Feasibility of diffusional kurtosis tensor imaging in prostate MRI for the assessment of prostate cancer: preliminary results

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.

Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.

The Histogram Analysis of Intravoxel Incoherent Motion-Kurtosis Model in the Diagnosis and Grading of Prostate Cancer-A Preliminary Study

Objectives

This study was conducted in order to explore the value of histogram analysis of the intravoxel incoherent motion-kurtosis (IVIM-kurtosis) model in the diagnosis and grading of prostate cancer (PCa), compared with monoexponential model (MEM).

Materials and Methods

Thirty patients were included in this study. Single-shot echo-planar imaging (SS-EPI) diffusion-weighted images (b-values of 0, 20, 50, 100, 200, 500, 1,000, 1,500, 2,000 s/mm²) were acquired. The pathologies were confirmed by in-bore MR-guided biopsy. The postprocessing and measurements were processed using the software tool Matlab R2015b for the IVIM-kurtosis model and MEM. Regions of interest (ROIs) were drawn manually. Mean values of D, D*, f, K, ADC, and their histogram parameters were acquired. The values of these parameters in PCa and benign prostatic hyperplasia (BPH)/prostatitis were compared. Receiver operating characteristic (ROC) curves were used to investigate the diagnostic efficiency. The Spearman test was used to evaluate the correlation of these parameters and Gleason scores (GS) of PCa.

Results

For the IVIM-kurtosis model, D (mean, 10th, 25th, 50th, 75th, 90th), D* (90th), and f (10th) were significantly lower in PCa than in BPH/prostatitis, while D (skewness), D* (kurtosis), and K (mean, 75th, 90th) were significantly higher in PCa than in BPH/prostatitis. For MEM, ADC (mean, 10th, 25th, 50th, 75th, 90th) was significantly lower in PCa than in BPH/prostatitis. The area under the ROC curve (AUC) of the IVIM-kurtosis model was higher than MEM, without significant differences (z = 1.761, P = 0.0783). D (mean, 50th, 75th, 90th), D* (mean, 10th, 25th, 50th, 75th), and f (skewness, kurtosis) correlated negatively with GS, while D (kurtosis), D* (skewness, kurtosis), f (mean, 75th, 90th), and K (mean, 75th, 90th) correlated positively with GS. The histogram parameters of ADC did not show correlations with GS.

Conclusion

The IVIM-kurtosis model has potential value in the differential diagnosis of PCa and BPH/prostatitis. IVIM-kurtosis histogram analysis may provide more information in the grading of PCa than MEM.

Integration of quantitative diffusion kurtosis imaging and prostate specific antigen in differential diagnostic of prostate cancer

This study aimed to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) and prostate-specific antigen (PSA) biomarkers in differentiating prostate cancer (PCa) and benign prostatic hyperplasia (BPH).A total of 43 cases of prostate diseases verified by pathology were enrolled in the present study. These cases were assigned to the BPH group (n = 20, 68.85±10.81 years old) and PCa group (n = 23, 74.13 ± 7.37 years old). All patients underwent routine prostate magnetic resonance imaging and DKI examinations, and the mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA) values were calculated. Three serum indicators (PSA, free PSA [fPSA], and f/t PSA) were collected. We used univariate logistic regression to analyze the above quantitative parameters between the 2 groups, and the independent factors were further incorporated into the multivariate logistic regression model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic efficacy of the single indicator and combined model.The difference in PSA, f/t PSA, MK, and FA between PCa and BPH was statistically significant (P < .05). The AUC for the combined model (f/t PSA, MK, and FA) of 0.972 (95% confidence interval [CI]: 0.928, 1.000) was higher than the AUC of 0.902 (95% CI: 0.801, 1.000) for f/t PSA, 0.833 (95% CI: 0.707, 0.958) for MK, and 0.807 (95% CI: 0.679, 0.934) for FA.The MK and FA values for DKI and f/t PSA effectively identify PCa and BPH, compared to the PSA indicators. Combining DKI and PSA derivatives can further improve the diagnosis efficiency and might help in the clinical setting.

Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas

PURPOSE

EphA2 is a key factor underlying invasive propensity of gliomas, and is associated with poor prognosis of tumors. We aimed to develop a radiomics-based imaging index for predicting EphA2 expression in diffuse gliomas, and further estimating its value for grading of tumors.

METHODS

A total of 182 patients with diffuse gliomas were included. All subjects underwent pre-operative MRI and post-operative pathological diagnosis. EphA2 expression of tumors was scored on pathological sections with immunohistochemical staining using monoclonal EphA2 antibody. MRI radiomics features were extracted from three-dimensional contrast-enhanced T1-weighted imaging and diffusion kurtosis imaging. Predictive models were constructed using machine learning-based radiomics features selection and three classifiers for predicting EphA2 expression and tumor grade. Features of best EphA2 expression model were subsequently used to construct another model of tumor grading. For each model, 146 cases (80%) were randomly picked as training and the rest 36 (20%) were testing cohorts. EphA2 expression was further correlated to the radiomics features in both grade models using Spearman's correlation.

RESULTS

Logistic regression model presented highest performance for predicting EphA2 expression (AUC: 0.836/0.724 in training/validation set). Tumor gradings model guided by features from EphA2 expression model demonstrated comparable performance (AUC: 0.930/0.983) to that constructed directly using imaging radiomics features (AUC: 0.960/0.977). Two radiomics features which included in both LR-grade models showed strong correlation (P < 0.05) with EphA2 expression.

CONCLUSION

The expression of EphA2 in gliomas could be predicted by radiomics features extracted from diffusion kurtosis MRI, which could also be used to assist tumor grading.

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.

Diffusion kurtosis imaging for the assessment of renal fibrosis of chronic kidney disease: A preliminary study

PURPOSE

To investigate the potential of diffusion kurtosis imaging (DKI) for the assessment of renal fibrosis in chronic kidney disease (CKD), using histopathology as the reference standard.

METHODS

Eighty-nine CKD patients and twenty healthy volunteers were recruited in this study. DKI was performed in all participants and all CKD patients received renal biopsy. The values of mean diffusivity (MD) and mean kurtosis (MK) in the renal cortex and medulla were compared between CKD patients and healthy volunteers. The Spearman correlation coefficient was calculated to assess the relationship between MD, MK values and the estimated glomerular filtration rate (eGFR), serum creatinine (SCr), 24 h urinary protein (24 h-UPRO), histopathological fibrosis score.

RESULTS

The medullary MD values were significantly lower than cortex, while the cortical MK values were significantly lower than medulla for all participants. Renal parenchymal MD values were significantly lower in the CKD patients than healthy controls, whereas MK values were significantly higher in the CKD patients than healthy controls. In the CKD patients, the significantly negative correlation was observed between the renal parenchymal MD values and the 24 h-UPRO, SCr, histopathological fibrosis score, as well as between the renal parenchymal MK values and the eGFR, while the significantly positive correlation was found between the renal parenchymal MD values and the eGFR, as well as between the renal parenchymal MK values and the 24 h-UPRO, SCr, histopathological fibrosis score.

CONCLUSION

DKI shows great potential in the noninvasive assessment of renal fibrosis in CKD.

Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer

Objectives

This study aims to evaluate and compare the diagnostic value of DKI and APT in prostate cancer (PCa), and their correlation with Gleason Score (GS).

Materials and Methods

DKI and APT imaging of 49 patients with PCa and 51 patients with benign prostatic hyperplasia (BPH) were collected and analyzed, respectively. According to the GS, the patients with PCa were divided into high-risk, intermediate-risk and low-risk groups. The mean kurtosis (MK), mean diffusion (MD) and magnetization transfer ratio asymmetry (MTRasym, 3.5 ppm) values among PCa, BPH, and different GS groups of PCa were compared and analyzed respectively. The diagnostic accuracy of each parameter was evaluated by using the receiver operating characteristic (ROC) curve. The correlation between each parameter and GS was analyzed by using Spearman’s rank correlation.

Results

The MK and MTRasym (3.5 ppm) values were significantly higher in PCa group than in BPH group, while the MD value was significantly lower than in BPH group. The differences of MK/MD/MTRasym (3.5 ppm) between any two of the low-risk, intermediate-risk, and high-risk groups were all statistically significant (p <0.05). The MK value showed the highest diagnostic accuracy in differentiating PCa and BPH, BPH and low-risk, low-risk and intermediate-risk, intermediate-risk and high-risk (AUC = 0.965, 0.882, 0.839, 0.836). The MK/MD/MTRasym (3.ppm) values showed good and moderate correlation with GS (r = 0.844, −0.811, 0.640, p <0.05), respectively.

Conclusion

DKI and APT imaging are valuable in the diagnosis of PCa and demonstrate strong correlation with GS, which has great significance in the risk assessment of PCa.

Application of diffusion kurtosis tensor MR imaging in characterization of renal cell carcinomas with different pathological types and grades

Background

To probe the feasibility and reproducibility of diffusion kurtosis tensor imaging (DKTI) in renal cell carcinoma (RCC) and to apply DKTI in distinguishing the subtypes of RCC and the grades of clear cell RCC (CCRCC).

Methods

Thirty-eight patients with pathologically confirmed RCCs [CCRCC for 30 tumors, papillary RCC (PRCC) for 5 tumors and chromophobic RCC (CRCC) for 3 tumors] were involved in the study. Diffusion kurtosis tensor MR imaging were performed with 3 b-values (0, 500, 1000s/mm²) and 30 diffusion directions. The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr) values and mean diffusity (MD) for RCC and contralateral normal parenchyma were acquired. The inter-observer agreements of all DKTI metrics of contralateral renal cortex and medulla were evaluated using Bland-Altman plots. Statistical comparisons with DKTI metrics of 3 RCC subtypes and between low-grade (Furman grade I ~ II, 22 cases) and high-grade (Furman grade III ~ IV, 8 cases) CCRCC were performed with ANOVA test and Student t test separately. Receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic efficacy of DKTI metrics for predicting nuclear grades of CCRCC. Correlations between DKTI metrics and nuclear grades were also evaluated with Spearman correlation analysis.

Results

Inter-observer measurements for each metric showed great reproducibility with excellent ICCs ranging from 0.81 to 0.87. There were significant differences between the DKTI metrics of RCCs and contralateral renal parenchyma, also among the subtypes of RCC. MK and Ka values of CRCC were significantly higher than those of CCRCC and PRCC. Statistical difference of the MK, Ka, Kr and MD values were also obtained between CCRCC with high- and low-grades. MK values were more effective for distinguishing between low- and high- grade CCRCC (area under the ROC curve: 0.949). A threshold value of 0.851 permitted distinction with high sensitivity (90.9%) and specificity (87.5%).

Conclusion

Our preliminary results suggest a possible role of DKTI in differentiating CRCC from CCRCC and PRCC. MK, the principle DKTI metric might be a surrogate biomarker to predict nuclear grades of CCRCC.

Trial registration

ChiCTC, ChiCTR-DOD-17010833, Registered 10 March, 2017, retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=17559.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models

BACKGROUND

Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.

PURPOSE

To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.

STATISTICAL TESTS

Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.

RESULTS

The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm² /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order.

DATA CONCLUSION

The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

BACKGROUND

There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present.

PURPOSE

To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM).

MATERIAL AND METHODS

Twenty-three patients were enrolled in this study. For DKI model and MEM, the D_app, K_app, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman's test was used to evaluate the correlation of parameters and Gleason scores.

RESULTS

The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and D_app were significantly lower in prostate cancer than non-cancerous foci (P < 0.001). The mean, 50th, 75th, and 90th percentiles of K_app were significantly higher in prostate cancer (P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased (ρ = -0.583, P = 0.018), but the 90th K_app increased (ρ = 0.642, P = 0.007).

CONCLUSION

Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.

New Advances in Magnetic Resonance Techniques in Abdomen and Pelvis

This article explores new acquisition methods in magnetic resonance (MR) imaging to provide high spatial and temporal resolution imaging for a wide spectrum of clinical applications in the abdomen and pelvis. We present an overview of some of these advanced MR techniques, such as non-cartesian image acquisition, fast sampling and compressed sensing, diffusion quantification and quantitative MR that can improve data sampling, enhance image quality, yield quantitative measurements, and/or optimize diagnostic performance in the body.

Diffusional kurtosis imaging of kidneys in patients with hyperuricemia: initial study

BACKGROUND

At present, there remains a lack of a reliable indicator for monitoring renal function in patients with hyperuricemia.

PURPOSE

This study aimed to evaluate the feasibility of diffusion kurtosis imaging in the assessment of renal function in patients with hyperuricemia.

MATERIAL AND METHODS

A total of 75 male participants, including 25 with asymptomatic hyperuricemia, 25 with gouty arthritis, and 25 age-matched male healthy controls, were enrolled in this study. Diffusion kurtosis imaging data were acquired to derive axial (Ka), radial (Kr), and mean kurtosis (MK), fractional anisotropy, axial (Da), radial (Dr), and mean diffusivity (MD) for comparisons among the three groups. They were also correlated with estimated glomerular filtration rate (eGFR).

RESULTS

The MK values of the renal cortex and medulla and Kr value of the renal medulla in patients with asymptomatic hyperuricemia and gouty arthritis significantly increased compared with those in the controls (P < 0.05). Patients with gouty arthritis showed significant higher cortical and medullary Ka values compared with the other two groups (P < 0.05). The cortical Kr values of the asymptomatic hyperuricemia and gouty arthritis patients were significantly higher than that of the controls (P < 0.05). The medullary fractional anisotropy value showed a significant difference between the control and gouty arthritis groups (P < 0.05). No correlation was found between any diffusion kurtosis imaging parameters and eGFR value.

CONCLUSION

Diffusion kurtosis imaging is feasible in the assessment of the early changes of renal cortex and medulla in patients with hyperuricemia.

Diffusion Kurtosis Imaging in the Assessment of Cervical Carcinoma

RATIONALE AND OBJECTIVES

To evaluate the additional value of diffusion kurtosis imaging (DKI) in the characterization of cervical carcinoma.

MATERIALS AND METHODS

Seventy-five patients (56.9 ± 13.4 years) with histologic-confirmed cervical carcinoma were included. Diffusion-weighted imaging (DWI) was acquired on a 3T MRI with five b values (0, 500, 800, 1000, and 1500 s/mm²). Data were analyzed based on DKI model (5 b values) and conventional DWI (0 and 1000 s/mm²). Largest single-slice region of interest (ROI) and volume of interest (VOI) were drawn around the tumor. Mean diffusivity (MD), mean kurtosis (MK), and apparent diffusion coefficient (ADC) of cervical carcinoma and normal myometrium were measured and compared. MD, MK, and ADC of cervical carcinoma were compared among histologic subtypes, tumor grades, and FIGO stages.

RESULTS

ROI- and VOI-derived DKI parameters and ADC were all in excellent consistency (intraclass correlation coefficient, ICC > 0.90, respectively). Cervical carcinoma had significantly lower MD, ADC, and higher MK than normal myometrium (p < 0.001). MD and ADC showed significant differences between histologic subtypes and FIGO stages, lower in squamous cell carcinoma than adenocarcinoma and higher in FIGO I-II than FIGO III-IV (p < 0.050), but not with tumor grade. No difference was observed in MK for different clinicopathologic features tested.

CONCLUSION

ROI and VOI analyses were in excellent consistency. MD and ADC were able to distinguish histologic subtypes and separating FIGO stages, MK could not. DKI showed no clear added value over conventional DWI in the characterization of cervical carcinoma.

Characterization of breast lesions using diffusion kurtosis model-based imaging: An initial experience

OBJECTIVE

To investigate the characterization of breast lesions using diffusion kurtosis model-based imaging.

METHODS

This prospective study included 120 consecutive patients underwent preoperative DCE-MRI examinations and multi-b-value diffusion-weighted imaging (DWI). Among them, 88 malignant lesions and 44 benign lesions were detected, 56 normal fibroglandular breast tissue were selected as normal control. Conventional apparent diffusion coefficient (ADC), DKI-based parameters mean kurtosis (MK) and mean diffusivity (MD) were analyzed by lesions types and histological subtypes using one-way ANOVA and receiver operating characteristic (ROC) curve.

RESULTS

(1) The malignant group showed significantly lower ADC and MD (1.07±0.32×10-3 mm2/s and 1.30±0.40×10-3 mm2/s, respectively) and higher MK (0.87±0.18) than those in the benign group (1.29±0.26×10-3 mm2/s, 1.62±0.31×10-3 mm2/s and 0.67±0.18) and control group (1.67±0.33×10-3 mm2/s, 2.24±0.28×10-3 mm2/s and 0.52±0.08) with all P < 0.001. (2) Areas under ROC curve (AUC) for diagnosing malignant lesions were 0.936 for MD, 0.911 for MK and 0.897 for ADC, respectively. AUC for MD was significantly higher than that for ADC (P = 0.015). The optimal cut-off value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were as follow: ADC = 1.18×10-3mm2/s, 78.3%, 93.2%, 81.2%, 81.6%, 81.4%; MD = 1.48×10-3mm2/s, 82.2%, 98.3%, 84.4%, 87.8%, 86.2%; MK = 0.78, 91.5%, 85.3%, 89.0%, 85.8%, 87.2%. (3) Invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and mucinous adenocarcinoma also showed significant differences among ADC, MD and MK (with P < 0.05).

CONCLUSIONS

MR-DKI parameters enable to improve breast lesion characterization and have diagnostic potential applying to different pathological subtypes of breast cancers.

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer

PURPOSE

To evaluate repeatability of prostate DWI-derived radiomics and machine learning methods for prostate cancer (PCa) characterization.

METHODS

A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b-values (0-2000 s/mm² ), post-processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman's ρ, Wilcoxon rank-sum) were evaluated in terms of Scan1-Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus >1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split.

RESULTS

The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28-0.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61-81%. Surface-to-volume ratio and corner detector-based features were among the most represented features with high repeatability, ICC(3,1) >0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve >0.70.

CONCLUSION

Surface-to-volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features.

Prostate cancer aggressive prediction: preponderant diagnostic performances of intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) beyond ADC at 3.0 T scanner with gleason score at final pathology

PURPOSE

To explore the preponderant diagnostic performances of IVIM and DKI in predicting the Gleason score (GS) of prostate cancer.

METHODS

Diffusion-weighted imaging data were postprocessed using monoexponential, lVIM and DK models to quantitate the apparent diffusion coefficient (ADC), molecular diffusion coefficient (D), perfusion-related diffusion coefficient (Dstar), perfusion fraction (F), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Spearman's rank correlation coefficient was used to explore the relationship between those parameters and the GS, Kruskal-Wallis test, and Mann-Whitney U test were performed to compare the above parameters between the different groups, and a receiver-operating characteristic (ROC) curve was used to analyze the differential diagnosis ability. The interpretation of the results is in view of histopathologic tumor tissue composition.

RESULTS

The area under the ROC curves (AUCs) of ADC, F, D, Dapp, and Kapp in differentiating GS ≤ 3 + 4 and GS > 3 + 4 PCa were 0.744 (95% CI 0.581-0.868), 0.726 (95% CI 0.563-0.855), 0.732 (95% CI 0.569-0.860), and 0.752 (95% CI 0.590-0.875), 0.766 (95% CI 0.606-0.885), respectively, and those in differentiating GS ≤ 7 and GS > 7 PCa were 0.755 (95% CI 0.594-0.877), 0.734 (95% CI 0.571-0.861), 0.724 (95% CI0.560-0.853), and 0.716 (95% CI 0.552-0.847), 0.828 (95% CI 0.676-0.929), respectively. All the P values were less than 0.05. There was no significant difference in the AUC for the detection of different GS groups by using those parameters.

CONCLUSION

Both the IVIM and DKI models are beneficial to predict GS of PCa and indirectly predict its aggressiveness, and they have a comparable diagnostic performance with each other as well as ADC.

Correlations between Apparent Diffusion Coefficient and Gleason Score in Prostate Cancer: A Systematic Review

BACKGROUND

Reported data regarding the associations between apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) and Gleason score in prostate cancer (PC) are inconsistent.

OBJECTIVE

The aim of the present systematic review was to analyze relationships between ADC and Gleason score in PC.

DESIGN, SETTING, AND PARTICIPANTS

MEDLINE library, SCOPUS, and EMBASE databases were screened for relationships between ADC and Gleason score in PC up to April 2018. Overall, 39 studies with 2457 patients were identified. Data on the following parameters were extracted from the literature: number of patients, cancer localization, and correlation coefficients between ADC and Gleason score.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations between ADC and Gleason score were analyzed by the Spearman's correlation coefficient.

RESULTS AND LIMITATIONS

In overall sample, the pooled correlation coefficient between ADC and Gleason score was -0.45 (95% confidence interval [CI]=[-0.50; -0.40]). In PC in the transitional zone, the pooled correlation coefficient was -0.22 (95% CI=[-0.47; 0.03]). In PC in the peripheral zone, the pooled correlation coefficient was -0.48 (95% CI=[-0.54; -0.42]).

CONCLUSIONS

In PC located in the peripheral zone, ADC correlated moderately with Gleason score. In PC located in the transitional zone, ADC correlated weakly with Gleason score.

PATIENT SUMMARY

We reviewed studies using apparent diffusion coefficient for the prediction of Gleason score in prostate cancer patients.

Diffusion kurtosis imaging to assess correlations with clinicopathologic factors for bladder cancer: a comparison between the multi-b value method and the tensor method

OBJECTIVES

To assess the efficacy of diffusion kurtosis imaging (DKI) in differentiating low-grade from high-grade tumors and evaluating the aggressiveness of bladder cancer.

METHODS

From January 2017 to July 2017, 35 patients (28 males, 7 females; mean age 63 ± 9 years) diagnosed with bladder cancer underwent diffusion-weighted imaging (DWI) with two types of DKI protocols: (1) multi-b value ranging from 0 to 2000 s/mm² to obtain mean diffusivity/kurtosis (MD_b/MK_b) and (2) the tensor method with 32 directions with 3 b values (0, 1000, and 2000s/mm²) to obtain mean/axial/radial diffusivity (MD_t/Da/Dr), mean/axial/radial kurtosis (MK_t/Ka/Kr), and fractional anisotropy (FA) before radical cystectomy. Comparisons between the low- and high-grade groups, non-muscle-invasive bladder cancer (NMIBC), and muscle-invasive bladder cancer (MIBC) were performed with the areas under the receiver operating characteristic curves (AUCs).

RESULTS

The MK_t and Kr values were significantly (p = 0.017 and p = 0.048) higher in patients with high-grade bladder tumors than in those with low-grade tumors. The MK_t, Kr, and MK_b values were significantly (p = 0.022, p = 0.000, and p = 0.044, respectively) higher in patients with MIBC than in those with NMIBC, while no significant differences (p > 0.05) were found in other values. The AUC of Kr (0.883) was the largest and was significantly higher than those of other metrics (all p < 0.05) for differentiating MIBC from NMIBC, with a sensitivity and specificity of 81.8% and 91.7%, respectively.

CONCLUSIONS

Kurtosis metrics performed better than diffusion metrics in differentiating MIBC from NMIBC, and directional kurtosis and Kr metrics may also have great potential in providing additional information regarding bladder cancer invasiveness.

KEY POINTS

• Kurtosis metrics performed better than diffusion metrics in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). • Directional kurtosis can provide additional directional microstructural information regarding bladder cancer invasiveness.

A comparative study of Gaussian and non-Gaussian diffusion models for differential diagnosis of prostate cancer with in-bore transrectal MR-guided biopsy as a pathological reference

Background Although several studies have been reported on evaluating the performance of Gaussian and different non-Gaussian diffusion models on prostate cancer, few studies have been reported on the comparison of different models on differential diagnosis for prostate cancer. Purpose To compare the utility of various metrics derived from monoexponential model (MEM), biexponential model (BEM), stretched-exponential model (SEM) based diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in the differential diagnosis of prostate cancer. Material and Methods Thirty-three patients underwent magnetic resonance imaging (MRI) examination. Multi-b value and multi-direction DWIs were performed. In-bore MR-guided biopsy was performed. Apparent diffusion coefficient (ADC), pure molecular diffusion (ADC_slow), pseudo-diffusion coefficient (ADC_fast), perfusion fraction (f), water molecular diffusion heterogeneity index (α), distributed diffusion coefficient (DDC), non-Gaussian diffusion coefficient (MD), and mean kurtosis (MK) values were calculated and compared between cancerous and non-cancerous groups. Receiver operating characteristic (ROC) analysis was performed for all parameters and models. Results ADC, ADC_slow, DDC, and MD values were significantly lower while MK value was significantly higher in prostate cancer than those of prostatitis and benign prostatic hyperplasia. ADC, ADC_slow, DDC, MD, and MK could discriminate between tumor and non-tumorous lesions (area under the curve, 0.856, 0.835, 0.866, 0.918, and 0.937, respectively). MK was superior to ADC in the discrimination of prostate cancer. DKI was superior to MEM in the discrimination of prostate cancer. Conclusions Parameters derived from both Gaussian and non-Gaussian models could characterize prostate cancer. DKI may be advantageous than DWI for detection of prostate cancer.

Imaging biomarkers in oncology: Basics and application to MRI

Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called "precision cancer medicine." Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI).

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2018;48:13-26.

Design and characterization of tissue-mimicking gel phantoms for diffusion kurtosis imaging

PURPOSE

The aim of this work was to create tissue-mimicking gel phantoms appropriate for diffusion kurtosis imaging (DKI) for quality assurance, protocol optimization, and sequence development.

METHODS

A range of agar, agarose, and polyvinyl alcohol phantoms with concentrations ranging from 1.0% to 3.5%, 0.5% to 3.0%, and 10% to 20%, respectively, and up to 3 g of glass microspheres per 100 ml were created. Diffusion coefficients, excess kurtosis values, and relaxation rates were experimentally determined.

RESULTS

The kurtosis values for the plain gels ranged from 0.05 with 95% confidence interval (CI) of (0.029,0.071) to 0.216(0.185,0.246), well below the kurtosis values reported in the literature for various tissues. The addition of glass microspheres increased the kurtosis of the gels with values up to 0.523(0.465,0.581) observed for gels with the highest concentration of microspheres. Repeat scans of some of the gels after more than 6 months of storage at room temperature indicate changes in the diffusion parameters of less than 10%. The addition of the glass microspheres reduces the apparent diffusion coefficients (ADCs) and increases the longitudinal and transverse relaxation rates, but the values remain comparable to those for plain gels and tissue, with ADCs observed ranging from 818(585,1053) × 10^-6  mm² /s to 2257(2118,2296) × 10^-6  mm² /s, R₁ values ranging from 0.34(0.32,0.35) 1/s to 0.51(0.50,0.52) 1/s, and R₂ values ranging from 9.69(9.34,10.04) 1/s to 33.07(27.10, 39.04) 1/s.

CONCLUSIONS

Glass microspheres can be used to effectively modify diffusion properties of gel phantoms and achieve a range of kurtosis values comparable to those reported for a variety of tissues.

Detection of high GS risk group prostate tumors by diffusion tensor imaging and logistic regression modelling

PURPOSE

To assess the value of joint evaluation of diffusion tensor imaging (DTI) measures by using logistic regression modelling to detect high GS risk group prostate tumors.

MATERIALS AND METHODS

Fifty tumors imaged using DTI on a 3 T MRI device were analyzed. Regions of interests focusing on the center of tumor foci and noncancerous tissue on the maps of mean diffusivity (MD) and fractional anisotropy (FA) were used to extract the minimum, the maximum and the mean measures. Measure ratio was computed by dividing tumor measure by noncancerous tissue measure. Logistic regression models were fitted for all possible pair combinations of the measures using 5-fold cross validation.

RESULTS

Systematic differences are present for all MD measures and also for all FA measures in distinguishing the high risk tumors [GS ≥ 7(4 + 3)] from the low risk tumors [GS ≤ 7(3 + 4)] (P < 0.05). Smaller value for MD measures and larger value for FA measures indicate the high risk. The models enrolling the measures achieve good fits and good classification performances (R²_adj = 0.55-0.60, AUC = 0.88-0.91), however the models using the measure ratios perform better (R²_adj = 0.59-0.75, AUC = 0.88-0.95). The model that employs the ratios of minimum MD and maximum FA accomplishes the highest sensitivity, specificity and accuracy (Se = 77.8%, Sp = 96.9% and Acc = 90.0%).

CONCLUSION

Joint evaluation of MD and FA diffusion tensor imaging measures is valuable to detect high GS risk group peripheral zone prostate tumors. However, use of the ratios of the measures improves the accuracy of the detections substantially. Logistic regression modelling provides a favorable solution for the joint evaluations easily adoptable in clinical practice.

Diffusion kurtosis imaging in identifying the malignancy of lymph nodes during the primary staging of rectal cancer

AIM

The aim was to assess the diagnostic value of diffusion kurtosis imaging (DKI) for discriminating between benign and malignant lymph nodes in patients with rectal carcinoma.

METHOD

ighty-five patients with rectal adenocarcinoma who underwent total mesorectal excision of the rectum were studied. A total of 273 lymph nodes were harvested and subjected to histological analysis. Quantitative parameters [apparent diffusion parameter D_app of the Gaussian distribution, apparent kurtosis coefficient K_app and apparent diffusion coefficient (ADC)] of lymph nodes were derived from DKI. Differences and the diagnostic performance of these parameters were calculated by using the independent-samples t test and receiver operating characteristic curve analyses.

RESULTS

The median D_app and ADC values of metastatic lymph nodes were significantly greater than those of benign lymph nodes, whereas the median K_app of metastatic lymph nodes was statistically less than that of normal lymph nodes. D_app had the relatively highest area under the curve of 0.774. When 1126.15 × 10^-6  mm² /s was used as a D_app threshold value, the sensitivity and specificity were 96.97% and 41.82%, respectively.

CONCLUSION

DKI can help differentiate metastatic vs benign lymph nodes during the primary staging of rectal cancer.

Application of non-Gaussian water diffusional kurtosis imaging in the assessment of uterine tumors: A preliminary study

OBJECTIVES

To evaluate the interobserver reliability and value of diffusional kurtosis imaging (DKI) in the assessment of uterine tumors compared with those of conventional diffusion-weighted imaging (DWI).

METHODS

This retrospective study was approved by our institutional review board, which waived the requirement for informed consent. Fifty-eight women (mean age: 55.0 ± 13.6 years; range: 30-89 years) with suspected malignant uterine tumors underwent 3-T magnetic resonance imaging using DKI and DWI. Twelve had coexisting leiomyoma. Two observers analyzed region-of-interest measurements of diffusivity (D), kurtosis (K), and the apparent diffusion coefficient (ADC) of uterine lesions and healthy adjacent tissues. Interobserver agreement was evaluated using the intra-class correlation coefficient (ICC). The mean values were compared using one-way analysis of variance with a post-hoc Tukey's honestly significant difference test. The diagnostic accuracy of D and ADC in differentiating malignant tumors from benign leiomyomas was analyzed using receiver operating characteristic (ROC) analysis.

RESULTS

The ICCs between the two observers in evaluating D, K, and the ADC of the malignant tumors were higher than 0.84, suggesting excellent interobserver agreements. The mean D (×10-3 mm2/s) of uterine cancers (1.05 ± 0.41 and 1.09 ± 0.40 for observers 1 and 2, respectively) were significantly lower than those of leiomyoma (1.40 ± 0.37 and 1.56 ± 0.33, respectively; P < 0.05), healthy myometrium (1.72 ± 0.27 and 1.69 ± 0.30, respectively; P < 0.001), and healthy endometrium (1.53 ± 0.35 and 1.42 ± 0.37, respectively; P < 0.005). There was no significant difference in the area under the ROC curve between D and ADC. The mean K of uterine cancers (0.88 ± 0.28 and 0.90 ± 0.23, respectively) were higher than those of myometrium (0.72 ± 0.10 and 0.73 ± 0.10, respectively; P < 0.001), healthy endometrium (0.65 ± 0.13 and 0.60 ± 0.18, respectively; P < 0.001), and leiomyoma (0.76 ± 0.14 and 0.77 ± 0.16, respectively; not significant, P > 0.1).

CONCLUSIONS

Interobserver agreements in evaluating D, K, and ADC were moderate to excellent. D performed equally to conventional DWI in differentiating between benign and malignant uterine lesions. The mean K of malignant uterine lesions was significantly higher than that of non-tumorous myometrium or endometrium.

Evaluation of Peripheral Zone Prostate Cancer Aggressiveness Using the Ratio of Diffusion Tensor Imaging Measures

Purpose

To evaluate the aggressiveness of peripheral zone prostate cancer by correlating the Gleason score (GS) with the ratio of the diffusion tensor imaging (DTI) measures.

Materials and Methods

Forty-two peripheral zone prostate tumors were imaged using DTI. Regions of interest focusing on the center of tumor foci and noncancerous tissue were used to extract statistical measures of mean diffusivity (MD) and fractional anisotroy (FA). Measure ratio was calculated by dividing tumor measure by noncancerous tissue measure.

Results

Strong correlations are observable between GS and MD measures while weak correlations are present between GS and FA measures. Minimum tumor MD (MD_min) and the ratio of minimum MD (rMD_min) show the same highest correlation with GS (both ρ = −0.73). Between GS ≤ 7 (3 + 4) and GS ≥ 7 (4 + 3), differences are significant for all MD measures but for some FA measures. MD measures perform better than FA measures in discriminating GS ≥ 7 (4 + 3).

Conclusion

Ratios of MD measures can be used in evaluation of peripheral zone prostate cancer aggressiveness; however tumor MD measures alone perform similarly.

Diffusion-kurtosis imaging predicts early radiotherapy response in nasopharyngeal carcinoma patients

In this prospective study, we analyzed diffusion kurtosis imaging (DKI) parameters to predict the early response to radiotherapy in 23 nasopharyngeal carcinoma (NPC) patients. All patients underwent conventional magnetic resonance imaging (MRI) and DKI before and after radiotherapy. The patients were divided into response (RG; no residual tumors; 16/23 patients) and no-response (NRG; residual tumors; 7/23 patients) groups, based on MRI and biopsy results 3 months after radiotherapy. The maximum diameter of tumors in RG and NRG patients were similar prior to radiotherapy (p=0.103). The pretreatment diffusion coefficient (D) parameters (D_axis, D_mean and D_rad) were higher in RG than NRG patients (p=0.022, p=0.027 and p=0.027). Conversely, the pre-treatment fractional anisotropy (FA) and kurtosis coefficient (K) parameters (K_axis, K_fa, K_mean, K_rad and Mkt) were lower in RG than NRG patients (p=0.015, p=0.022, p=0.008, p=0.004, p=0.001, p=0.002). The K_rad coefficient (0.76) was the best parameter to predict the radiotherapy response. Based on receiver operating characteristic curve analysis K_rad showed 71.4% sensitivity and 93.7% specificity (AUC: 0.897, 95% CI, 0.756-1). Multivariate analysis indicated DKI parameters were independent prognostic factors for the short-term effect in NPC. Thus, DKI predicts the early response to radiotherapy in NPC patients.

Diffusion Kurtosis Imaging Helps to Predict Upgrading in Biopsy-Proven Prostate Cancer With a Gleason Score of 6

OBJECTIVE

The purpose of this study was to investigate whether diffusion kurtosis imaging (DKI) is useful for predicting upgrades in Gleason score (GS) in biopsy-proven prostate cancer with a GS of 6.

MATERIALS AND METHODS

A total of 46 patients with biopsy-proven GS 6 prostate cancer, 3-T DWI results, and surgical pathologic results were retrospectively included in the study. DWI data were postprocessed with monoexponential and DK models to quantify the apparent diffusion coefficient (ADC), apparent diffusion for gaussian distribution (D_app), and apparent kurtosis coefficient (K_app). The volume of the lesions, prostate-specific antigen (PSA) level, and diffusion variables (ADC_min, D_appmin, K_appmax, ADC_mean, D_appmean, and K_appmean) were evaluated. PSA and DKI were combined as a parameter in a logistic regression model. The utility of these parameters in predicting an upgrade in GS was analyzed with ROC regression.

RESULTS

The rate of GS upgrade was 50.0% (23/46). The GS upgrade group had significantly lower ADC_min (p = 0.007), ADC mean (p = 0.003), D appmin (p < 0.001), and D_appmean (p = 0.001) values and significantly higher K_appmax (p = 0.003), K_appmean (p = 0.005), and PSA (p = 0.004) values than the group that did not have an upgrade. Among single parameters, K_appmax had the highest ROC AUC value (0.819, p < 0.05), and among all the parameters and models, PSA-K_appmax had the highest AUC (0.868, p < 0.05) and Youden index (0.6522).

CONCLUSION

The results showed that DKI may help in prediction of GS upgrade in biopsy-proven GS 6 prostate cancer. The comprehensive consideration of DKI and PSA may be a promising approach to predicting GS upgrade.

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

OBJECTIVE

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).

METHODS

This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis.

RESULTS

For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]).

CONCLUSION

Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa.

KEY POINTS

• Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

Diffusion tensor imaging in abdominal organs

Initially, diffusion tensor imaging (DTI) was mainly applied in studies of the human brain to analyse white matter tracts. As DTI is outstanding for the analysis of tissue´s microstructure, the interest in DTI for the assessment of abdominal tissues has increased continuously in recent years. Tissue characteristics of abdominal organs differ substantially from those of the human brain. Further peculiarities such as respiratory motion and heterogenic tissue composition lead to difficult conditions that have to be overcome in DTI measurements. Thus MR measurement parameters have to be adapted for DTI in abdominal organs. This review article provides information on the technical background of DTI with a focus on abdominal imaging, as well as an overview of clinical studies and application of DTI in different abdominal regions. Copyright © 2015 John Wiley & Sons, Ltd.

Non-Gaussian diffusion imaging for malignant and benign pulmonary nodule differentiation: a preliminary study

BACKGROUND

Diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) has demonstrated inconsistent results in pulmonary nodule differentiation. Diffusion kurtosis imaging (DKI), which quantifies non-Gaussian diffusion, is believed to better characterize tissue micro-structure than conventional DWI.

PURPOSE

To assess the feasibility of DKI in human lungs and to compare its diagnostic value with standard DWI in differentiating malignancies from benign pulmonary nodules.

MATERIAL AND METHODS

Thirty-five pulmonary nodules in 32 consecutive patients were evaluated by DKI by using 3b-values of 0, 500, and 1000 s/mm² and conventional DWI with b values of 0 and 800 s/mm². Two observers independently evaluated and compared diagnostic accuracy of mean kurtosis (MK) and ADC values in differentiating malignancies from benign pulmonary nodules. The intra- and inter-observer repeatability (intra-class correlation coefficient [ICC]) were also assessed for each derived measures.

RESULTS

The diagnostic accuracy, and the area under curve (AUC) in differentiating malignancies from benign pulmonary nodule, were not significantly higher for MK (Obs. 1a: 85.70%, 0.87; Obs. 1b: 80.00%, 0.80; and Obs. 2: 82.80%, 0.91) as compared to ADC (Obs. 1a: 77.14%, 0.81; Obs. 1b: 80.00%, 0.85; and Obs. 2: 77.14%, 0.85 respectively). The intra- and inter-observer agreement (ICC) for malignant and benign lesions was substantial for each reading.

CONCLUSION

The initial results of this study indicate the feasibility of DKI in human lungs. However, there was no significant benefit of DKI derived MK values over ADC for malignant and benign pulmonary nodule differentiation.

The value of diffusion kurtosis magnetic resonance imaging for assessing treatment response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer

OBJECTIVES

To evaluate the feasibility and value of diffusion kurtosis (DK) imaging in assessing treatment response to neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancer (LARC).

METHODS

Forty-one patients were included. All patients underwent pre- and post-CRT DCE-MRI on a 3.0-Tesla MRI scanner. Imaging indices (D _app , K _app and ADC values) were measured. Change value (∆X) and change ratio (r∆X) were calculated. Pathological tumour regression grade scores (Mandard) were the standard reference (good responders: pTRG 1-2; poor responders: pTRG 3-5). Diagnostic performance was compared using ROC analysis.

RESULTS

For the pre-CRT measurements, pre-D _app-10th was significantly lower in the good responder group than that of the poor responder group (p = 0.036). For assessing treatment response to neoadjuvant CRT, pre-D _app-10th resulted in AUCs of 0.753 (p = 0.036) with a sensitivity of 66.67 % and a specificity of 77.78 %. The r∆D _app had a relatively high AUC (0.859) and high sensitivity (100 %) compared with other image indices.

CONCLUSIONS

DKI is feasible for selecting good responders for neoadjuvant CRT for LARC.

KEY POINTS

• LARC responded well after neoadjuvant chemoradiotherapy with lower pre-D _app-10th . • LARC responded well with greater increases in mean ADC and D _app . • The change ratio of D _app (r∆D _app ) had a relatively better diagnostic performance.

Characterization of clear cell renal cell carcinoma with diffusion kurtosis imaging: correlation between diffusion kurtosis parameters and tumor cellularity

The aim of this study was to evaluate the role of diffusion kurtosis imaging (DKI) in the characterization of clear cell renal cell carcinoma (ccRCC) and to correlate DKI parameters with tumor cellularity. Fifty-nine patients with pathologically diagnosed ccRCCs were evaluated by DKI on a 3-T scanner. Regions of interest were drawn on the maps of the mean diffusion coefficient (MD) and mean diffusion kurtosis (MK). All ccRCCs were histologically graded according to the Fuhrman classification system. Tumor cellularity was measured by the nuclear-to-cytoplasm (N/C) ratio and the number of tumor cell nuclei (NTCN). ccRCCs were classified as grade 1 (n = 23), grade 2 (n = 24), grade 3 (n = 10) and grade 4 (n = 3). Both MD and MK could readily discriminate between normal renal parenchyma and ccRCCs (p < 0.001), and receiver operating characteristic (ROC) curve analysis showed that MK exhibited a better performance with an area under the ROC curve of 0.874 and sensitivity/specificity of 68.33%/100% (p < 0.001). Further, MD and MK were significantly different between grade 1 and grades 3 and 4 (p = 0.01, p < 0.001) and between grade 2 and grades 3 and 4 (p = 0.015, p < 0.005), respectively. However, no significant difference was found between grade 1 and grade 2 (p > 0.05) for both MD and MK. With regard to NTCN, no significant difference was found between any two grades (p > 0.05), and the N/C ratio changed significantly with grade (p < 0.01, between any two grades). Negative correlations were found between MK and MD (r = -0.56, p < 0.001), and between MD and N/C ratio (r = -0.36, p < 0.005), whereas MK and the N/C ratio were positively correlated (r = 0.45, p = 0.003). DKI could quantitatively characterize ccRCC with different grades by probing non-Gaussian diffusion properties related to changes in the tumor microenvironment or tissue complexities in the tumor. Copyright © 2016 John Wiley & Sons, Ltd.

New RESOLVE-Based Diffusional Kurtosis Imaging in MRI-Visible Prostate Cancer: Effect of Reduced b Value on Image Quality and Diagnostic Effectiveness

OBJECTIVE

The purpose of this article was to investigate whether a new readout segmentation of long variable echo-trains (RESOLVE)-based diffusional kurtosis imaging (DKI) with reduced b value technique can affect image quality and diagnostic effectiveness in MRI-visible prostate cancer (PCA).

SUBJECTS AND METHODS

Prostatic RESOLVE DKI (0-1400 s/mm2) was prospectively performed for 12 volunteers. The optimal protocol was then performed in 108 MRI-visible PCAs to determine whether it can compete against a preferred b-value set (0-2000 s/mm(2)) regarding image quality and diagnostic effectiveness. Images were interpreted by two independent radiologists using the prostate imaging reporting and data system (PI-RADS). Readers' concordance and diagnostic effectiveness were tested with the Fleiss kappa and area under the ROC curve (Az) analyses.

RESULTS

A b value of 1400 s/mm(2) generated a larger apparent diffusion coefficient of gaussian distribution (Dapp) (1.35 ± 0.31 vs 1.30 ± 0.30 mm(2)/s; p < 0.001) and apparent kurtosis coefficient (Kapp) (1.11 ± 0.26 vs 1.00 ± 0.21; p < 0.001) in PCA than did a b value of 2000 s/mm(2). Interreader agreement using PI-RADS was relatively low when Dapp and Kapp maps were excluded from image interpretations (κ = 0.39-0.41 vs κ = 0.66-0.68 with Dapp and Kapp maps). Interreader agreement in staging PCA was relatively high (κ > 0.80) and was not influenced by reducing the b value. The power of Dapp and Kapp to differentiate PCA from normal tissue (Az = 0.97-0.98), tissue with a Gleason score less than or equal to 3 + 4 from tissue with a Gleason score greater than 3 + 4 (Az = 0.77-0.82), and PCA stage lower than pT3 from stage pT3 and higher PCA (Az = 0.70-0.75) was not significantly degraded by reducing the b value.

CONCLUSION

We found that b values significantly influenced image quality, PI-RADS score, and DKI outputs but did not degrade the diagnostic effectiveness of DKI parameters to detect and classify PCA.

Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation

The use of multiparametric MRI scans for the evaluation of men with prostate cancer has increased dramatically and is likely to continue expanding as new developments come to practice. However, it has not yet gained the same level of acceptance of other imaging tests. Partly, this is because of the use of suboptimal protocols, lack of standardization, and inadequate patient preparation. In this manuscript, we describe several practical aspects of prostate MRI that may facilitate the implementation of new prostate imaging programs or the expansion of existing ones.

The expanding landscape of diffusion-weighted MRI in prostate cancer

The added value of diffusion-weighted magnetic resonance imaging (DW-MRI) for the detection, localization, and staging of primary prostate cancer has been extensively reported in original studies and meta-analyses. More recently, DW-MRI and related techniques have been used to noninvasively assess prostate cancer aggressiveness and estimate its biological behavior. The present article aims to summarize the potential applications of DW-MRI for noninvasive optimization of pretherapeutic risk assessment, patient management decisions, and evaluation of treatment response.

Correlation of standard diffusion-weighted imaging and diffusion kurtosis imaging with distant metastases of rectal carcinoma

PURPOSE

To investigate the correlation of standard diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) with distant metastases of rectal carcinoma.

MATERIALS AND METHODS

Fifty-eight patients with rectal carcinoma (27 with distant metastasis and 31 with no metastasis) were included in this study. The apparent diffusion coefficient (ADC) value from standard DWI (b values of 0 and 1000 sec/mm(2) ), Dapp , and Kapp from DKI (b values of 0, 700, 1400, and 2000 sec/mm(2) ) were acquired with a 3.0T magnetic resonance imaging (MRI) scanner. These quantitative parameters were calculated from the entire tumors. Receiver operating characteristic curve analyses were conducted to assess the utility for discrimination of tumor with distant metastasis and those without metastasis. Parameters were compared using the independent-samples t-test.

RESULTS

The histogram metrics 10th percentile of Dapp (Dapp-10th ) and ADC values (ADC10th ) were significantly lower in the distant metastasis group than those without metastasis (972.5 ± 118.8 vs. 1121.3 ± 133.8 × 10(-6) mm(2) /s, P = 0.03; 809.2 ± 67.1 vs. 856.2 ± 72.1 × 10(-6) mm(2) /s, P = 0.03). Dapp-10th showed relatively higher area under the curve (AUC) (0.856 vs. 0.669, P = 0.024), and higher specificity (100% vs. 68%) than ADC10th did for differentiation of lesions with distant metastasis from those without metastasis.

CONCLUSION

DKI was relatively better than standard DWI in discriminating rectal carcinoma with distant metastasis from those without metastasis. J. Magn. Reson. Imaging 2016;44:221-229.

Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice

Technologic advances enable performance of diffusion-weighted imaging (DWI) at ultrahigh b-values, where standard monoexponential model analysis may not apply. Rather, non-Gaussian water diffusion properties emerge, which in cellular tissues are, in part, influenced by the intracellular environment that is not well evaluated by conventional DWI. The novel technique, diffusion kurtosis imaging (DKI), enables characterization of non-Gaussian water diffusion behavior. More advanced mathematical curve fitting of the signal intensity decay curve using the DKI model provides an additional parameter Kapp that presumably reflects heterogeneity and irregularity of cellular microstructure, as well as the amount of interfaces within cellular tissues. Although largely applied for neural applications over the past decade, a small number of studies have recently explored DKI outside the brain. The most investigated organ is the prostate, with preliminary studies suggesting improved tumor detection and grading using DKI. Although still largely in the research phase, DKI is being explored in wider clinical settings. When assessing extracranial applications of DKI, careful attention to details with which body radiologists may currently be unfamiliar is important to ensure reliable results. Accordingly, a robust understanding of DKI is necessary for radiologists to better understand the meaning of DKI-derived metrics in the context of different tumors and how these metrics vary between tumor types and in response to treatment. In this review, we outline DKI principles, propose biostructural basis for observations, provide a comparison with standard monoexponential fitting and the apparent diffusion coefficient, report on extracranial clinical investigations to date, and recommend technical considerations for implementation in body imaging.

Diffusion anisotropy in fresh and fixed prostate tissue ex vivo

PURPOSE

To investigate diffusion anisotropy in whole human prostate specimens

METHODS

Seven whole radical prostatectomy specimens were obtained with informed patient consent and institutional ethics approval. Diffusion tensor imaging was performed at 9.4 Tesla. Diffusion tensors were calculated from the native acquired data and after progressive downsampling

RESULTS

Fractional anisotropy (FA) decreased as voxel volume increased, and differed widely between prostates. Fixation decreased mean FA by ∼0.05-0.08 at all voxel volumes but did not alter principle eigenvector orientation. In unfixed tissue high FA (> 0.6) was found only in voxels of volume <0.5 mm(3) , and then only in a small fraction of all voxels. At typical clinical voxel volumes (4-16 mm(3) ) less than 50% of voxels had FA > 0.25. FA decreased at longer diffusion times (Δ = 60 or 80 ms compared with 20 ms), but only by ∼0.02 at typical clinical voxel volume. Peripheral zone FA was significantly lower than transition zone FA in five of the seven prostates

CONCLUSION

FA varies widely between prostates. The very small proportion of clinical size voxels with high FA suggests that in clinical DWI studies ADC based on three-direction measurements will be minimally affected by anisotropy. Magn Reson Med 76:626-634, 2016. © 2015 Wiley Periodicals, Inc.

Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis

Objectives

A new method that can estimate diffusional kurtosis image (DKI), estimated DKI (eDKI), parallel and perpendicular to neuronal fibres from greatly limited image data was designed to enable quick and practical assessment of DKI in clinics. The purpose of this study was to discuss the potential of this method for clinical use.

Methods

Fourteen healthy volunteers were examined with a 3-Tesla MRI. The diffusion-weighting parameters included five different b-values (0, 500, 1,500, 2,000 and 2,500 s/mm²) with 64 different encoding directions for each of the b-values. K values were calculated by both conventional DKI (convDKI) and eDKI from these complete data, and also from the data that the encoding directions were abstracted to 32, 21, 15, 12 and 6. Error-pixel ratio and the root mean square error (RMSE) compared with the standard were compared between the methods (Wilcoxon signed-rank test: P < 0.05 was considered significant).

Results

Error-pixel ratio was smaller in eDKI than in convDKI and the difference was significant. In addition, RMSE was significantly smaller in eDKI than in convDKI, or otherwise the differences were not significant when they were obtained from the same data set.

Conclusion

eDKI might be useful for assessing DKI in clinical settings.

Key Points

• A method to practically estimate axial/radial DKI from limited data was developed.

• The high robustness of the proposed method can greatly improve map images.

• The accuracy of the proposed method was high.

• Axial/radial K maps can be calculated from limited diffusion-encoding directions.

• The proposed method might be useful for assessing DKI in clinical settings.

Updates in advanced diffusion-weighted magnetic resonance imaging techniques in the evaluation of prostate cancer

Diffusion-weighted magnetic resonance imaging (DW-MRI) is considered part of the standard imaging protocol for the evaluation of patients with prostate cancer. It has been proven valuable as a functional tool for qualitative and quantitative analysis of prostate cancer beyond anatomical MRI sequences such as T2-weighted imaging. This review discusses ongoing controversies in DW-MRI acquisition, including the optimal number of b-values to be used for prostate DWI, and summarizes the current literature on the use of advanced DW-MRI techniques. These include intravoxel incoherent motion imaging, which better accounts for the non-mono-exponential behavior of the apparent diffusion coefficient as a function of b-value and the influence of perfusion at low b-values. Another technique is diffusion kurtosis imaging (DKI). Metrics from DKI reflect excess kurtosis of tissues, representing its deviation from Gaussian diffusion behavior. Preliminary results suggest that DKI findings may have more value than findings from conventional DW-MRI for the assessment of prostate cancer.

Correlation of gleason scores with magnetic resonance diffusion tensor imaging in peripheral zone prostate cancer

BACKGROUND

To investigate tumor aggressiveness in peripheral zone prostate cancer (PCa) by correlating Gleason score (GS) with diffusion tensor imaging (DTI) from multiparametric magnetic resonance imaging (MRI) at 3.0 Tesla (T).

METHODS

Eighty-three patients with pathological proven peripheral zone PCa whose GS in at least one core biopsy met the criteria(GS ≤3+3, GS 3+4, GS 4+3, or GS ≥4+4) were included in this study. DTI was performed using b values of 0 and 800 s/mm(2) with 32 directions in all patients on a 3.0T MRI scanner. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were calculated from the DTI data of patients with the previously mentioned four categories of Gleason scores. An association between DTI measurements(FA, ADC) and GS was tested using the Spearman rank correlation analysis.

RESULTS

FA values in the sextants found to harbor cancer were positively correlated with the GS(r = 0.48; P < 0.001), while the ADC values were negatively correlated with GS(r = -0.54; P < 0.001). Statistical significance(P < 0.05) was found for FA values among different GS groups, with the exception of GS 3+4 versus GS 4+3 (P = 0.105). The differences between the ADC values were statistically significant for all four different scores(all P < 0.05).

CONCLUSION

Quantitative DTI at 3.0T MRI shows a significant association with GS in the evaluation of tumor aggressiveness in peripheral zone PCa, which may be useful to ensure concordance of biopsy results and therefore make the appropriate decision in the management of patients with PCa.