Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors

Objective

To investigate potential relationships between diffusion kurtosis imaging (DKI)-derived parameters using whole-tumour volume histogram analysis and clinicopathological prognostic factors in patients with rectal adenocarcinoma.

Material and methods

79 consecutive patients who underwent MRI examination with rectal adenocarcinoma were retrospectively evaluated. Parameters D, K and conventional ADC were measured using whole-tumour volume histogram analysis. Student’s t-test or Mann-Whitney U-test, receiver operating characteristic curves and Spearman’s correlation were used for statistical analysis.

Results

Almost all the percentile metrics of K were correlated positively with nodal involvement, higher histological grades, the presence of lymphangiovascular invasion (LVI) and circumferential margin (CRM) (p<0.05), with the exception of between K_10th, K_90th and histological grades. In contrast, significant negative correlations were observed between 25th, 50th percentiles and mean values of ADC and D, as well as ADC_10th, with tumour T stages (p< 0.05). Meanwhile, lower 75th and 90th percentiles of ADC and D values were also correlated inversely with nodal involvement (p< 0.05). K_mean showed a relatively higher area under the curve (AUC) and higher specificity than other percentiles for differentiation of lesions with nodal involvement.

Conclusion

DKI metrics with whole-tumour volume histogram analysis, especially K parameters, were associated with important prognostic factors of rectal cancer.

Key Points

? K correlated positively with some important prognostic factors of rectal cancer. ? K _mean showed higher AUC and specificity for differentiation of nodal involvement. ? DKI metrics with whole-tumour volume histogram analysis depicted tumour heterogeneity.

     

Whole brain analyses of age-related microstructural changes quantified using different diffusional magnetic resonance imaging methods

Purpose

The new diffusional magnetic resonance imaging (dMRI) techniques, diffusional kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) have been developed to clarify the microstructural changes. To our knowledge, however, there is little information on the similarities and differences of these metrics evaluated by the image-by-image paired t test.

Materials and methods

Twenty-three healthy subjects underwent dMRI. We estimated the relationships of these metrics evaluated by the image-by-image paired t-test and compared aging effects on each metric.

Results

We found that fractional anisotropy (FA), mean kurtosis (MK) derived from DKI and neurite density index (NDI) values derived from NODDI correlated with each other positively, and mean diffusivity (MD) and orientation dispersion index (ODI) values from NODDI correlated negatively with the FA value. There were no significant relationships of age with FA or MD values, while MK, ODI and NDI values showed significant correlations with age.

Conclusion

These results may indicate not only the similar tendency among the metrics, but also the higher sensitivity of NODDI and DKI to the changes in microstructural tissue organization with advancing age. These techniques could shed light on both normal and degenerated brain changes.

     

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.

     

Alteration of putaminal fractional anisotropy in Parkinson’s disease: a longitudinal diffusion kurtosis imaging study

Purpose

In Parkinson’s disease (PD), pathological microstructural changes occur that may be detected using diffusion magnetic resonance imaging (dMRI). However, there are few longitudinal studies that explore the effect of disease progression on diffusion indices.

Methods

We prospectively included 76 patients with PD and 38 healthy controls (HC), all of whom underwent diffusion kurtosis imaging (DKI) as part of the prospective Swedish BioFINDER study at baseline and 2 years later. Annualized rates of change in DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK), were estimated in the gray matter (GM) by placing regions of interest (ROIs) in the basal ganglia and the thalamus, and in the white matter (WM) by tract-based spatial statistics (TBSS) analysis.

Results

When adjusting for potential confounding factors (age, gender, baseline-follow-up interval, and software upgrade of MRI scanner), only a decrease in FA in the putamen of PD patients (β?=???0.248, P?<?.01) over 2 years was significantly different from the changes observed in HC over the same time period. This 2-year decrease in FA in the putamen in PD correlated with higher l-dopa equivalent dose at baseline (Spearman’s rho?=?.399, P?<?.0001).

Conclusion

The study indicates that in PD microstructural changes in the putamen occur selectively over a 2-year period and can be detected with DKI.

     

Histogram analysis of apparent diffusion coefficients after neoadjuvant chemotherapy in breast cancer

Purpose

To evaluate the changes in the apparent diffusion coefficient (ADC) histogram during neoadjuvant chemotherapy (NAC) for breast cancer, and to compare the observed changes in pathologically verified responders and non-responders.

Materials and methods

Sixty-two patients received NAC followed by surgery. Responders were defined by a tumor cell reduction of at least 30 % using the Miller–Payne grading system. All the patients underwent 3T magnetic resonance with diffusion-weighted imaging (b values of 0 and 750 s/mm²) before the NAC and after the completion of two cycles of NAC.

Results

Mean, minimum, 10th, 25th, 50th, and 75th percentile of ADCs significantly increased after NAC and maximum ADC significantly decreased. Skewness became less positive and kurtosis decreased. A tendential, although not statistically significant, higher increase in mean, minimum, 10th, 25th, 50th, 75th, and 90th percentiles of ADCs was observed in responders in comparison with non-responders.

Conclusion

ADC histogram analysis quantitatively demonstrates the alterations during the treatment course.

     

Intravoxel incoherent motion MR imaging for breast lesions: comparison and correlation with pharmacokinetic evaluation from dynamic contrast-enhanced MR imaging

Objectives

To compare diagnostic performance for breast lesions by quantitative parameters derived from intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and to explore whether correlations exist between these parameters.

Methods

IVIM and DCE MRI were performed on a 1.5-T MRI scanner in patients with suspicious breast lesions. Thirty-six breast cancers and 23 benign lesions were included in the study. Quantitative parameters from IVIM (D, f and D*) and DCE MRI (K^trans, K_ep, V_e and V_p) were calculated and compared between malignant and benign lesions. Spearman correlation test was used to evaluate correlations between them.

Results

D, f, D* from IVIM and K^trans, K_ep, V_p from DCE MRI were statistically different between breast cancers and benign lesions (p?<?0.05, respectively) and D demonstrated the largest area under the receiver-operating characteristic curve (AUC?=?0.917) and had the highest specificity (83 %). The f value was moderately statistically correlated with V_p (r?=?0.692) and had a poor correlation with K^trans (r?=?0.456).

Conclusions

IVIM MRI is useful in the differentiation of breast lesions. Significant correlations were found between perfusion-related parameters from IVIM and DCE MRI. IVIM may be a useful adjunctive tool to standard MRI in diagnosing breast cancer.

Key Points

? IVIM provided diffusion as well as perfusion information ? IVIM could help differential diagnosis of breast lesions ? Correlations were found between perfusion-related parameters from IVIM and DCE MRI

     

Spinal cord microstructure integrating phase-sensitive inversion recovery and diffusional kurtosis imaging

Purpose

The aim of this prospective study was to determine the feasibility in terms of repeatability and reproducibility of diffusional kurtosis imaging (DKI) for microstructural assessment of the normal cervical spinal cord (cSC) using a phase-sensitive inversion recovery (PSIR) sequence as the anatomical reference for accurately defining white-matter (WM) and gray-matter (GM) regions of interests (ROIs).

Methods

Thirteen young healthy subjects were enrolled to undergo DKI and PSIR sequences in the cSC. The repeatability and reproducibility of kurtosis metrics and fractional anisotropy (FA) were calculated in GM, WM, and cerebral-spinal-fluid (CSF) ROIs drawn by two independent readers on PSIR images of three different levels (C1–C4). The presence of statistically significant differences in DKI metrics for levels, ROIs (GM, WM, and CSF) repeatability, reproducibility, and inter-reader agreement was evaluated.

Results

Intra-class correlation coefficients between the two readers ranged from good to excellent (0.75 to 0.90). The inferior level consistently had the highest concordance. The lower values of scan–rescan variability for all DKI parameters were found for the inferior level. Statistically significant differences in kurtosis values were not found in the lateral white-matter bundles of the spinal cord.

Conclusion

The integration of DKI and PSIR sequences in a clinical MR acquisition to explore the regional microstructure of the cSC in healthy subjects is feasible, and the results obtainable are reproducible. Further investigation will be required to verify the possibility to translate this method to a clinical setting to study patients with SC involvement especially in the absence of MRI abnormalities on standard sequences.

     

Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases

Purpose

To determine if magnetic resonance imaging (MRI) histogram analyses can help predict response to chemotherapy in patients with colorectal hepatic metastases by using response evaluation criteria in solid tumours (RECIST1.1) as the reference standard.

Materials and methods

Standard MRI including diffusion-weighted imaging (b=0, 500 s/mm²) was performed before chemotherapy in 53 patients with colorectal hepatic metastases. Histograms were performed for apparent diffusion coefficient (ADC) maps, arterial, and portal venous phase images; thereafter, mean, percentiles (1st, 10th, 50th, 90th, 99th), skewness, kurtosis, and variance were generated. Quantitative histogram parameters were compared between responders (partial and complete response, n=15) and non-responders (progressive and stable disease, n=38). Receiver operator characteristics (ROC) analyses were further analyzed for the significant parameters.

Results

The mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, 99th percentile of the ADC maps were significantly lower in responding group than that in non-responding group (p=0.000–0.002) with area under the ROC curve (AUCs) of 0.76–0.82. The histogram parameters of arterial and portal venous phase showed no significant difference (p>0.05) between the two groups.

Conclusion

Histogram-derived parameters for ADC maps seem to be a promising tool for predicting response to chemotherapy in patients with colorectal hepatic metastases.

Key Points

? ADC histogram analyses can potentially predict chemotherapy response in colorectal liver metastases. ? Lower histogram-derived parameters (mean, percentiles) for ADC tend to have good response. ? MR enhancement histogram analyses are not reliable to predict response.

     

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status,histological subtype,and molecular prognostic factors

Purpose

To examine heterogeneous breast cancer through intravoxel incoherent motion (IVIM) histogram analysis.

Materials and methods

This HIPAA-compliant, IRB-approved retrospective study included 62 patients (age 48.44?±?11.14 years, 50 malignant lesions and 12 benign) who underwent contrast-enhanced 3 T breast MRI and diffusion-weighted imaging. Apparent diffusion coefficient (ADC) and IVIM biomarkers of tissue diffusivity (D_t), perfusion fraction (f_p), and pseudo-diffusivity (D_p) were calculated using voxel-based analysis for the whole lesion volume. Histogram analysis was performed to quantify tumour heterogeneity. Comparisons were made using Mann–Whitney tests between benign/malignant status, histological subtype, and molecular prognostic factor status while Spearman’s rank correlation was used to characterize the association between imaging biomarkers and prognostic factor expression.

Results

The average values of the ADC and IVIM biomarkers, D_t and f_p, showed significant differences between benign and malignant lesions. Additional significant differences were found in the histogram parameters among tumour subtypes and molecular prognostic factor status. IVIM histogram metrics, particularly f_p and D_p, showed significant correlation with hormonal factor expression.

Conclusion

Advanced diffusion imaging biomarkers show relationships with molecular prognostic factors and breast cancer malignancy. This analysis reveals novel diagnostic metrics that may explain some of the observed variability in treatment response among breast cancer patients.

Key Points

? Novel IVIM biomarkers characterize heterogeneous breast cancer.? Histogram analysis enables quantification of tumour heterogeneity.? IVIM biomarkers show relationships with breast cancer malignancy and molecular prognostic factors.

     

Intravoxel incoherent motion MR imaging of early cervical carcinoma: correlation between imaging parameters and tumor-stroma ratio

Objectives

To investigate if intravoxel incoherent motion (IVIM) MR imaging can predict the tumour-stroma ratio (TSR) in patients with early cervical carcinoma.

Methods

Fifty-four patients with early cervical carcinoma were prospectively enrolled into this study. All patients underwent IVIM imaging and parameters including D, D* and f value were measured. The tumours were classified into stroma-rich and stroma-poor group according to TSR, and comparisons of IVIM parameters between two groups were performed. The relationships between IVIM parameters and TSR were analysed by using a multivariate multi-regression analysis.

Results

D and f values were significantly lower in stroma-poor tumours than in stroma-rich tumours (p=0.02, 0.04), while the difference in D* value between two groups didn't achieve statistical significance (p=0.09). The areas under ROC curves of D and f values in discriminating stroma-rich and stroma-poor tumours were 0.835 (95%CI=0.616~0.905) and 0.686 (95%CI=0.575~0.798). In multiple linear regression analysis, D value, pathologic type, histologic grade, tumour size and f value were independently correlated with TSR of cervical carcinoma.

Conclusions

D and f values are independently correlated with TSR of cervical carcinoma and have the potential for quantitative measurement of TSR.

Key Points

? TSR is a recognized independent prognostic factor in many solid tumours. ? D and f values measured by IVIM MRI are independently correlated with TSR while D* is not. ? IVIM offers the potential to predict TSR.

     

Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas

Introduction

In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.

Methods

Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (n = 19), grade III (n = 20) and grade IV (n = 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy.

Results

Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of P = 0.029 (0.0421) for grade II vs. III and P = 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability, P = 0.018 (0.038) for grade II vs. III and P = 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed P values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK).

Conclusion

In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters.

     

HRCT texture analysis for pure or part-solid ground-glass nodules: distinguishability of adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma

Purpose

To distinguish between adenocarcinoma in situ (AIS)–minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) showing pure or part-solid ground-glass nodules (GGNs) by high-resolution computed tomography (HRCT) texture analysis.

Materials and methods

This retrospective study included 101 consecutive patients with 115 pure or part-solid GGNs ≤ 3 cm diameter, which were surgically resected and pathologically diagnosed with AIS, MIA, or IAC (48 AIS–MIA and 67 IAC) between April 2011 and March 2015. Each tumor was manually segmented on axial CT images, and the following texture features were calculated: volume, mass, mean CT value, variance, skewness, kurtosis, entropy, uniformity, and percentile CT numbers (10th, 25th, 50th, 75th, 90th, 95th percentiles). The differences between AIS–MIA and IAC were statistically evaluated using univariate, multivariate, and receiver operating characteristic analysis.

Results

Compared with IAC, AIS–MIA had significantly greater skewness, kurtosis, and uniformity, whereas in the other parameters, AIS–MIA demonstrated significantly lower values than those of IAC. Multivariate analysis revealed that independent differentiators were the 90th percentile CT numbers (P < 0.001) and entropy (P = 0.005) with an excellent accuracy (area under the curve, 0.90).

Conclusions

The 90th percentile CT numbers and entropy can accurately distinguish AIS–MIA from IAC.

     

Intravoxel incoherent motion magnetic resonance imaging to predict vesicoureteral reflux in children with urinary tract infection

Objectives

To compare the diffusion parameters of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) between the “reflux” and the “non-reflux” kidneys, and to evaluate the feasibility of using IVIM DWI to predict vesicoureteral reflux (VUR) in children with a urinary tract infection (UTI).

Methods

Eighty-three kidneys from 57 pediatric patients with a UTI were classified into “reflux” and “non-reflux” groups according to voiding cystourethrography (VCUG) results. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (PF) were measured and compared in the renal pelvis of both groups. Four indices (D*/ADC, PF/ADC, D*/D, and PF/D) were calculated and receiver operating characteristic (ROC) curve analyses were performed.

Results

VURs were detected on VCUG in 21 kidneys. PF and D* were significantly higher in the “reflux” group than in the “non-reflux” group. The indices were all significantly higher. The PF/D index showed the best diagnostic performance in predicting VUR in children with UTI (A_z?=?0.864).

Conclusion

PF and D* were significantly higher in the "reflux" kidney than in the "non-reflux" kidney. Our new index (PF/D) could prove useful for predicting VUR.

Key Points

? IVIM DWI is both radiation-free and contrast media-free. ? IVIM DWI index is easily calculated by combining diffusion parameters. ? IVIM DWI may help predict VUR in children with UTI. ? PF is significantly higher in the “reflux” than the “non-reflux” kidneys. ? A new VUR index, PF/D could prove useful for predicting VUR.

     

Using intravoxel incoherent motion MR imaging to study the renal pathophysiological process of contrast-induced acute kidney injury in rats: Comparison with conventional DWI and arterial spin labelling

Objectives

To investigate the potential of intravoxel incoherent motion (IVIM) to assess the renal pathophysiological process in contrast-induced acute kidney injury (CIAKI).

Methods

Twenty-seven rats were induced with CIAKI model, six rats were imaged longitudinally at 24 h prior to and 30 min, 12, 24, 48, 72 and 96 h after administration; three rats were randomly chosen from the rest for serum creatinine and histological studies. D, f, D* and ADC were calculated from IVIM, and renal blood flow (RBF) was obtained from arterial spin labelling (ASL).

Results

A progressive reduction in D and ADC was observed in cortex (CO) by 3.07 and 8.62 % at 30 min, and by 25.77 and 28.16 % at 48 h, respectively. A similar change in outer medulla (OM) and inner medulla (IM) was observed at a later time point (12–72 h). D values were strongly correlated with ADC (r?=?0.885). As perfusion measurement, a significant decrease was shown for f in 12–48 h and an increase in 72–96 h. A slightly different trend was found for D*, which was decreased by 26.02, 21.78 and 10.19 % in CO, OM and IM, respectively, at 30 min. f and D* were strongly correlated with RBF in the cortex (r?=?0.768, r?=?0.67), but not in the medulla.

Conclusions

IVIM is an effective imaging tool for monitoring progress in renal pathophysiology undergoing CIAKI.

Key Points

? IVIM analysis permits separate quantification of diffusion and perfusion. ? IVIM can provide useful biomarkers ifor changes in renal pathophysiology. ? IVIM can be useful for monitoring progress in renal pathophysiology undergoing CIAKI.

     

Differential diagnosis of parkinsonism by a combined use of diffusion kurtosis imaging and quantitative susceptibility mapping

Purpose

We investigated whether diffusion kurtosis imaging (DKI) and quantitative susceptibility mapping (QSM) could detect pathological changes that occur in Parkinson’s disease (PD), multiple system atrophy with predominant parkinsonism (MSA-P) or predominant cerebellar ataxia (MSA-C), and progressive supranuclear palsy syndrome (PSPS) and thus be used for differential diagnosis that is often difficult.

Methods

Seventy patients (41 with PD, 6 with MSA-P, 7 with MSA-C, 16 with PSPS) and 20 healthy controls were examined using a 3.0 T MRI scanner. From DKI and QSM data, we automatically obtained mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) values of the midbrain tegmentum (MBT), pontine crossing tract (PCT), and superior/middle cerebellar peduncles (CPs), which were used to calculate diffusion MBT/PCT ratios (dMPRs) and diffusion superior/middle CP ratios (dCPRs), as well as MS (magnetic susceptibility) values of the anterior/posterior putamen (PUa and PUp) and globus pallidus (GP).

Results

dMPRs of MK were significantly decreased in PSPS and increased in MSA-C compared with the other groups, while dCPRs of MK showed significant differences only between MSA-C and PD, PSPS, or control. MS values were significantly increased in the PUp of MSA-P and in the PUa and GP of PSPS compared with those in PD. The combined use of MK-dMPR and MS-PUp showed sensitivities of 83–100% and specificities of 81–100% for discriminating among the disease groups, respectively.

Conclusion

A quantitative assessment using DKI and QSM analyses, particularly MK-dMPR and MS-PUp values, can readily identify patients with parkinsonism.

     

Comparison between borderline ovarian tumors and carcinomas using semi-automated histogram analysis of diffusion-weighted imaging: focusing on solid components

Purpose

This study aimed to evaluate whether histogram analysis of the apparent diffusion coefficient (ADC) of a solid tumor component could distinguish borderline ovarian tumors from ovarian carcinoma.

Materials and methods

Sixteen pathologically proven borderline tumors and 21 carcinomas were retrospectively examined. Magnetic resonance (1.5-T) image data sets were coregistered, and the solid components of each tumor were semiautomatically segmented. ADC histograms of the solid components were extracted; modes, minimums, means, and 10th, 25th, 50th, 75th, and 90th percentiles of the histograms were compared between the two tumor types, and receiver-operating characteristic (ROC) analysis was performed.

Results

The mode, minimum, mean, 10th, 25th, 50th, and 75th percentile ADC values of solid components of borderline tumors were significantly larger than those of carcinomas. Among these, the 10th percentile values had the lowest p value (p = 0.0003). At ROC analysis, the area under the curve (AUC) in the 10th percentile was the greatest (0.854), and the best cutoff value in the 10th percentile provided the highest specificity (93.8 %).

Conclusions

ADC histograms of solid tumor components facilitated the distinction between borderline ovarian tumors and carcinoma. The 10th percentile ADC values had the best diagnostic performance.

     

Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study

Objectives

To explore the diagnostic value of MRI-based 3D texture analysis to identify texture features that can be used for discrimination of low-grade chondrosarcoma from enchondroma.

Methods

Eleven patients with low-grade chondrosarcoma and 11 patients with enchondroma were retrospectively evaluated. Texture analysis was performed using mint Lesion: Kurtosis, entropy, skewness, mean of positive pixels (MPP) and uniformity of positive pixel distribution (UPP) were obtained in four MRI sequences and correlated with histopathology. The Mann-Whitney U-test and receiver operating characteristic (ROC) analysis were performed to identify most discriminative texture features. Sensitivity, specificity, accuracy and optimal cut-off values were calculated.

Results

Significant differences were found in four of 20 texture parameters with regard to the different MRI sequences (p<0.01). The area under the ROC curve values to discriminate chondrosarcoma from enchondroma were 0.876 and 0.826 for kurtosis and skewness in contrast-enhanced T1 (ceT1w), respectively; in non-contrast T1, values were 0.851 and 0.822 for entropy and UPP, respectively. The highest discriminatory power had kurtosis in ceT1w with a cut-off ≥3.15 to identify low-grade chondrosarcoma (82 % sensitivity, 91 % specificity, accuracy 86 %).

Conclusion

MRI-based 3D texture analysis might be able to discriminate low-grade chondrosarcoma from enchondroma by a variety of texture parameters.

Key Points

? MRI texture analysis may assist in differentiating low-grade chondrosarcoma from enchondroma. ? Kurtosis in the contrast-enhanced T1w has the highest power of discrimination. ? Tools provide insight into tumour characterisation as a non-invasive imaging biomarker.

     

Diagnostic evaluation of magnetization transfer and diffusion kurtosis imaging for prostate cancer detection in a re-biopsy population

Objective

To evaluate diffusion kurtosis imaging (DKI) and magnetisation transfer imaging (MTI) compared to standard MRI for prostate cancer assessment in a re-biopsy population.

Methods

Thirty-patients were imaged at 3 T including DKI (K_app and D_app) with b-values 150/450/800/1150/1500 s/mm² and MTI performed with and without MT saturation. Patients underwent transperineal biopsy based on prospectively defined MRI targets. Receiver-operating characteristic (ROC) analyses assessed the parameters and Wilcoxon-signed ranked test assessed relationships between metrics.

Results

Twenty patients had ≥ 1 core positive for cancer in a total of 26 MRI targets (Gleason 3+3 in 8, 3+4 in 12, ≥ 4+3 in 6): 13 peripheral (PZ) and 13 transition zone (TZ). The apparent diffusion coefficient (ADC) and D_app were significantly lower and the K_app and MT ratio (MTR) significantly higher in tumour versus benign tissue (all p ≤ 0.005); ROC values 0.767-1.000. Normal TZ had: lower ADC and D_app and higher K_app and MTR compared to normal PZ. MTR showed a moderate correlation to K_app (r = 0.570) and D_app (r = -0.537) in normal tissue but a poor correlation in tumours. No parameter separated low-grade (Gleason 3+3) from high-grade (≥ 3+4) disease for either PZ (p = 0.414-0.825) or TZ (p = 0.148-0.825).

Conclusion

ADC, D_app, K_app and MTR all distinguished benign tissue from tumour, but none reliably differentiated low- from high-grade disease.

Key Points

? MTR was significantly higher in PZ and TZ tumours versus normal tissue ? K _app was significantly lower and D _app higher for PZ and TZ tumours ? There was no incremental value for DKI/MTI over mono-exponential ADC parameters ? No parameter could consistently differentiate low-grade (Gleason 3+3) from high-grade (≥ 3+4) disease ? Divergent MTR/DKI values in TZ tumours suggests they offer different functional information

     

Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis

Objectives

To evaluate automated texture-based segmentation of dual-energy CT (DECT) images in diffuse interstitial lung disease (DILD) patients and prognostic stratification by overlapping morphologic and perfusion information of total lung.

Methods

Suspected DILD patients scheduled for surgical biopsy were prospectively included. Texture patterns included ground-glass opacity (GGO), reticulation and consolidation. Pattern- and perfusion-based CT measurements were assessed to extract quantitative parameters. Accuracy of texture-based segmentation was analysed. Correlations between CT measurements and pulmonary function test or 6-minute walk test (6MWT) were calculated. Parameters of idiopathic pulmonary fibrosis/usual interstitial pneumonia (IPF/UIP) and non-IPF/UIP were compared. Survival analysis was performed.

Results

Overall accuracy was 90.47 % for whole lung segmentation. Correlations between mean iodine values of total lung, 50–97.5th (%) attenuation and forced vital capacity or 6MWT were significant. Volume of GGO, reticulation and consolidation had significant correlation with DLco or SpO₂ on 6MWT. Significant differences were noted between IPF/UIP and non-IPF/UIP in 6MWT distance, mean iodine value of total lung, 25–75th (%) attenuation and entropy. IPF/UIP diagnosis, GGO ratio, DILD extent, 25–75th (%) attenuation and SpO₂ on 6MWT showed significant correlations with survival.

Conclusion

DECT combined with pattern analysis is useful for analysing DILD and predicting survival by provision of morphology and enhancement.

Key Points

? Dual-energy CT (DECT) produces morphologic and parenchymal enhancement information. ? Automated lung segmentation enables analysis of disease extent and severity. ? This prospective study showed value of DECT in DILD patients. ? Parameters on DECT enable characterization and survival prediction of DILD.

     

Cardiac-gated intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for the investigation of intracranial cerebrospinal fluid dynamics in the lateral ventricle: a feasibility study

Purpose

Intravoxel incoherent motion (IVIM) in diffusion-weighted magnetic resonance imaging (DW-MRI) attributes the signal attenuation to the molecular diffusion and to a faster pseudo-diffusion. Purpose of the study was to demonstrate the feasibility of IVIM for the investigation of intracranial cerebrospinal fluid (CSF) dynamics.

Methods

Cardiac-gated DW-MRI images with fifteen b-values (0–1300s/mm²) along three orthogonal directions (mediolateral (ML), anteroposterior (AP), and craniocaudal (CC)) were acquired during maximum systole and diastole in 10 healthy volunteers (6 males, mean age 36?±?15 years). A pixel-wise bi-exponential fitting with an iterative nonparametric algorithm was carried out to calculate the following parameters: diffusion coefficient (D), fast diffusion coefficient (D*), and fraction of fast diffusion (f). Region of interest measurements were performed in both lateral ventricles. Comparison of IVIM parameters was performed among two cardiac cycle acquisitions and among the diffusion-encoding directions using a paired Student’s t test.

Results

f significantly (p?<?0.05) depended on the diffusion-encoding direction and on the cardiac cycle (diastole AP 0.30?±?0.13, ML 0.22?±?0.12, CC 0.26?±?0.17; systole AP 0.45?±?0.17, ML 0.34?±?0.15, CC 0.40?±?0.21). Neither a cardiac cycle nor a direction dependency was found among mean D values (which is in line with the expected intraventricular isotropic diffusion) and D* values (p?>?0.05 each).

Conclusion

The fraction of fast diffusion from IVIM is feasible to detect a direction-dependent and cardiac-dependent pulsatile CSF flow within the lateral ventricles allowing for quantitative monitoring of CSF dynamics. This technique might provide opportunities to further investigate the pathophysiology of various neurological disorders involving altered CSF dynamics.