Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer

Inter-visit and inter-reader reproducibility of multi-parametric diffusion-weighted MR imaging in longitudinally imaged patients with metabolic dysfunction-associated fatty liver disease and healthy volunteers

BACKGROUND

Despite the widespread use of diffusion-weighted imaging (DWI) in metabolic dysfunction-associated fatty liver disease (MAFLD), MRI acquisition and quantification techniques vary in the literature suggesting the need for established and reproducible protocols. The goal of this study was to assess inter-visit and inter-reader reproducibility of DWI- and IVIM-derived parameters in patients with MAFLD and healthy volunteers using extensive sampling of the "fast" compartment, non-rigid registration, and exclusion voxels with poor fit quality.

METHODS

From June 2019 to April 2023, 31 subjects (20 patients with biopsy-proven MAFLD and 11 healthy volunteers) were included in this IRB-approved study. Subjects underwent MRI examinations twice within 40 days. 3.0 T DWI was acquired using a respiratory-triggered spin-echo diffusion-weighted echo-planar imaging sequence (b-values of 0, 10, 20, 30, 40, 50, 100, 200, 400, 800 s/mm2). DWI series were co-registered prior to voxel-wise non-linear regression of the IVIM model and voxels with poor fit quality were excluded (normalized root mean squared error ≥ 0.05). IVIM parameters (perfusion fraction, f; diffusion coefficient, D; and pseudo-diffusion coefficient, D*), and apparent diffusion coefficients (ADC) were computed from manual segmentation of the right liver lobe performed by two analysts on two MRI examinations.

RESULTS

All results are reported for f, D, D*, and ADC respectively. For inter-reader agreement on the first visit, ICC were of 0.985, 0.994, 0.986, and 0.993 respectively. For intra-reader agreement of analyst 1 assessed on both imaging examinations, ICC between visits were of 0.805, 0.759, 0.511, and 0.850 respectively. For inter-reader agreement on the first visit, mean bias and 95 % limits of agreement were (0.00 ± 0.03), (-0.01 ± 0.03) × 10-3 mm2/s, (0.70 ± 10.40) × 10-3 mm2/s, and (-0.02 ± 0.04) × 10-3 mm2/s respectively. For intra-reader agreement of analyst 1, mean bias and 95 % limits of agreement were (0.01 ± 0.09) × 10-3 mm2/s, (-0.01 ± 0.21) × 10-3 mm2/s, (-13.37 ± 56.19) × 10-3 mm2/s, and (-0.01 ± 0.16) × 10-3 mm2/s respectively. Except for parameter D* that was associated with between-subjects parameter variability (P = 0.009), there was no significant variability between subjects, examinations, or readers.

CONCLUSION

With our approach, IVIM parameters f, D, D*, and ADC provided excellent inter-reader agreement and good to very good inter-visit or intra-reader agreement, thus showing the reproducibility of IVIM-DWI of the liver in MAFLD patients and volunteers.

Effect of Mediterranean diet or mindfulness-based stress reduction during pregnancy on placental volume and perfusion: A subanalysis of the IMPACT BCN randomized clinical trial

INTRODUCTION

The IMPACT BCN trial-a parallel-group randomized clinical trial where 1221 pregnant women at high risk for small-for-gestational age (SGA) newborns were randomly allocated at 19- to 23-week gestation into three groups: Mediterranean diet, Mindfulness-based Stress reduction or non-intervention-has demonstrated a positive effect of Mediterranean diet and Stress reduction in the prevention of SGA. However, the mechanism of action of these interventions remains still unclear. The aim of this study is to investigate the effect of Mediterranean diet and Stress reduction on placental volume and perfusion.

MATERIAL AND METHODS

Participants in the Mediterranean diet group received monthly individual and group educational sessions, and free provision of extra-virgin olive oil and walnuts. Women in the Stress reduction group underwent an 8-week Stress reduction program adapted for pregnancy, consisting of weekly 2.5-h and one full-day sessions. Non-intervention group was based on usual care. Placental volume and perfusion were assessed in a subgroup of randomly selected women (n = 165) using magnetic resonance (MR) at 36-week gestation. Small placental volume was defined as MR estimated volume <10th centile. Perfusion was assessed by intravoxel incoherent motion.

RESULTS

While mean MR placental volume was similar among the study groups, both interventions were associated with a lower prevalence of small placental volume (3.9% Mediterranean diet and 5% stress reduction vs. 17% non-intervention; p = 0.03 and p = 0.04, respectively). Logistic regression showed that small placental volume was significantly associated with higher risk of SGA in both study groups (OR 7.48 [1.99-28.09] in Mediterranean diet and 20.44 [5.13-81.4] in Stress reduction). Mediation analysis showed that the effect of Mediterranean diet on SGA can be decomposed by a direct effect and an indirect effect (56.6%) mediated by a small placental volume. Similarly, the effect of Stress reduction on SGA is partially mediated (45.3%) by a small placental volume. Results on placental intravoxel incoherent motion perfusion fraction and diffusion coefficient were similar among the study groups.

CONCLUSIONS

Structured interventions during pregnancy based on Mediterranean diet or Stress reduction are associated with a lower proportion of small placentas, which is consistent with the previously observed beneficial effects of these interventions on fetal growth.

Multisite MRI Intravoxel Incoherent Motion Repeatability and Reproducibility across 3 T Scanners in a Breast Diffusion Phantom: A BReast Intravoxel Incoherent Motion Multisite (BRIMM) Study

BACKGROUND

Monoexponential apparent diffusion coefficient (ADC) and biexponential intravoxel incoherent motion (IVIM) analysis of diffusion-weighted imaging is helpful in the characterization of breast tumors. However, repeatability/reproducibility studies across scanners and across sites are scarce.

PURPOSE

To evaluate the repeatability and reproducibility of ADC and IVIM parameters (tissue diffusivity (Dt), perfusion fraction (Fp) and pseudo-diffusion (Dp)) within and across sites employing MRI scanners from different vendors utilizing 16-channel breast array coils in a breast diffusion phantom.

STUDY TYPE

Phantom repeatability.

PHANTOM

A breast phantom containing tubes of different polyvinylpyrrolidone (PVP) concentrations, water, fat, and sponge flow chambers, together with an MR-compatible liquid crystal (LC) thermometer.

FIELD STRENGTH/SEQUENCE

Bipolar gradient twice-refocused spin echo sequence and monopolar gradient single spin echo sequence at 3 T.

ASSESSMENT

Studies were performed twice in each of two scanners, located at different sites, on each of 2 days, resulting in four studies per scanner. ADCs of the PVP and water were normalized to the vendor-provided calibrated values at the temperature indicated by the LC thermometer for repeatability/reproducibility comparisons.

STATISTICAL TESTS

ADC and IVIM repeatability and reproducibility within and across sites were estimated via the within-system coefficient of variation (wCV). Pearson correlation coefficient (r) was also computed between IVIM metrics and flow speed. A P value <0.05 was considered statistically significant.

RESULTS

ADC and Dt demonstrated excellent repeatability (<2%; <3%, respectively) and reproducibility (both <5%) at the two sites. Fp and Dp exhibited good repeatability (mean of two sites 3.67% and 5.59%, respectively) and moderate reproducibility (mean of two sites 15.96% and 13.3%, respectively). The mean intersite reproducibility (%) of Fp/Dp/Dt was 50.96/13.68/5.59, respectively. Fp and Dt demonstrated high correlations with flow speed while Dp showed lower correlations. Fp correlations with flow speed were significant at both sites.

DATA CONCLUSION

IVIM reproducibility results were promising and similar to ADC, particularly for Dt. The results were reproducible within both sites, and a progressive trend toward reproducibility across sites except for Fp.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Assessment of Hypoxia in Breast Cancer: Emerging Functional MR Imaging and Spectroscopy Techniques and Clinical Applications

Breast cancer is one of the most prevalent forms of cancer affecting women worldwide. Hypoxia, a condition characterized by insufficient oxygen supply in tumor tissues, is closely associated with tumor aggressiveness, resistance to therapy, and poor clinical outcomes. Accurate assessment of tumor hypoxia can guide treatment decisions, predict therapy response, and contribute to the development of targeted therapeutic interventions. Over the years, functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRS) techniques have emerged as promising noninvasive imaging options for evaluating hypoxia in cancer. Such techniques include blood oxygen level-dependent (BOLD) MRI, oxygen-enhanced MRI (OE) MRI, chemical exchange saturation transfer (CEST) MRI, and proton MRS (1H-MRS). These may help overcome the limitations of the routinely used dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) techniques, contributing to better diagnosis and understanding of the biological features of breast cancer. This review aims to provide a comprehensive overview of the emerging functional MRI and MRS techniques for assessing hypoxia in breast cancer, along with their evolving clinical applications. The integration of these techniques in clinical practice holds promising implications for breast cancer management. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

Diffusion-Weighted MRI of the Liver in Patients With Chronic Liver Disease: A Comparative Study Between Different Fitting Approaches and Diffusion Models

BACKGROUND

Diffusion-weighted imaging (DWI) has been considered for chronic liver disease (CLD) characterization. Grading of liver fibrosis is important for disease management.

PURPOSE

To investigate the relationship between DWI's parameters and CLD-related features (particularly regarding fibrosis assessment).

STUDY TYPE

Retrospective.

SUBJECTS

Eighty-five patients with CLD (age: 47.9 ± 15.5, 42.4% females).

FIELD STRENGTH/SEQUENCE

3-T, spin echo-echo planar imaging (SE-EPI) with 12 b-values (0-800 s/mm2 ).

ASSESSMENT

Several models statistical models, stretched exponential model, and intravoxel incoherent motion were simulated. The corresponding parameters (Ds , σ, DDC, α, f, D, D*) were estimated on simulation and in vivo data using the nonlinear least squares (NLS), segmented NLS, and Bayesian methods. The fitting accuracy was analyzed on simulated Rician noised DWI. In vivo, the parameters were averaged from five central slices entire liver to compare correlations with histological features (inflammation, fibrosis, and steatosis). Then, the differences between mild (F0-F2) or severe (F3-F6) groups were compared respecting to statistics and classification. A total of 75.3% of patients used to build various classifiers (stratified split strategy and 10-folders cross-validation) and the remaining for testing.

STATISTICAL TESTS

Mean squared error, mean average percentage error, spearman correlation, Mann-Whitney U-test, receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, precision. A P-value <0.05 was considered statistically significant.

RESULTS

In simulation, the Bayesian method provided the most accurate parameters. In vivo, the highest negative significant correlation (Ds , steatosis: r = -0.46, D*, fibrosis: r = -0.24) and significant differences (Ds , σ, D*, f) were observed for Bayesian fitted parameters. Fibrosis classification was performed with an AUC of 0.92 (0.91 sensitivity and 0.70 specificity) with the aforementioned diffusion parameters based on the decision tree method.

DATA CONCLUSION

These results indicate that Bayesian fitted parameters may provide a noninvasive evaluation of fibrosis with decision tree.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Imaging Biomarkers of Peripheral Nerves: Focus on Magnetic Resonance Neurography and Ultrasonography

Peripheral neuropathy is a prevalent and debilitating condition affecting millions of individuals globally. Magnetic resonance neurography (MRN) and ultrasonography (US) are noninvasive methods offering comprehensive visualization of peripheral nerves, using anatomical and functional imaging biomarkers to ensure accurate evaluation. For optimized MRN, superior and high-resolution two-dimensional and three-dimensional imaging protocols are essential. The anatomical MRN and US imaging markers include quantitative measures of nerve and fascicular size and signal, and qualitative markers of course and morphology. Among them, quantitative markers of T2-signal intensity ratio are sensitive to nerve edema-like signal changes, and the T1-mapping technique reveals nerve and muscle tissue fatty and fibrous compositional alterations.The functional markers are derived from physiologic properties of nerves, such as diffusion characteristics or blood flow. They include apparent diffusion coefficient from diffusion-weighted imaging and fractional anisotropy and tractography from diffusion tensor imaging to delve into peripheral nerve microstructure and integrity. Peripheral nerve perfusion using dynamic contrast-enhanced magnetic resonance imaging estimates perfusion parameters, offering insights into nerve health and neuropathies involving edema, inflammation, demyelination, and microvascular alterations in conditions like type 2 diabetes, linking nerve conduction pathophysiology to vascular permeability alterations.Imaging biomarkers thus play a pivotal role in the diagnosis, prognosis, and monitoring of nerve pathologies, thereby ensuring comprehensive assessment and elevating patient care. These biomarkers provide valuable insights into nerve structure, function, and pathophysiology, contributing to the accurate diagnosis and management planning for peripheral neuropathy.

Intravoxel Incoherent Motion Quantification Dependent on Measurement SNR and Tissue Perfusion: A Simulation Study

Background

The intravoxel incoherent motion (IVIM) model extracts both functional and structural information of a tissue using motion-sensitizing gradients.

Objective

The Objective of the present work is to investigate the impact of signal to noise ratio (SNR) and physiologic conditions on the validity of IVIM parameters.

Material and Methods

This study is a simulation study, modeling IVIM at a voxel, and also done 10,000 times for every single simulation. Complex noises with various standard deviations were added to signal in-silico to investigate SNR effects on output validity. Besides, some blood perfusion situations for different tissues were considered based on their physiological range to explore the impacts of blood fraction at each voxel on the validity of the IVIM outputs. Coefficient variation (CV) and bias of the estimations were computed to assess the validity of the IVIM parameters.

Results

This study has shown that the validity of IVIM output parameters highly depends on measurement SNR and physiologic characteristics of the studied organ.

Conclusion

IVIM imaging could be useful if imaging parameters are correctly selected for each specific organ, considering hardware limitations.

Intravoxel incoherent motion for differentiating residual/recurrent tumor from post-treatment change in patients with high-grade glioma

PURPOSE

To investigate the diagnostic value of f derived from IVIM technique and to correlate it with rCBV derived from DSC for the differentiation of residual/recurrent tumor from post-treatment change in patients with high-grade glioma.

MATERIALS AND METHODS

Patients who underwent MR imaging with IVIM and DSC studies for evaluation of high-grade glioma after standard treatment were enrolled in this retrospective study. For qualitative analysis, the f and rCBV maps were interpreted as hypoperfused or hyperperfused in each parameter. Quantitative analysis was performed using ROI analysis in f and rCBV parameters. The lesions were divided into residual/recurrent tumor and post-treatment change groups.

RESULTS

Nineteen patients with high-grade glioma were included. In qualitative analysis, the f-map shows higher sensitivity (100.0%) than rCBV map (92.3%), while the rCBV map shows higher specificity (100.0%) than the f-map (83.3%). In quantitative analysis, the optimal cutoff values of 1.19 for f and 1.06 for rCBV are shown to provide high diagnostic value with high sensitivity (91.7%) for both parameters but slightly higher specificity of rCBV (85.7%) than f (71.4%). The correlation between f and rCBV was good with ICC of 0.810.

CONCLUSION

The f value measured by IVIM technique, non-contrast perfusion technique, has high diagnostic performance and potential to be an alternative method to CBV measured by DSC for differentiation between residual/recurrent tumor and post-treatment change in patients with high-grade glioma.

Diagnostic accuracy of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI to differentiate benign from malignant breast lesions: A systematic review and meta-analysis

PURPOSE

Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions.

METHOD

We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis.

RESULTS

Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively.

CONCLUSIONS

Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.

Intravoxel incoherent motion diffusion-weighted MRI for predicting the efficacy of high-intensity focused ultrasound ablation for uterine fibroids

Purpose

To evaluate the significance of magnetic resonance (MR) intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters in predicting early efficacy of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids before treatment.

Method

64 patients with 89 uterine fibroids undergoing HIFU ablation (51 sufficient ablations and 38 insufficient ablations) were enrolled in the study and completed MR imaging and IVIM-DWI before treatment. The IVIM-DWI parameters, including D (diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction) and relative blood flow (rBF) were calculated. The logistic regression (LR) model was constructed to analyze the predictors of efficacy. The receiver operating characteristic (ROC) curve was drawn to assess the model's performance. A nomograph was constructed to visualize the model.

Results

The D value of the sufficient ablation group (931.0(851.5-987.4) × 10-6 mm2/s) was significantly lower than that of the insufficient ablation group (1052.7(1019.6-1158.7) × 10-6 mm2/s) (p<0.001). However, differences in D*, f, and rBF values between the groups were not significant (p>0.05). The LR model was constructed with D value, fibroid position, ventral skin distance, T2WI signal intensity, and contrast enhanced degree. The area under the ROC curve, specificity, and sensitivity of the model were 0.858 (95% confidence interval: 0.781, 0.935), 0.686, and 0.947. The nomogram and calibration curves confirmed that the model had excellent performance.

Conclusion

The IVIM-DWI quantitative parameters can be used to predict early effects of HIFU ablation on uterine fibroids. A high D value before treatment may indicate that the treatment will be less effective in the early stages.

Characterization of breast lesions using multi-parametric diffusion MRI and machine learning

Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andβand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofβ;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.

Synthetic-to-real domain adaptation with deep learning for fitting the intravoxel incoherent motion model of diffusion-weighted imaging

BACKGROUND

Intravoxel incoherent motion (IVIM) is a type of diffusion-weighted imaging (DWI), and IVIM model parameters (water molecule diffusion rate D_t , pseudo-diffusion coefficient D_p , and tissue perfusion fraction F_p ) have been widely used in the diagnosis and characterization of malignant lesions.

PURPOSE

This study proposes a deep-learning model with synthetic-to-real domain adaptation to fit the IVIM model parameters of DWI.

METHODS

Ninety-eight consecutive patients diagnosed with hepatocellular carcinoma between January 2017 and September 2020 were included in the study, and routine IVIM-DWI serial examinations were performed using a 3.0 T magnetic resonance imaging system in preoperative MR imaging. The proposed method is mainly composed of two modules: a convolutional neural network-based IVIM model fitting network to map b-value images to the IVIM parameter maps and a domain discriminator to improve the accuracy of the IVIM parameter maps in the real data. The proposed method was compared with previously reported fitting methods, including the nonlinear least squares (NLSs), IVIM-NET_optim , and self-supervised U-network methods. The IVIM parameter-fitting performance was assessed by measuring the DWI reconstruction performance and testing the robustness of each method against noise using noise-corrupted data.

RESULTS

The DWI reconstruction performance demonstrates that the proposed method has better reconstruction accuracy for DWI with a low signal-to-noise ratio, which implies that the proposed method improves the fitting accuracy of the IVIM parameters. Noise-corrupt experiments show that the proposed method is more robust against noise-corrupted signals. With the proposed method, no outliers were found in D_t , and outliers were reduced for F_p in the abnormal regions (proposed method: 1.85%; NLS: 5.90%; IVIM-NET_optim : 6.61%; and self-U-net: 25.36%). Moreover, experiments show that the proposed method has a more stable parameter estimation performance than the existing methods in the absence of real data.

CONCLUSIONS

IVIM parameters can be estimated using a synthetic-to-real domain-adaptation framework with deep learning, and the proposed method outperforms previously reported methods.

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

OBJECTIVE

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

METHODS

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

RESULT

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

CONCLUSION

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

Assessment of intravoxel incoherent motion MR imaging for differential diagnosis of breast lesions and evaluation of response: a systematic review

Background

The current study aimed to assess the performance for quantitative differentiation and evaluation of response in categorized observations from intravoxel incoherent motion analyses of patients based on breast tumors. To assess the presence of heterogeneity, the Cochran's Q tests for heterogeneity with a significance level of P < 0.1 and I2 statistic with values > 75% were used. A random-effects meta-analysis model was used to estimate pooled sensitivity and specificity. The standardized mean difference (SMD) and 95% confidence intervals of the true diffusivity (D), pseudo-diffusivity (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) were calculated, and publication bias was evaluated using the Begg's and Egger's tests and also funnel plot. Data were analyzed by STATA v 16 (StataCorp, College Station).

Results

The pooled D value demonstrated good measurement performance showed a sensitivity 86%, specificity 86%, and AUC 0.91 (SMD − 1.50, P < 0.001) in the differential diagnosis of breast lesions, which was comparable to that of the ADC that showed a sensitivity of 76%, specificity 79%, and AUC 0.85 (SMD 1.34, P = 0.01), then by the f it showed a sensitivity 80%, specificity 76%, and AUC 0.85 (SMD 0.89, P = 0.001), and D* showed a sensitivity 84%, specificity 59%, and AUC 0.71 (SMD − 0.30, P = 0.20).

Conclusion

The estimated sensitivity and specificity in the current meta-analysis were acceptable. So, this approach can be used as a suitable method in the differentiation and evaluation response of breast tumors.

Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions

Background

To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging.

Methods

1.5-T DWI data (b = 0, 50, 250, 800 s/mm²) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D₁′ = ADC (50, 800), D₂′ = ADC (250, 800), f₁′ = f (0, 50, 800), f₂′ = f (0, 250, 800) and D*′ = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D₁′ with f₁′ and D₂′ with f₂′ was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm² (n = 191) and (ii) with suspicious contrast-enhancement (n = 135).

Results

All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm². For task (i), best discrimination was reached for the combination of D₁′ and f₁′ using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D₁′ (88.0%) and f₁′ (87.4%). For task (ii), best discrimination was reached for single parameter D₁′ using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D₂′ (88.1%). Adding f₁′ to D₁′ did not improve discrimination.

Conclusions

IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.

Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI

Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.

Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer

Simple Summary

Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial, as pCR is a surrogate marker for survival. However, only 10–30% of patients achieve it. It is therefore essential to develop tools that enable to accurately predict response. Recently, different studies have demonstrated the feasibility of applying machine learning approaches to non-invasively predict pCR before NAC administration from magnetic resonance imaging (MRI) data. Some of those models are based on radiomics, an emerging field that allows the automated extraction of clinically relevant information from radiologic images. However, the research is still at an early stage and further data are needed. Here, we determine whether the combination of imaging data (radiomic features and perfusion/diffusion imaging biomarkers) extracted from multiparametric MRIs and clinical variables can improve pCR prediction to NAC compared to models only including imaging or clinical data, potentially avoiding unnecessary treatment and delays to surgery.

Abstract

Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular−extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases.

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T

PURPOSE

The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value.

MATERIALS AND METHODS

Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm² ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm² ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2_p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times.

RESULTS

Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2_p = 100 ms) and 60 % (T2_p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm² was around 8% lower than at b = 0 s/mm² (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2_p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01).

CONCLUSION

The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.

Intravoxel Incoherent Motion Magnetic Resonance Imaging Used in Preoperative Screening of High-Risk Patients With Moyamoya Disease Who May Develop Postoperative Cerebral Hyperperfusion Syndrome

Objective

This study aimed to investigate the feasibility of preoperative intravoxel incoherent motion (IVIM) MRI for the screening of high-risk patients with moyamoya disease (MMD) who may develop postoperative cerebral hyperperfusion syndrome (CHS).

Methods

This study composed of two parts. In the first part 24 MMD patients and 24 control volunteers were enrolled. IVIM-MRI was performed. The relative pseudo-diffusion coefficient, perfusion fraction, apparent diffusion coefficient, and diffusion coefficient (rD*, rf, rADC, and rD) values of the IVIM sequence were compared according to hemispheres between MMD patient and healthy control groups. In the second part, 98 adult patients (124 operated hemispheres) with MMD who underwent surgery were included. Preoperative IVIM-MRI was performed. The rD*, rf, rADC, rD, and rfD* values of the IVIM sequence were calculated and analyzed. Operated hemispheres were divided into CHS and non-CHS groups. Patients’ age, sex, Matsushima type, Suzuki stage, and IVIM-MRI examination results were compared between CHS and non-CHS groups.

Results

Only the rf value was significantly higher in the healthy control group than in the MMD group (P < 0.05). Out of 124 operated hemispheres, 27 were assigned to the CHS group. Patients with clinical presentation of Matsushima types I–V were more likely to develop CHS after surgery (P < 0.05). The rf values of the ipsilateral hemisphere were significantly higher in the CHS group than in the non-CHS group (P < 0.05). The rfD* values of the ACA and MCA supply areas of the ipsilateral hemisphere were significantly higher in the CHS group than in the non-CHS group (P < 0.05). Only the rf value of the anterior cerebral artery supply area in the contralateral hemisphere was higher in the CHS group than in the non-CHS group (P < 0.05). The rf values of the middle and posterior cerebral artery supply areas and the rD, rD*, and rADC values of the both hemispheres were not significantly different between the CHS and non-CHS groups (P > 0.05).

Conclusion

Preoperative non-invasive IVIM-MRI analysis, particularly the f-value of the ipsilateral hemisphere, may be helpful in predicting CHS in adult patients with MMD after surgery. MMD patients with ischemic onset symptoms are more likely to develop CHS after surgery.

Diagnostic Performance of Diffusion MRI for differentiating Benign and Malignant Nonfatty Musculoskeletal Soft Tissue Tumors: A Systematic Review and Meta-analysis

Objective: To evaluate the diagnostic performance of standard diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for differentiating benign and malignant soft tissue tumors (STTs).

Materials and methods: A thorough search was carried out to identify suitable studies published up to September 2020. The quality of the studies involved was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity (SEN), specificity (SPE), and summary receiver operating characteristic (SROC) curve were calculated using bivariate mixed effects models. A subgroup analysis was also performed to explore the heterogeneity.

Results: Eighteen studies investigating 1319 patients with musculoskeletal STTs (malignant, n=623; benign, n=696) were enrolled. Thirteen standard DWI studies using the apparent diffusion coefficient (ADC) showed that the pooled SEN and SPE of ADC were 0.80 (95% CI: 0.77-0.82) and 0.63 (95% CI: 0.60-0.67), respectively. The area under the curve (AUC) calculated from the SROC curve was 0.806. The subgroup analysis indicated that the percentage of myxoid malignant tumors, magnet strength, study design, and ROI placement were significant factors affecting heterogeneity. Four IVIM studies showed that the AUCs calculated from the SROC curves of the parameters ADC and D were 0.859 and 0.874, respectively. The AUCs for the IVIM parameters pseudo diffusion coefficient (D*) and perfusion fraction (f) calculated from the SROC curve were 0.736 and 0.573, respectively. Two DKI studies showed that the AUCs of the DKI parameter mean kurtosis (MK) were 0.97 and 0.89, respectively.

Conclusion: The DWI-derived ADC value and the IVIM DWI-derived D value might be accurate tools for discriminating musculoskeletal STTs, especially for non-myxoid SSTs, using more than two b values, with maximal b value ranging from 600 to 800 s/mm², additionally, a high-field strength (3.0 T) optimizes the diagnostic performance.

Characterization of the diffusion signal of breast tissues using multi-exponential models

PURPOSE

Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues.

METHODS

The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.

RESULTS

A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D_1,3 = 0, D_2,3 = 1.5 × 10^-3 , and D_3,3 = 10.8 × 10^-3 mm² /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent.

CONCLUSION

Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Triexponential Diffusion Analysis of Diffusion-weighted Imaging for Breast Ductal Carcinoma in Situ and Invasive Ductal Carcinoma

Purpose

To obtain detailed information in breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) using triexponential diffusion analysis.

Methods

Diffusion-weighted images (DWI) of the breast were obtained using single-shot diffusion echo-planar imaging with 15 b-values. Mean signal intensities at each b-value were measured in the DCIS and IDC lesions and fitted with the triexponential function based on a two-step approach: slow-restricted diffusion coefficient (D_s) was initially determined using a monoexponential function with b-values > 800 s/mm². The diffusion coefficient of free water at 37°C was assigned to the fast-free diffusion coefficient (D_f). Finally, the perfusion-related diffusion coefficient (D_p) was derived using all the b-values. Furthermore, biexponential analysis was performed to obtain the perfusion-related diffusion coefficient (D^*) and the perfusion-independent diffusion coefficient (D). Monoexponential analysis was performed to obtain the apparent diffusion coefficient (ADC). The sensitivity and specificity of the aforementioned diffusion coefficients for distinguishing between DCIS and IDC were evaluated using the pathological results.

Results

The D_s, D, and ADC of DCIS were significantly higher than those of IDC (P < 0.01 for all). There was no significant correlation between D_p and D_s, but there was a weak correlation between D^* and D. The combination of D_p and D_s showed higher sensitivity and specificity (85.9% and 71.4%, respectively), compared to the combination of D^* and D (81.5% and 33.3%, respectively).

Conclusion

Triexponential analysis can provide detailed diffusion information for breast tumors that can be used to differentiate between DCIS and IDC.

Associations between angiogenic factors and intravoxel incoherent motion-derived parameters in diffusion-weighted magnetic resonance imaging of breast cancer

Intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) can be used to estimate perfusion-related parameters, but these parameters may differ, based on the curve-fitting algorithm used for IVIM. Microvessel density (MVD) and vascular endothelial growth factor (VEGF) status are used as angiogenic factors in breast cancer. We aimed to investigate the relationship between MVD, VEGF, and intravoxel incoherent motion (IVIM)-derived parameters, obtained by 4 curve-fitting algorithms, in patients with invasive breast cancers.This retrospective study investigated IVIM-derived parameters, D (ie, tissue diffusivity), D∗ (ie, pseudodiffusivity), and f (ie, perfusion fraction), of 55 breast cancers, using 10 b values (range, 0-800 s/mm2) and 4 curve-fitting algorithms: algorithm 1, linear fitting of D and f first, followed by D∗; algorithm 2, linear fitting of D and f and nonlinear fitting of D∗; algorithm 3, linear fitting of D and f, linear fitting of D∗, and ignoring D contribution for low b values; and algorithm 4, full nonlinear fitting of D, f, and D∗. We evaluated whole-tumor histograms of D, f, and D∗ for their association with MVD and VEGF.D∗10, D∗25, D∗50, D∗mean, D∗75, D∗90, f10, and f25, derived using algorithm 3, were associated with VEGF expression (P = .043, P = 0.012, P = .019, P = .024, P = .044, P = .041, P = .010, and P = .005, respectively). However, no correlation existed between MVD and IVIM-derived parameters.Perfusion-related IVIM parameters obtained by curve-fitting algorithm 3 may reflect VEGF expression.

Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort

Diffusion-weighted MRI (DWI) is an important tool for oncology research, with great clinical potential for the classification and monitoring of breast lesions. The utility of parameters derived from DWI, however, is influenced by specific analysis choices. The purpose of this study was to critically evaluate repeatability and curve-fitting performance of common DWI signal representations, for a prospective cohort of patients with benign breast lesions. Twenty informed, consented patients with confirmed benign breast lesions underwent repeated DWI (3 T) using: sagittal single-shot spin-echo echo planar imaging, bipolar encoding, TR/TE: 11,600/86 ms, FOV: 180 x 180 mm, matrix: 90 x 90, slices: 60 x 2.5 mm, iPAT: GRAPPA 2, fat suppression, and 13 b-values: 0-700 s/mm² . A phase-reversed scan (b = 0 s/mm² ) was acquired for distortion correction. Voxel-wise repeat-measures coefficients of variation (CoVs) were derived for monoexponential (apparent diffusion coefficient [ADC]), biexponential (intravoxel incoherent motion: f, D, D*) and stretched exponential (α, DDC) across the parameter histograms for lesion regions of interest (ROIs). Goodness-of-fit for each representation was assessed by Bayesian information criterion. The volume of interest (VOI) definition was repeatable (CoV 13.9%). Within lesions, and across both visits and the cohort, there was no dominant best-fit model, with all representations giving the best fit for a fraction of the voxels. Diffusivity measures from the signal representations (ADC, D, DDC) all showed good repeatability (CoV < 10%), whereas parameters associated with pseudodiffusion (f, D*) performed poorly (CoV > 50%). The stretching exponent α was repeatable (CoV < 12%). This pattern of repeatability was consistent over the central part of the parameter percentiles. Assumptions often made in diffusion studies about analysis choices will influence the detectability of changes, potentially obscuring useful information. No single signal representation prevails within or across lesions, or across repeated visits; parameter robustness is therefore a critical consideration. Our results suggest that stretched exponential representation is more repeatable than biexponential, with pseudodiffusion parameters unlikely to provide clinically useful biomarkers.

Application of Diffusion Weighted Imaging Techniques for Differentiating Benign and Malignant Breast Lesions

To explore the value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusional kurtosis imaging (DKI) based on diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating benign and malignant breast lesions. A total of 215 patients with breast lesions were prospectively collected for breast MR examination. Single exponential, IVIM, and DKI models were calculated using a series of b values. Parameters including ADC, perfusion fraction (f), tissue diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), average kurtosis (MK), and average diffusivity (MD) were compared between benign and malignant lesions. ROC curves were used to analyze the optimal diagnostic threshold of each parameter, and to evaluate the diagnostic efficacy of single and combined parameters. ADC, D, MK, and MD values were significantly different between benign and malignant breast lesions (P<0.001). Among the single parameters, ADC had the highest diagnostic efficiency (sensitivity 91.45%, specificity 82.54%, accuracy 88.84%, AUC 0.915) and the best diagnostic threshold (0.983 μm²/ms). The combination of ADC and MK offered high diagnostic performance (sensitivity 90.79%, specificity 85.71%, accuracy 89.30%, AUC 0.923), but no statistically significant difference in diagnostic performance as compared with single-parameter ADC (P=0.268). The ADC, D, MK, and MD parameters have high diagnostic value in differentiating benign and malignant breast lesions, and of these individual parameters the ADC has the best diagnostic performance. Therefore, our study revealed that the use of ADC alone should be useful for differentiating between benign and malignant breast lesions, whereas the combination of MK and ADC might improve the diagnostic performance to some extent.

Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models

Background

To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables.

Methods

In this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed.

Results

Of the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change (ΔADC_200,1000) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment ΔADC_200,1000 improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001).

Conclusion

Mono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades

PURPOSE

The purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer.

METHODS

Fifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared.

RESULTS

D* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (-) group (D*:36.8 × 10-3 ± 5.3 × 10-3 mm2/s, f:29.5%, D*:29.8 × 10-3 ± 5.6 × 10-3 mm2/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (-) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease.

CONCLUSION

D* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

PURPOSE

Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.

EXPERIMENTAL DESIGN

Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.

RESULTS

Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).

CONCLUSIONS

The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Intravoxel incoherent motion magnetic resonance imaging: basic principles and clinical applications

The purpose of this article was to show basic principles, acquisition, advantages, disadvantages, and clinical applications of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI). IVIM MRI as a method was introduced in the late 1980s, but recently it started attracting more interest thanks to its applications in many fields, particularly in oncology and neuroradiology. This imaging technique has been developed with the objective of obtaining not only a functional analysis of different organs but also different types of lesions. Among many accessible tools in diagnostic imaging, IVIM MRI aroused the interest of many researchers in terms of studying its applicability in the evaluation of abdominal organs and diseases. The major conclusion of this article is that IVIM MRI seems to be a very auspicious method to investigate the human body, and that nowadays the most promising clinical application for IVIM perfusion MRI is oncology. However, due to lack of standardisation of image acquisition and analysis, further studies are needed to validate this method in clinical practice.

Multiparametric quantitative analysis of tumor perfusion and diffusion with 3T MRI: differentiation between benign and malignant soft tissue tumors

OBJECTIVE

To evaluate multiparametric MRI for differentiating benign and malignant soft tissue tumors.

METHODS

This retrospective study included 67 patients (mean age, 55 years; 18-82 years) with 35 benign and 32 malignant soft tissue tumors. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI)-derived parameters (D, D*, f), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE)-MRI parameters (K^trans, K_ep, V_e, iAUC) were calculated. Myxoid and non-myxoid soft tissue tumors were divided for subgroup analysis. The parameters were compared between benign and malignant tumors.

RESULTS

ADC and D were significantly lower in malignant than benign soft tissue tumors (1170 ± 488 vs 1472 ± 349 µm²/s; 1132 ± 500 vs 1415 ± 374 µm²/s; p < 0.05). K^trans, K_ep, V_e, and iAUC were significantly different between malignant and benign soft tissue tumors (0.209 ± 0.160 vs 0.092 ± 0.067 min^-1; 0.737 ± 0.488 vs 0.311 ± 0.230 min^-1; 0.32 ± 0.17 vs 0.44 ± 0.28; 0.23 ± 0.14 vs 0.12 ± 0.09, p < 0.05, respectively). ADC (0.752), D (0.742), and K_ep (0.817) had high AUCs. Subgroup analysis showed that only K^trans, and iAUC were significantly different in myxoid tumors, while, ADC, D, K^trans, K_ep, and iAUC were significantly different in non-myxoid tumor for differentiating benign and malignant tumors. D, K_ep, and iAUC were the most significant parameters predicting malignant soft tissue tumors.

CONCLUSION

Multiparametric MRI can be useful to differentiate benign and malignant soft tissue tumors using IVIM-DWI and DCE-MRI.

ADVANCES IN KNOWLEDGE

1. Pure tissue diffusion (D), transfer constant (K^trans), rate constant (K_ep), and initial area under time-signal intensity curve (iAUC) can be used to differentiate benign malignant soft tissue tumors.2. K^trans and iAUC enable differentiation of benign and malignant myxoid soft tissue tumors.

B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer

Objective

This study aimed to find the optimal number of b-values for intravoxel incoherent motion (IVIM) imaging analysis, using simulated and in vivo data from cervical cancer patients.

Materials and Methods

Simulated data were generated using literature pooled means, which served as reference values for simulations. In vivo data from 100 treatment-naïve cervical cancer patients with IVIM imaging (13 b-values, scan time, 436 seconds) were retrospectively reviewed. A stepwise b-value fitting algorithm calculated optimal thresholds. Feed forward selection determined the optimal subsampled b-value distribution for biexponential IVIM fitting, and simplified IVIM modeling using monoexponential fitting was attempted. IVIM parameters computed using all b-values served as reference values for in vivo data.

Results

In simulations, parameters were accurately estimated with six b-values, or three b-values for simplified IVIM, respectively. In vivo data showed that the optimal threshold was 40 s/mm² for patients with squamous cell carcinoma and a subsampled acquisition of six b-values (scan time, 198 seconds) estimated parameters were not significantly different from reference parameters (individual parameter error rates of less than 5%). In patients with adenocarcinoma, the optimal threshold was 100 s/mm², but an optimal subsample could not be identified. Irrespective of the histological subtype, only three b-values were needed for simplified IVIM, but these parameters did not retain their discriminative ability.

Conclusion

Subsampling of six b-values halved the IVIM scan time without significant losses in accuracy and discriminative ability. Simplified IVIM is possible with only three b-values, at the risk of losing diagnostic information.

Rapid golden-angle diffusion-weighted propeller MRI for simultaneous assessment of ADC and IVIM

Purpose:

Golden-angle single-shot PROPLLER (GA-SS-PROP) is proposed to accelerate the PROPELLER acquisition for distortion-free diffusion-weighted (DW) imaging. Acceleration is achieved by acquiring one-shot per b-value and several b-values can be acquired along a diffusion direction, where the DW signal follows a bi-exponential decay (i.e. IVIM). Sparse reconstruction is used to reconstruct full resolution DW images. Consequently, apparent diffusion coefficient (ADC) map and IVIM maps (i.e., perfusion fraction (f) and the perfusion-free diffusion coefficient (D)) are obtained simultaneously. The performance of GA-SS-PROP was demonstrated with simulation and human experiments.

Methods:

A realistic numerical phantom of high-quality diffusion images of the brain was developed. The error of the reconstructed DW images and quantitative maps were compared to the ground truth. The pulse sequence was developed to acquire human brain data. For comparison, fully sampled PROPELLER and conventional single-shot echo planar imaging (SS-EPI) acquisitions were performed.

Results:

GA-SS-PROP was 5 times faster than conventional PROPELLER acquisition with comparable image quality. The simulation demonstrated that sparse reconstruction is effective in restoring contrast and resolution. The human experiments demonstrated that GA-SS-PROP achieved superior image fidelity compared to SS-EPI for the same acquisition time and same in-plane resolution (1 × 1 mm²).

Conclusion:

GA-SS-PROP offers fast, high-resolution and distortion-free DW images. The generated quantitative maps (f, D and ADC) can provide valuable information on tissue perfusion and diffusion properties simultaneously, which are desirable in many applications, especially in oncology. As a turbo spin-echo based technique, it can be applied in most challenging regions where SS-EPI is problematic.

Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer

PURPOSE

The purpose of this study was to assess the clinical value of conventional magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) features for predicting the risk of axillary lymph node (ALN) metastasis in patients with breast cancer.

METHODS

This retrospective study involved 265 patients with breast cancer who underwent 3.0 T breast magnetic resonance imaging examinations prior to surgery and other treatment. Of these, 119 underwent IVIM examination. The features of MRI and IVIM and postoperative pathologic results were collected. The association of MRI features of breast cancer with ALN metastasis were determined by univariate and multivariate analyses. Comparison of IVIM parameters between breast cancer patients with and without ALN metastasis was performed using the Mann-Whitney U test.

RESULTS

Among the 265 patients, 144 (54.3%) had ALN metastasis, and 121 (45.7%) did not. The size and shape of the tumours, T2WI signal, inhomogeneous enhancement, washout intensity-time curves and the values of slow ADC, fast ADC and fraction of fast ADC parameters were significantly associated with ALN metastasis. The AUC of conventional MRI for diagnosing axillary lymph node metastasis was 0.722. The AUC of MRI combined with slow ADC, fast ADC and fraction of fast ADC parameters that were used to diagnose breast cancer with ALN metastasis were 0.814, 0.803 and 0.900, respectively.

CONCLUSIONS

The features of IVIM parameters and conventional MRI can be used to predict the ALN metastasis in patients with breast cancer. MRI combined with fraction of fast ADC showed higher diagnostic efficiency for ALN metastasis in breast cancer than MRI did.

Intravoxel incoherent motion magnetic resonance imaging for predicting the long-term efficacy of immune checkpoint inhibitors in patients with non-small-cell lung cancer

OBJECTIVES

Conventional evaluation of anti-tumor activity on the basis of tumor size is inadequate for immune checkpoint inhibitors (ICIs). We therefore aimed to assess the usefulness of intravoxel incoherent motion magnetic resonance imaging (IVIM-MRI) for evaluation of the therapeutic efficacy of ICIs.

MATERIALS AND METHODS

A chest IVIM-MRI was performed before and 2, 4, and 8 weeks after administration of ICIs in patients with advanced non-small-cell lung cancer. Apparent diffusion coefficient (ADC), skewness of ADC (ADC_skew), kurtosis of ADC (ADC_kurt), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were evaluated at each evaluation point and changes from the baseline (Δ).

RESULTS

Twenty patients were enrolled in this study. An increased ADC 8 weeks and decreased ADC_kurt and ΔADC_kurt 4 weeks after ICIs was associated with objective responses and longer progression-free survival (PFS). A decreased ΔADC_skew at 4 weeks was associated with objective responses, disease control, and longer PFS and overall survival. There was no correlation between the efficacy of ICIs and D, D* and f. All of three patients who had pseudoprogression had decreased ΔADC_skew at 4 weeks and two of them had decreased ΔADC_kurt at 4 weeks. Inversely, all five patients who had progressive disease (PD) did not have increased ΔADC_skew at 4 weeks and only one of them had decreased ΔADC_kurt at 4 weeks.

CONCLUSIONS

Changes in histograms of ADC may be useful for predicting long-term efficacy and distinguishing between pseudoprogression and actual PD after ICIs.

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion-weighted MRI

The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion coefficients of water molecules in biological tissues, which are used in cancer applications. The most reported fitting approach is a voxel-wise segmented non-linear least square, whereas Bayesian approaches with a direct fit, also considering spatial regularization, were proposed too. In this work a novel segmented Bayesian method was proposed, also in combination with a spatial regularization through a Conditional Autoregressive (CAR) prior specification. The two segmented Bayesian approaches, with and without CAR specification, were compared with two standard least-square and a direct Bayesian fitting methods. All approaches were tested on simulated images and real data of patients with head-and-neck and rectal cancer. Estimation accuracy and maps noisiness were quantified on simulated images, whereas the coefficient of variation and the goodness of fit were evaluated for real data. Both versions of the segmented Bayesian approach outperformed the standard methods on simulated images for pseudo-diffusion (D^∗ ) and perfusion fraction (f), whilst the segmented least-square fitting remained the less biased for the diffusion coefficient (D). On real data, Bayesian approaches provided the less noisy maps, and the two Bayesian methods without CAR generally estimated lower values for f and D^∗ coefficients with respect to the other approaches. The proposed segmented Bayesian approaches were superior, in terms of estimation accuracy and maps quality, to the direct Bayesian model and the least-square fittings. The CAR method improved the estimation accuracy, especially for D^∗ .

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

Background

Our study aims to reveal whether the low b-values distribution, high b-values upper limit, and the number of excitation (NEX) influence the accuracy of the intravoxel incoherent motion (IVIM) parameter derived from multi-b-value diffusion-weighted imaging (DWI) in the brain.

Methods

This prospective study was approved by the local Ethics Committee and informed consent was obtained from each participant. The five consecutive multi-b DWI with different b-value protocols (0–3500 s/mm²) were performed in 22 male healthy volunteers on a 3.0-T MRI system. The IVIM parameters from normal white matter (WM) and gray matter (GM) including slow diffusion coefficient (D), fast perfusion coefficient (D*) and perfusion fraction (f) were compared for differences among defined groups with different IVIM protocols by one-way ANOVA.

Results

The D* and f value of WM or GM in groups with less low b-values distribution (less than or equal to 5 b-values) were significantly lower than ones in any other group with more low b-values distribution (all P <  0.05), but no significant differences among groups with more low b-values distribution (P > 0.05). In addition, no significant differences in the D, D* and f value of WM or GM were found between group with one and more NEX of low b-values distribution (all P > 0.05). IVIM parameters in normal WM and GM strongly depended on the choice of the high b-value upper limit.

Conclusions

Metrics of IVIM parameters can be affected by low and high b value distribution. Eight low b-values distribution with high b-value upper limit of 800–1000 s/mm² may be the relatively proper set when performing brain IVIM studies.

Quantitative Non-Gaussian Intravoxel Incoherent Motion Diffusion-Weighted Imaging Metrics and Surgical Pathology for Stratifying Tumor Aggressiveness in Papillary Thyroid Carcinomas

We assessed a priori aggressive features using quantitative diffusion-weighted imaging metrics to preclude an active surveillance management approach in patients with papillary thyroid cancer (PTC) with tumor size 1-2 cm. This prospective study enrolled 24 patients with PTC who underwent pretreatment multi-b-value diffusion-weighted imaging on a GE 3 T magnetic resonance imaging scanner. The apparent diffusion coefficient (ADC) metric was calculated from monoexponential model, and the perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), and diffusion kurtosis coefficient (K) metrics were estimated using the non-Gaussian intravoxel incoherent motion model. Neck ultrasonography examination data were used to calculate tumor size. The receiver operating characteristic curve assessed the discriminative specificity, sensitivity, and accuracy between PTCs with and without features of tumor aggressiveness. Multivariate logistic regression analysis was performed on metrics using a leave-1-out cross-validation method. Tumor aggressiveness was defined by surgical histopathology. Tumors with aggressive features had significantly lower ADC and D values than tumors without tumor-aggressive features (P < .05). The absolute relative change was 46% in K metric value between the 2 tumor types. In total, 14 patients were in the critical size range (1-2 cm) measured by ultrasonography, and the ADC and D were significantly different and able to differentiate between the 2 tumor types (P < .05). ADC and D can distinguish tumors with aggressive histological features to preclude an active surveillance management approach in patients with PTC with tumors measuring 1-2 cm.

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Effect of intravoxel incoherent motion on diffusion parameters in normal brain

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm² versus at b = 250 and 1000 s/mm². The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ ​= ​0.35, p ​= ​3*10^-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10^-4/year and 3.61*10^-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.

Impact of hypertension on cerebral microvascular structure in CPAP-treated obstructive sleep apnoea patients: a diffusion magnetic resonance imaging study

PURPOSE

Obstructive sleep apnoea (OSA) is a highly prevalent sleep-related breathing disorder associated with hypertension, impaired peripheral vascular function and an increased risk of stroke. Evidence suggests that abnormalities of the cerebral microcirculation, such as capillary rarefication, may be present in these patients. We evaluated whether the presence of hypertension may affect the cerebral capillary architecture and function assessed by Intravoxel Incoherent Motion (IVIM) magnetic resonance imaging (MRI) in patients with continuous positive airway pressure (CPAP)-treated OSA.

METHODS

Forty-one patients (88% male, mean age 57 ± 10 years) with moderate-to-severe OSA were selected and divided into two groups (normotensive vs. hypertensive). All hypertensive OSA patients were adherent with their antihypertensive medication. Cerebral microvascular structure was assessed in grey (GM) and white matter (WM) using an echo-planar diffusion imaging sequence with 14 different b values. A step-wise IVIM analysis algorithm was applied to compute true diffusion (D), perfusion fraction (f) and pseudo-diffusion (D*) values. Group comparisons were performed with the Wilcoxon-Mann-Whitney-Test. Regression analysis was adjusted for age.

RESULTS

Diffusion- and perfusion-related indexes in middle-aged OSA normotensive patients were quantified in both tissue types (D [10^-3 mm²/s]: GM = 0.83 ± 0.03; WM = 0.72 ± 0.03; f (%) GM = 0.09 ± 0.01; WM = 0.06 ± 0.01; D* [10^-3 mm²/s]: GM = 7.72 ± 0.89; WM = 7.38 ± 0.98). In the examined tissue types, hypertension did not result in changes on the estimated MRI IVIM index values.

CONCLUSION

Based on IVIM analysis, cerebral microvascular structure and function showed no difference between hypertensive and normotensive patients with moderate-to-severe OSA treated with CPAP. Treatment adherence with antihypertensive drug regime and, in turn, controlled hypertension seems not to affect microvascular structure and perfusion of the brain.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02493673.

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Diagnostic evaluation of diffusion kurtosis imaging for prostate cancer: Detection in a biopsy population

PURPOSE

To prospectively assess the feasibility of diffusional kurtosis (DK) imaging for distinguishing prostate cancer(PCa) from benign prostate hyperplasia (BPH) in comparison with standard diffusion-weighted (DW) imaging, as well as low-from high-grade malignant regions.

MATERIALS AND METHODS

147 consecutive patients with suspected PCa underwent multi-parametric 1.5-TMR. Diffusion kurtosis imaging was acquired with with 5 b values (0,600,800,1600,and 2400sec/mm2).Region of interest (ROI)-based measurements were performed on ADC, D, and K map by two radiologists. Data were analyzed by using mixed-model analysis of variance and receiver operating characteristic curves. Correlations among the three parameters (ADC,D and K) in all patients, and correlations between three parameters with the tumor Gleason score (GS) in PCa group were analyzed using Pearson's correlation coefficient in peripheral zone(PZ) and transiton zone(TZ).

RESULTS

58 patients were proved with PCa (9 GS 3 + 3[PZ/TZ = 4/5], 49 GS ≥ 7 [PZ/TZ = 26/23]), and 89 patients were with BPH. ADC,D and K were able to distinguish benignance from tumor tissue both in PZ and TZ(P＜0.01), but performed poorly in neither differentiating low-(GS 3 + 3) from high-grade (GS≥3 + 4) disease, nor GS(3 + 4) from GS(4 + 3).There was a weak correlation between the GS and ADC, D (PZ:ADC r=-0.113, D r=-0.139; TZ:ADC r=-0.104,D r=-0.103), while a moderate correlation between the GS and K(PZ:K r = 0.492; TZ:K r = 0.433, P＜0.01).K had significantly greater area under the curve for differentiating PCa from BHP than ADC both in PZ and TZ.

CONCLUSION

DK model may add value in PCa detection and diagnosis, but none can differentiate low-from high-grade PCas (including GS=3+4 from GS=4+3).

Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques

Breast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent findings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). After automatically finding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types.

Evidence of the diffusion time dependence of intravoxel incoherent motion in the brain

PURPOSE

To investigate the diffusion time (T_D ) dependence of intravoxel incoherent motion (IVIM) signals in the brain.

METHODS

A 3-compartment IVIM model was proposed to characterize 2 types of microcirculatory flows in addition to tissue water in the brain: flows that cross multiple vascular segments (pseudo-diffusive) and flows that stay in 1 segment (ballistic) within T_D . The model was first evaluated using simulated flow signals. Experimentally, flow-compensated (FC) pulsed-gradient spin-echo (PGSE) and oscillating-gradient spin-echo (OGSE) sequences were tested using a flow phantom and then used to examine IVIM signals in the mouse brain with T_D ranging from ~2.5 ms to 40 ms on an 11.7T scanner.

RESULTS

By fitting the model to simulated flow signals, we demonstrated the T_D dependency of the estimated fraction of pseudo-diffusive flow and the pseudo-diffusion coefficient (D*), which were dictated by the characteristic timescale of microcirculatory flow (τ). Flow phantom experiments validated that the OGSE and FC-PGSE sequences were not susceptible to the change in flow velocity. In vivo mouse brain data showed that both the estimated fraction of pseudo-diffusive flow and D* increased significantly as T_D increased.

CONCLUSION

We demonstrated that IVIM signals measured in the brain are T_D -dependent, potentially because more microcirculatory flows approach the pseudo-diffusive limit as T_D increases with respect to τ. Measuring the T_D dependency of IVIM signals may provide additional information on microvascular flows in the brain.

Oxygen-sensitive MRI assessment of tumor response to hypoxic gas breathing challenge

Oxygen-sensitive MRI has been extensively used to investigate tumor oxygenation based on the response (R₂ * and/or R₁ ) to a gas breathing challenge. Most studies have reported response to hyperoxic gas indicating potential biomarkers of hypoxia. Few studies have examined hypoxic gas breathing and we have now evaluated acute dynamic changes in rat breast tumors. Rats bearing syngeneic subcutaneous (n = 15) or orthotopic (n = 7) 13762NF breast tumors were exposed to a 16% O₂ gas breathing challenge and monitored using blood oxygen level dependent (BOLD) R₂ * and tissue oxygen level dependent (TOLD) T₁ -weighted measurements at 4.7 T. As a control, we used a traditional hyperoxic gas breathing challenge with 100% O₂ on a subset of the subcutaneous tumor bearing rats (n = 6). Tumor subregions identified as responsive on the basis of R₂ * dynamics coincided with the viable tumor area as judged by subsequent H&E staining. As expected, R₂ * decreased and T₁ -weighted signal increased in response to 100% O₂ breathing challenge. Meanwhile, 16% O₂ breathing elicited an increase in R₂ *, but divergent response (increase or decrease) in T₁ -weighted signal. The T₁ -weighted signal increase may signify a dominating BOLD effect triggered by 16% O₂ in the relatively more hypoxic tumors, whereby the influence of increased paramagnetic deoxyhemoglobin outweighs decreased pO₂ . The results emphasize the importance of combined BOLD and TOLD measurements for the correct interpretation of tumor oxygenation properties.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.