Model selection for high b-value diffusion-weighted MRI of the prostate

Diffusion-weighted MR spectroscopy of the prostate

PURPOSE

Prostate tissue has a complex microstructure, mainly composed of epithelial and stromal cells, and of extracellular (acinar-luminal) spaces. Diffusion-weighted MR spectroscopy (DW-MRS) is ideally suited to explore complex microstructure in vivo with metabolites selectively distributed in different subspaces. To date, this technique has been applied to brain and muscle. This study presents the development and pioneering utilization of 1H-DW-MRS in the prostate, accompanied by in vitro studies to support interpretations of in vivo findings.

METHODS

Nine healthy volunteers underwent a prostate MR examination (mean age, 56 years; range, 31-66). Metabolic complexation was studied in vitro using solutions with major compounds found in prostatic fluid of the lumen. DW-MRS was performed at 3 T with a non-water-suppressed single-voxel sequence with metabolite-cycling to concurrently measure metabolite and water signals. The water signal was used in postprocessing as a reference in a motion-compensation scheme. The spectra were fitted simultaneously in the spectral and diffusion-weighting dimensions. Apparent diffusion coefficients (ADCs) were derived by fitting signal decays that were assumed to be mono-exponential for metabolites and biexponential for water.

RESULTS

DW-MRS of the prostate revealed relatively low ADCs for Cho and Cr compounds, aligning with their intracellular location and higher ADCs for citrate and spermine supporting their luminal origin. In vitro assessments of the ADCs of citrate and spermine demonstrated their complex formation and protein binding. Tissue concentrations of MRS-detectable metabolites were as expected for the voxel location.

CONCLUSIONS

This work successfully demonstrates the feasibility of 1H-DW-MRS of the prostate and its potential for providing valuable microstructural information.

Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer

Background and Objective

In recent years, magnetic resonance imaging (MRI) has shown excellent results in the study of the prostate gland. MRI has indeed shown to be advantageous in the prostate cancer (PCa) detection, as in guiding targeting biopsy, improving its diagnostic yield. Although current acquisition protocols provide for multiparametric acquisition, recent evidence has shown that biparametric protocols can be non-inferior in PCa detection. Diffusion-weighted imaging (DWI) sequence, in particular, plays a key role, particularly in the peripheral zone which accounts for the larger part of the prostate. High b-values are generally recommended, although with the possibility of obtaining non-Gaussian diffusion effects, which requires a more sophisticated model for the analysis, namely through the diffusion kurtosis imaging (DKI). Purpose of this narrative review was to analyze the current applications and clinical evidence regarding the use of DKI with a main focus on PCa detection, also in comparison with DWI.

Methods

This narrative review synthesized the findings of literature retrieved from main researches, narrative and systematic reviews, and meta-analyses obtained from PubMed.

Key Content and Findings

DKI analyses the non-Gaussian water diffusivity and describe the effect of signal intensity decay related to high b-value through two main metrics (Dapp and Kapp). Differently from DWI-apparent diffusion coefficient (DWI-ADC) which reflects only water restriction outside of cells, DKI metrics are supposed to represent also the direct interaction of water molecules with cell membranes and intracellular compounds. This review describes current evidence on ADC and DKI metrics in clinical imaging, and finally collect the results derived from the main articles focused on DWI and DKI models in detecting PCa.

Conclusions

DKI advantages, compared to conventional ADC analysis, still remain controversial. Wider application and greater technical knowledge of DKI, however, may help in proving its intrinsic validity in the field of oncology and therefore in the study of clinically significant PCa. Finally, a deep understanding of DKI is important for radiologists to better understand what Kapp and Dapp mean in the context of different cancer and how these metrics may vary specifically in PCa imaging.

Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions

OBJECTIVE

The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We aim to evaluate the diagnostic performance of stand-alone parameter or in combination with multiparameter derived from multiple b-value DWI in differentiating malignant from benign breast lesions.

METHODS

A total of forty-one patients diagnosed with benign breast tumor and thirty-eight patients with malignant breast tumor underwent DWI using thirteen b values and other MRI functional sequence at 3.0 T magnetic resonance. Data were accepted mono-exponential, bi-exponential, stretched-exponential, aquaporins (AQP) model analysis. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of quantitative parameter or multiparametric combination. The Youden index, sensitivity and specificity were used to assess the optimal diagnostic model. T-test, logistic regression analysis, and Z-test were used. P value < 0.05 was considered statistically significant.

RESULT

The ADC_avg, ADC_max, f, and α value of the malignant group were lower than the benign group, while the ADC_fast value was higher instead. The ADC_min, ADC_slow, DDC and ADC_AQP showed no statistical significance. The combination (ADC_avg-ADC_fast) yielded the largest area under curve (AUC = 0.807) with sensitivity (68.42%), specificity (87.8%) and highest Youden index, indicating that multiparametric combination (ADC_avg-ADC_fast) was validated to be a useful model in differentiating the benign from breast malignant lesion.

CONCLUSION

The current study based on the multiple b-value diffusion model demonstrated quantitatively multiparametric combination (ADC_avg-ADC_fast) exhibited the optimal diagnostic efficacy to differentiate malignant from benign breast lesions, suggesting that multiparameter would be a promising non-invasiveness to diagnose breast lesions.

Comparison of Diffusion Kurtosis Imaging and Standard Mono-Exponential Apparent Diffusion Coefficient in Diagnosis of Significant Prostate Cancer-A Correlation with Gleason Score Assessed on Whole-Mount Histopathology Specimens

BACKGROUND

The study was undertaken to compare the diagnostic performance of diffusion kurtosis imaging (DKI) with the standard monoexponential (ME) apparent diffusion coefficient (ADC) model in the detection of significant prostate cancer (PCa), using whole-mount histopathology of radical prostatectomy specimens as a reference standard.

METHODS

155 patients with prostate cancer had undergone multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomy. Quantitative diffusion parameters-the apparent diffusion coefficient corrected for non-Gaussian behavior (D_app), kurtosis (K), ADC₁₂₀₀, and ADC₂₀₀₀ were correlated with Gleason score and compared between cancerous and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors.

RESULTS

The mean values of all diffusion parameters (D_app, K, ADC₁₂₀₀, ADC₂₀₀₀) were significantly different both between malignant and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. Although the kurtosis model was better fitted to DWI data, the diagnostic performance in receiver operating characteristic (ROC) analysis of DKI and the standard ADC model in the detection of significant PCa was similar in the peripheral zone (PZ) and in peripheral and transitional zones (TZ) together. In conclusion, our study was not able to demonstrate a clear superiority of the kurtosis model over standard ADC in the diagnosis of significant PCa in PZ and in both zones combined.

Quantitative Imaging in Pediatric Hepatobiliary Disease

Pediatric hepatobiliary imaging is important for evaluation of not only congenital or structural disease but also metabolic or diffuse parenchymal disease and tumors. A variety of ultrasonography and magnetic resonance imaging (MRI) techniques can be used for these assessments. In ultrasonography, conventional ultrasound imaging as well as vascular imaging, elastography, and contrast-enhanced ultrasonography can be used, while in MRI, fat quantification, T2/T2^* mapping, diffusion-weighted imaging, magnetic resonance elastography, and dynamic contrast-enhanced MRI can be performed. These techniques may be helpful for evaluation of biliary atresia, hepatic fibrosis, nonalcoholic fatty liver disease, sinusoidal obstruction syndrome, and hepatic masses in children. In this review, we discuss each tool in the context of management of hepatobiliary disease in children, and cover various imaging techniques in the context of the relevant physics and their clinical applications for patient care.

Advances in Prostate Magnetic Resonance Imaging

Prostate magnetic resonance (MR) imaging is a widely used imaging technique to detect intraprostatic lesions and guide prostate biopsies, with continuous technical advances for better accuracy in prostate cancer diagnosis. Current evaluation of prostate multiparametric MR imaging mainly depends on qualitative evaluation, which is prone to inter-reader variation. Recent advances in prostate MR imaging, such as quantitative T2 mapping and abbreviated MR imaging protocols (eg, biparametric MR imaging), are designed to simplify prostate MR imaging acquisition and interpretation.

Histogram analysis from stretched exponential model on diffusion-weighted imaging: evaluation of clinically significant prostate cancer

OBJECTIVE

To evaluate the usefulness of histogram analysis of stretched exponential model (SEM) on diffusion-weighted imaging in evaluating clinically significant prostate cancer (CSC).

METHODS

A total of 85 patients with prostate cancer underwent 3 T multiparametric MRI, followed by radical prostatectomy. Histogram parameters of the tumor from the SEM [distributed diffusion coefficient (DDC) and α] and the monoexponential model [MEM; apparent diffusion coefficient (ADC)] were evaluated. The associations between parameters and Gleason score or Prostate Imaging Reporting and Data System v. 2 were evaluated. The area under the receiver operating characteristics curve was calculated to evaluate diagnostic performance of parameters in predicting CSC.

RESULTS

The values of histogram parameters of DDC and ADC were significantly lower in patients with CSC than in patients without CSC (p < 0.05), except for skewness and kurtosis. The value of the 25th percentile of α was significantly lower in patients with CSC than in patients without CSC (p = 0.014). Histogram parameters of ADC and DDC had significant weak to moderate negative associations with Gleason score or Prostate Imaging Reporting and Data System v. 2 (p < 0.001), except for skewness and kurtosis. For predicting CSC, the area under the curves of mean ADC (0.856), 50th percentile DDC (0.852), and 25th percentile α (0.707) yielded the highest values compared to other histogram parameters from each group.

CONCLUSION

Histogram analysis of the SEM on diffusion-weighted imaging may be a useful quantitative tool for evaluating CSC. However, the SEM did not outperform the MEM.

ADVANCES IN KNOWLEDGE

Histogram parameters of SEM may be useful for evaluating CSC.

Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I2 statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

Tumor detectability and conspicuity comparison of standard b1000 and ultrahigh b2000 diffusion-weighted imaging in rectal cancer

PURPOSE

To compare tumor detectability and conspicuity of standard b = 1000 s/mm² (b1000) versus ultrahigh b = 2000 s/mm² (b2000) diffusion-weighted imaging (DWI) in rectal cancer.

METHODS

Fifty-five patients for a total of 81 3T DWI-MR scans were retrospectively evaluated by two differently experienced readers. A comparison between b1000 and b2000 for tumor detectability and conspicuity was performed. The conspicuity was qualitatively and quantitatively assessed by using three-point scale and whole tumor volume manual delineation, respectively. Receiver-operating characteristic curve (ROC) with area under the curve (AUC) analysis provided diagnostic accuracy in tumor detectability of restaging MR scans. Qualitative scores and quantitative features including mean signal intensity, variance, 10th percentile and 90th percentile, were compared using the Wilcoxon test. Interobserver agreement (IOA) for qualitative and quantitative data was calculated using Cohen's Kappa and intraclass correlation coefficient (ICC) respectively.

RESULTS

Diagnostic accuracy was comparable between b1000 and b2000 for both readers (p > 0.05). Overall quality scores were significantly better for b2000 than b1000 (2.29 vs 1.65 Reader 1, p = 0.01; 2.18 vs 1.69 Reader 2, p = 0.04). IOA was equally good for both b values (k = 0.86 b1000, k = 0.86 b2000). Quantitative analysis revealed more uniform signal (measured in variance) of b2000 in both healthy surrounding tissue (p < 0.05) and tumor (p < 0.05), with less outliers (measured using 10th and 90th percentile). Additionally, b2000 offered lower mean signal intensity in tissue sorrounding the tumor (p < 0.05). Finally, ICC improved from 0.92 (b1000) to 0.97 (b2000).

CONCLUSION

Ultrahigh b value (b2000) may improve rectal cancer conspicuity and introbserver agreement maintaining comparable diagnostic accuracy to standard b1000.