponent term [α]), and kurtosis (mean diffusivity and mean kurtosis [MK]) models in the differentiation of renal solid masses.

Materials and Methods

A total of 81 patients (56 men and 25 women; mean age, 57 years; age range, 30–69 years) with 18 benign and 63 malignant lesions were imaged using 3T diffusion-weighted MRI. Diffusion model selection was investigated in each lesion using the Akaike information criteria. Mann-Whitney U test and receiver operating characteristic (ROC) analysis were used for statistical evaluations.

Results

Goodness-of-fit analysis showed that the stretched-exponential model had the highest voxel percentages in benign and malignant lesions (90.7% and 51.4%, respectively). ADC, D_s, and MK showed significant differences between benign and malignant lesions (p < 0.05) and between low- and high-grade clear cell renal cell carcinoma (ccRCC) (p < 0.05). α was significantly lower in the benign group than in the malignant group (p < 0.05). All diffusion measures showed significant differences between ccRCC and non-ccRCC (p < 0.05) except D_f and α (p = 0.143 and 0.112, respectively). α showed the highest diagnostic accuracy in differentiating benign and malignant lesions with an area under the ROC curve of 0.923, but none of the parameters from these advanced models revealed significantly better performance over ADC in discriminating subtypes or grades of renal cell carcinoma (RCC) (p > 0.05).

Conclusion

Compared with conventional diffusion parameters, α may provide additional information for differentiating benign and malignant renal masses, while ADC remains the most valuable parameter for differentiation of RCC subtypes and for ccRCC grading.

Apparent Diffusion Coefficient for Distinguishing Between Malignant and Benign Lesions in the Head and Neck Region: A Systematic Review and Meta-Analysis

Background: The purpose of the present meta-analysis was to provide evident data about use of apparent diffusion coefficient (ADC) values for distinguishing malignant and benign lesions in the head and neck region.

Material and Methods: MEDLINE and Scopus databases were screened for associations between ADC and malignancy/benignancy of head and neck lesions up to December 2018. Overall, 22 studies met the inclusion criteria. The following data were extracted: authors, year of publication, study design, number of patients/lesions, lesion type, mean value, and standard deviation of ADC. The primary endpoint of the systematic review was the analysis of the association between lesion nature and ADC values. The methodological quality of the involved studies was checked according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malignant lesions.

Results: The acquired 22 studies comprised 1,227 lesions. Different malignant lesions were diagnosed in 818 cases (66.7%) and benign lesions in 409 cases (33.3%). The mean ADC value of the malignant lesions was 1.04 × 10⁻³ mm²/s, and the mean value of the benign lesions was 1.46 × 10⁻³ mm²/s. Lymphomas and sarcomas showed the lowest calculated mean ADC values, 0.7 and 0.79 × 10⁻³ mm²/s, respectively. Adenoid cystic carcinomas had the highest ADC values (1.5 × 10⁻³ mm²/s). None of the analyzed malignant tumors had mean ADC values above 1.75 × 10⁻³ mm²/s.

Conclusion: ADC values play a limited role in distinguishing between malignant and benign lesions in the head and neck region. It may be only suggested that lesions with mean ADC values above 1.75 × 10⁻³ mm²/s are probably benign. Further large studies are needed for the analysis of the role of diffusion-weighted imaging (DWI)/ADC in the discrimination of benign and malignant lesions in the head and neck region.

Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study

The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.

The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies

BACKGROUND

To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML).

METHODS

The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI).

RESULTS

The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10^- 1 vs. 2.53 ± 0.12 × 10^- 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10^- 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10^- 1).

CONCLUSIONS

Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

Multiparameter diffusion-weighted imaging for characterizing pathological patterns in lupus nephritis patients: A preliminary study

BACKGROUND

Microstructural changes of lupus nephritis (LN) kidney such as inflammatory cell infiltration or fibrosis could influence water molecular movement or diffusion, which indicates that diffusion-weighted imaging (DWI) may become a valuable tool in evaluation of this disease.

PURPOSE

To explore whether multiparameter diffusion-weighted imaging (mDWI) could contribute to characterize pathological patterns in LN patients.

STUDY TYPE

Retrospective.

POPULATION

Twenty-two patients with LN.

FIELD STRENGTH/SEQUENCE

Multi-b value DWI was performed with a 3.0 T scanner.

ASSESSMENT

Apparent diffusion coefficient (ADC)_m , perfusion-related diffusion coefficient (D_f ), molecular diffusion coefficient (D_s ), perfusion fraction (f), ADC_s , α, ADC_k , and mean kurtosis (MK) were calculated by monoexponential, biexponential, stretched-exponential, and kurtosis models fits, respectively.

STATISTICAL TESTS

Independent sample t-test, Pearson analysis and receiver operating characteristic (ROC).

RESULTS

In the whole group, the activity index (AI) correlated significantly with alpha values in the medulla (rho = -0.54, P = 0.03). The chronicity index (CI) correlated significantly with D_s values in the medulla (rho = -0.61, P = 0.02). No significant association was found between any other diffusion parameter and histologic grade with all P > 0.05. For differentiating proliferative LN (Class III or IV) from Class V, the area under the ROC curve (AUC) of alpha in the medulla was 0.833 (P = 0.023). DATA CONCLUSION: mDWI might be used for the characterization of pathological patterns in LN patients.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1075-1084.

Diffusional kurtosis imaging in head and neck cancer: On the use of trace-weighted images to estimate indices of non-Gaussian water diffusion

PURPOSE

While previous studies have demonstrated the feasibility and potential usefulness of quantitative non-Gaussian diffusional kurtosis imaging (DKI) of the brain, more recent research has focused on oncological application of DKI in various body regions such as prostate, breast, and head and neck (HN). Given the need to minimize scan time during most routine magnetic resonance imaging (MRI) acquisitions of body regions, diffusion-weighted imaging (DWI) with only three orthogonal diffusion weighting directions (x, y, z) is usually performed. Moreover, as water diffusion within malignant tumors is generically thought to be almost isotropic, DWI with only three diffusion weighting directions is considered sufficient for oncological application and it represents the de facto standard in body DKI. In this context, since the kurtosis tensor and diffusion tensor cannot be obtained, the averages of the three directional (K_x , K_y , K_z ) and (D_x , D_y , D_z ) - namely K and D, respectively - represent the best-possible surrogates of directionless DKI-derived indices of kurtosis and diffusivity, respectively. This would require fitting the DKI model to the diffusion-weighted images acquired along each direction (x, y, z) prior to averaging. However, there is a growing tendency to perform only a single fit of the DKI model to the geometric means of the images acquired with diffusion-sensitizing gradient along (x, y, z), referred to as trace-weighted (TW) images. To the best of our knowledge, no in vivo studies have evaluated how TW images affect estimates of DKI-derived indices of K and D. Thus, the aim of this study was to assess the potential bias and error introduced in estimated K and D by fitting the DKI model to the TW images in HN cancer patients.

METHODS

Eighteen patients with histologically proven malignant tumors of the HN were enrolled in the study. They underwent pretreatment 3 T MRI, including DWI (b-values: 0, 500, 1000, 1500, 2000 s/mm² ). Some patients had multiple lesions, and thus a total of 34 lesions were analyzed. DKI-derived indices were estimated, voxel-by-voxel, using single diffusion-weighted images along (x, y, z) as well as TW images. A comparison between the two estimation methods was performed by calculating the percentage error in D (D_err ) and K (K_err ). Also, diffusivity anisotropy (D_anis ) and diffusional kurtosis anisotropy (K_anis ) were estimated. Agreements between the two estimation methods were assessed by Bland-Altman plots. The Spearman rank correlation test was used to study the correlations between K_err /D_err and D_anis /K_anis. RESULTS: The median (95% confidence interval) K_err and D_err were 5.1% (0.8%, 32.6%) and 1.7% (-2.5%, 5.3%), respectively. A significant relationship was observed between K_err  and D_anis (correlation coefficient R = 0.694, P < 0.0001), as well as between K_err and K_anis (R = 0.848, P < 0.0001).

CONCLUSIONS

In HN cancer, the fit of the DKI model to TW images can introduce bias and error in the estimation of K and D, which may be non-negligible for single lesions, and should hence be adopted with caution.

Characterization of salivary gland tumours with diffusion tensor imaging

OBJECTIVES

To characterize salivary glands tumours with diffusion tensor imaging.

METHODS

This study was conducted upon 53 patients (aged 18-81 years: mean 37 years) with salivary gland tumours that underwent diffusion tensor imaging was obtained using a single-shot echoplanar imaging sequence with parallel imaging at 1.5 T scanner. 48 slices were obtained, with a thickness of 2.5 mm, with no gap and the total scan duration was 7-8 min. The fractional anisotropy (FA) and the mean diffusivity (MD) value of the salivary gland tumours was calculated and correlated with pathological findings. Image analysis was performed by one radiologist. The receiver operating characteristic curve was drawn to detect the cut-off point of FA and MD used to characterize salivary gland tumours.

RESULTS

The mean FA and MD of malignant salivary gland tumours (n = 17) (0.41 ± 0.07 and 0.89 ± 0.15 × 10-3 mm2 s-1) was significantly different (p = 0.001) than that of benign tumours (n = 36) (0.19 ± 0.07 and 1.28 ± 0.42 × 10-3 mm2 s-1), respectively. Combined FA and MD used to differentiate malignant from benign tumours has an area under the curve (AUC) of 0.974, and an accuracy of 86%. There was a significant difference in FA between Warthin tumours and malignant tumours (p = 0.001). Selection FA of 0.35 to differentiate malignant tumours from Warthin tumours revealed AUC of 0.878 and an accuracy of 80%. There was a significant difference in FA and MD of malignant tumours and pleomorphic adenomas (p = 0.001). Combined FA and MD used to differentiate malignant tumours from pleomorphic adenomas revealed AUC of 0.993, and an accuracy of 93%. There was a significant difference in FA and MD of Warthin tumours and pleomorphic adenomas (p = 0.001). Combined FA and MD used to differentiate Warthin tumours from pleomorphic adenomas revealed AUC of 0.978, and an accuracy of 86%.

CONCLUSIONS

Diffusion-weighed imaging is a promising non-invasive method and it may be useful for the characterization and differentiation of benign and malignant salivary gland tumours.

Repeatability of the "flash-replenishment" method in contrast-enhanced ultrasound for the quantitative assessment of hepatic microvascular perfusion

This study aimed to evaluate the feasibility and repeatability of the flash-replenishment method in contrast-enhanced ultrasound (CEUS) perfusion imaging and assess quantitatively microvascular perfusion in the liver. Twenty healthy New Zealand rabbits were submitted to CEUS perfusion imaging with continuous intravenous infusion. Using flash-replenishment kinetics, the dynamic process of depletion and refilling of microbubble contrast agent was recorded. The hepatic microvascular perfusion parameters were calculated, including region of interest, peak intensity (PI), area under the curve (AUC), and hepatic artery to vein transit time (HA-HVTT). A consistency test was performed for multiple measurements by the same operator and blind measurements by two different operators. The hepatic perfusion imaging of 3×10⁸ bubbles/min had minimal error and the best imaging effect and repeatability. The variability of the perfusion parameter measured at 3 cm depth under the liver capsule was at a minimum with coefficient of variation of 3.9%. The interclass correlation coefficient (ICC) of measurements taken by the same operator was 0.985, (95% confidence interval, CI=0.927-0.998). Measurements taken by two operators had good consistency and reliability, with the ICC of 0.948 (95%CI=0.853-0.982). The PI and AUC of liver parenchyma after reperfusion were lower than before blocking; and HA-HVTT was significantly longer than before blocking (P<0.05). The flash-replenishment method in CEUS perfusion imaging showed good stability and repeatability, which provide a valuable experimental basis for the quantitative assessment of hepatic microvascular perfusion in clinical practice.

Quantitative Evaluation of Hepatic Microvascular Perfusion after Ischemia-Reperfusion Injury in Rabbits by Contrast-Enhanced Ultrasound Perfusion Imaging

The aim of this study was to evaluate microvascular perfusion after liver ischemia-reperfusion injury (IRI) in rabbits using the "flash-replenishment" method of contrast-enhanced ultrasound (CEUS) perfusion imaging. Twenty-eight rabbits underwent either 30, 60 or 90 min of ischemia and 120 min of reperfusion. CEUS perfusion imaging was performed using the "flash-replenishment" model, and hepatic microvascular perfusion parameters, including peak intensity (PI), area under the curve (AUC), and hepatic artery-to-vein transit time (HA-HVTT), were calculated. Prolonged ischemia upregulated intracellular adhesion molecule-1 (ICAM-1), alanine transaminase (ALT) and aspartate transaminase (AST) levels. Longer ischemia decreased PI and AUC, but increased HA-HVTT. The perfusion parameters were significantly correlated with Suzuki's pathology scores and ALT and AST levels. The "flash-replenishment" method of CEUS perfusion imaging is an accurate and non-invasive method for evaluating hepatic microvascular perfusion and provides a valuable experimental basis for early prediction of liver IRI damage after liver transplantation or liver resection.

Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients

We assessed parameters of advanced diffusion weighted imaging (DWI) models for the prediction of the tumor growth rate in 55 head and neck squamous cell carcinoma (HNSCC) patients. The DWI acquisition used single-shot spin-echo echo-planar imaging with 12 b-values (0−2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, tri-exponential, stretched exponential and diffusion kurtosis imaging models. We directly measured the tumor growth rate from two sets of different-date imaging data. We divided the patients into a discovery group (n = 40) and validation group (n = 15) based on their MR acquisition dates. In the discovery group, we performed univariate and multivariate regression analyses to establish the multiple regression equation for the prediction of the tumor growth rate using diffusion parameters. The equation obtained with the discovery group was applied to the validation group for the confirmation of the equation's accuracy. After the univariate and multivariate regression analyses in the discovery-group patients, the estimated tumor growth rate equation was established by using the significant parameters of intermediate diffusion coefficient D₂ and slow diffusion coefficient D₃ obtained by the tri-exponential model. The discovery group's correlation coefficient between the estimated and directly measured tumor growth rates was 0.74. In the validation group, the correlation coefficient (r = 0.66) and intra-class correlation coefficient (0.65) between the estimated and directly measured tumor growth rates were respectively good. In conclusion, advanced DWI model parameters can be a predictor for determining HNSCC patients’ tumor growth rate.

Utility of a Hybrid IVIM-DKI Model to Predict the Development of Distant Metastasis in Head and Neck Squamous Cell Carcinoma Patients

Purpose:

To evaluate the diagnostic power of hybrid intravoxel incoherent motion (IVIM)-diffusion kurtosis imaging (DKI) model parameters in pretreatment for the prediction of future distant metastasis in head and neck squamous cell carcinoma (HNSCC) patients.

Materials and Methods:

We retrospectively evaluated 49 HNSCC patients who underwent curative chemoradiation therapy. Diffusion-weighted image (DWI) acquired by single-shot spin-echo echo-planar imaging with 12 b-values (0–2000) was performed in all patients before any treatment. We calculated the IVIM-DKI parameters and the conventional apparent diffusion coefficient (ADC) in the ROI placed on the primary lesion. The presence of future distant metastasis was determined by histological findings or clinical follow-up.

Results:

A univariate analysis revealed significant differences between the patients with distant metastasis and those without in slow diffusion coefficient (D) and kurtosis value (K). Highest diagnostic accuracy was obtained by the D value. In addition, a multivariate analysis revealed that the D value was an independent predictor of future distant metastasis.

Conclusion:

The D and K values obtained by this hybrid IVIM-DKI model can be one of the diagnostic tools for the prediction of future distant metastasis in HNSCC patients.

A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI

BACKGROUND

Diffusion-weighted MRI (DWI) signal attenuation is often not mono-exponential (i.e. non-Gaussian diffusion) with stronger diffusion weighting. Several non-Gaussian diffusion models have been developed and may provide new information or higher sensitivity compared with the conventional apparent diffusion coefficient (ADC) method. However the relative merits of these models to detect tumor therapeutic response is not fully clear.

METHODS

Conventional ADC, and three widely-used non-Gaussian models, (bi-exponential, stretched exponential, and statistical model), were implemented and compared for assessing SW620 human colon cancer xenografts responding to barasertib, an agent known to induce apoptosis via polyploidy. Bayesian Information Criterion (BIC) was used for model selection among all three non-Gaussian models.

RESULTS

All of tumor volume, histology, conventional ADC, and three non-Gaussian DWI models could show significant differences between control and treatment groups after four days of treatment. However, only the non-Gaussian models detected significant changes after two days of treatment. For any treatment or control group, over 65.7% of tumor voxels indicate the bi-exponential model is strongly or very strongly preferred.

CONCLUSION

Non-Gaussian DWI model-derived biomarkers are capable of detecting tumor earlier chemotherapeutic response of tumors compared with conventional ADC and tumor volume. The bi-exponential model provides better fitting compared with statistical and stretched exponential models for the tumor and treatment models used in the current work.