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

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.

Threshold Isocontouring on High b-Value Diffusion-Weighted Images in Magnetic Resonance Mammography

OBJECTIVES

Motivated by the similar appearance of malignant breast lesions in high b-value diffusion-weighted imaging (DWI) and positron emission tomography, the purpose of this work was to evaluate the applicability of a threshold isocontouring approach commonly used in positron emission tomography to analyze DWI data acquired from female human breasts with minimal interobserver variability.

METHODS

Twenty-three female participants (59.4 ± 10.0 years) with 23 lesions initially classified as suggestive of cancers in x-ray mammography screening were subsequently imaged on a 1.5-T magnetic resonance imaging scanner. Diffusion-weighted imaging was performed prior to biopsy with b values of 0, 100, 750, and 1500 s/mm. Isocontouring with different threshold levels was performed on the highest b-value image to determine the voxels used for subsequent evaluation of diffusion metrics. The coefficient of variation was computed by specifying 4 different regions of interest drawn around the lesion. Additionally, a receiver operating statistical analysis was performed.

RESULTS

Using a relative threshold level greater than or equal to 0.85 almost completely suppresses the intra-individual and inter-individual variability. Among 4 studied diffusion metrics, the diffusion coefficients from the intravoxel incoherent motion model returned the highest area under curve value of 0.9. The optimal cut-off diffusivity was found to be 0.85 μm/ms with a sensitivity of 87.5% and specificity of 90.9%.

CONCLUSION

Threshold isocontouring on high b-value maps is a viable approach to reliably evaluate DWI data of suspicious focal lesions in magnetic resonance mammography.

Monoexponential, Biexponential, and stretched-exponential models using diffusion-weighted imaging: A quantitative differentiation of breast lesions at 3.0T

BACKGROUND

Diffusion-weighted imaging (DWI) plays an important role in the differentiation of malignant and benign breast lesions.

PURPOSE

To investigate the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched-exponential DWI models in the differential diagnosis of breast lesions.

STUDY TYPE

Prospective.

POPULATION

Sixty-one patients (age range: 25-68 years old; mean age: 46 years old) with 31 malignant lesions, 42 benign lesions, and 28 normal breast tissues diagnosed initially by clinical palpation, ultrasonography, or conventional mammography were enrolled in the study from January to September 2016.

FIELD STRENGTH

3.0T MR scanner, T₁ WI, T₂ WI, DWI (conventional and multi-b values), dynamic contrast-enhanced.

ASSESSMENT

The apparent diffusion coefficient (ADC) was calculated by monoexponential analysis. The diffusion coefficient (ADC_slow ), pseudodiffusion coefficient (ADC_fast ), and perfusion fraction (f) were calculated using the biexponential model. The distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) were obtained using a stretched-exponential model. All parameters were compared for malignant tumors, benign tumors, and normal breast tissues. A receiver operating characteristic curve was used to compare the ability of these parameters, in order to differentiate benign and malignant breast lesions.

STATISTICAL TESTS

All statistical analyses were performed using statistical software (SPSS).

RESULTS

ADC, ADC_slow , f, DDC, and α values were significantly lower in malignant tumors when compared with normal breast tissues and benign tumors (P < 0.05). However, ADC and f had higher area under the receiver operating characteristic curve (AUC) values (0.889 and 0.919, respectively).

DATA CONCLUSION

The parameters derived from the biexponential and stretched-exponential DWI could provide additional information for differentiating between benign and malignant breast tumors when compared with conventional diffusion parameters.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:1461-1467.

Diffusion-weighted imaging and diffusion tensor imaging as adjuncts to conventional MRI for the diagnosis and management of peripheral nerve sheath tumors: current perspectives and future directions

Peripheral nerve sheath tumors (PNSTs) account for ~ 5% of soft tissue neoplasms and are responsible for a wide spectrum of morbidities ranging from localized neuropathy to fulminant metastatic spread and death. MR imaging represents the gold standard for identification of these neoplasms, however, current anatomic MR imaging markers do not reliably detect or differentiate benign and malignant lesions, and therefore, biopsy or excision is required for definitive diagnosis. Diffusion-weighted MR imaging (DWI) serves as a useful tool in the evaluation and management of PNSTs by providing functional information regarding the degree of diffusion, while diffusion tensor imaging (DTI) aids in determining the directional information of predominant diffusion and has been shown to be particularly useful for pre-operative planning of these tumors by delineating healthy and pathologic fascicles. The article focuses on these important neurogenic lesions, highlighting the current utility of diffusion MR imaging and future directions including computerized radiomic analysis. KEY POINTS: • Anatomic MRI is moderately accurate in differentiating benign from malignant PNST. • Diffusion tensor imaging facilitates pre-operative planning of PNSTs by depicting neuropathy and tractography. • Radiomics will likely augment current observer-based diagnostic criteria for PNSTs.

Three-dimensional MR Fingerprinting for Quantitative Breast Imaging

Purpose To develop a fast three-dimensional method for simultaneous T1 and T2 quantification for breast imaging by using MR fingerprinting. Materials and Methods In this prospective study, variable flip angles and magnetization preparation modules were applied to acquire MR fingerprinting data for each partition of a three-dimensional data set. A fast postprocessing method was implemented by using singular value decomposition. The proposed technique was first validated in phantoms and then applied to 15 healthy female participants (mean age, 24.2 years ± 5.1 [standard deviation]; range, 18-35 years) and 14 female participants with breast cancer (mean age, 55.4 years ± 8.8; range, 39-66 years) between March 2016 and April 2018. The sensitivity of the method to B₁ field inhomogeneity was also evaluated by using the Bloch-Siegert method. Results Phantom results showed that accurate and volumetric T1 and T2 quantification was achieved by using the proposed technique. The acquisition time for three-dimensional quantitative maps with a spatial resolution of 1.6 × 1.6 × 3 mm³ was approximately 6 minutes. For healthy participants, averaged T1 and T2 relaxation times for fibroglandular tissues at 3.0 T were 1256 msec ± 171 and 46 msec ± 7, respectively. Compared with normal breast tissues, higher T2 relaxation time (68 msec ± 13) was observed in invasive ductal carcinoma (P < .001), whereas no statistical difference was found in T1 relaxation time (1183 msec ± 256; P = .37). Conclusion A method was developed for breast imaging by using the MR fingerprinting technique, which allows simultaneous and volumetric quantification of T1 and T2 relaxation times for breast tissues. © RSNA, 2018 Online supplemental material is available for this article.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification

The objective of the present study was to compare the differentiation between breast cancer and benign breast lesions and study regional distribution characteristics in various subtypes of breast cancer using intravoxel incoherent motion (IVIM) parameters. This retrospective study involved 119 patients with breast cancer and 22 patients with benign breast lesions, who underwent 3.0T breast magnetic resonance imaging examinations. The apparent diffusion coefficient (ADC) and IVIM parameters (slow ADC, fast ADC and fraction of fast ADC) were obtained from patients with breast cancer and benign lesions using diffusion-weighted imaging (DWI) with b-values of 0, 50, 100, 150, 200, 400, 500, 1,000 and 1,500 sec/mm². Compared with patients with benign breast lesions, patients with breast cancer exhibited decreased ADC (P<0.001), slow ADC (P<0.001) and fast ADC (P<0.001) values, and higher fraction of fast ADC (P<0.001) values. Tumors with metastatic axillary lymph nodes demonstrated increased fraction of fast ADC values (P<0.001) and decreased slow ADC values (P<0.001) compared with tumors without metastatic axillary lymph nodes. The Fast ADC values of tumor tissues in estrogen receptor (ER) and progesterone receptor (PR) negative groups were higher than in positive groups (P<0.001), and the slow ADC values of tumor tissues were lower in ER and PR negative groups than positive groups (P<0.001). Luminal B (HER2- negative) tumor (P<0.001) and peritumor (P<0.001) tissues exhibited decreased fraction of fast ADC values, in comparison with other subtypes. Triple-negative breast cancer (TNBC) tumor tissue exhibited increased fast ADC (P<0.001) and fraction of fast ADC values (P<0.001), and decreased slow ADC values (P<0.001), when compared with other subtypes. The TNBC tumor edge tissues had increased fraction of fast ADC values compared with other subtypes (P<0.01) and TNBC tumor tissues (P<0.05). Therefore, the IVIM parameters of tumor, tumor edge and peritumor tissues in various subtypes of breast cancer may be useful for differentiation of breast cancer subtypes and to assess the invasive extent of the tumors.

Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis

BACKGROUND

As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide perfusion parameters, IVIM-derived perfusion parameters might be expected to correlate with the kinetic features from DCE-MRI.

PURPOSE

To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer-aided diagnosis (CAD).

STUDY TYPE

Retrospective.

POPULATION

Eighty-five patients (invasive cancers; mean size, 1.8 cm; range, 0.8-4.8 cm) who underwent diffusion-weighted imaging with 12 b-values (0-1000 s/mm² ).

FIELD STRENGTH/SEQUENCE

3.0T MRI axial, IVIM-DWI epi-sequence, and DCE-MRI.

ASSESSMENT

Two radiologists measured the apparent diffusion coefficient (ADC), diffusion coefficient, pseudodiffusion coefficient, and perfusion fraction (f) using IVIM modeling. Kinetic features such as peak enhancement and early and delayed enhancement profiles were acquired using CAD.

STATISTICAL TESTS

The correlation between the IVIM parameters and kinetic features and the association between the IVIM parameters and prognostic factors were investigated using Mann-Whitney test and Spearman correlation test.

RESULTS

There were no significant associations between IVIM parameters and prognostic factors. When IVIM parameters were correlated with kinetic features by CAD, both the ADC and f values showed correlations with delayed enhancement profiles. The ADC values were lower in tumors with lower persistent components (P = 0.013) and higher washout components (P = 0.045) and showed a positive correlation with persistent proportion (Spearman's rho (r) = 0.222, P = 0.041). The f value was higher in tumors with higher persistent components (P = 0.021) and showed a positive correlation with persistent proportion (r = 0.227, P = 0.029).

DATA CONCLUSION

This analysis revealed that IVIM-derived ADC and f values showed correlations with kinetic features at the delayed phase as assessed by CAD. These results indicate the potential of IVIM imaging biomarkers to provide information on the biological and kinetic properties of breast cancers without a contrast agent.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:118-130.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification

The objective of the present study was to compare the differentiation between breast cancer and benign breast lesions and study regional distribution characteristics in various subtypes of breast cancer using intravoxel incoherent motion (IVIM) parameters. This retrospective study involved 119 patients with breast cancer and 22 patients with benign breast lesions, who underwent 3.0T breast magnetic resonance imaging examinations. The apparent diffusion coefficient (ADC) and IVIM parameters (slow ADC, fast ADC and fraction of fast ADC) were obtained from patients with breast cancer and benign lesions using diffusion-weighted imaging (DWI) with b-values of 0, 50, 100, 150, 200, 400, 500, 1,000 and 1,500 sec/mm². Compared with patients with benign breast lesions, patients with breast cancer exhibited decreased ADC (P<0.001), slow ADC (P<0.001) and fast ADC (P<0.001) values, and higher fraction of fast ADC (P<0.001) values. Tumors with metastatic axillary lymph nodes demonstrated increased fraction of fast ADC values (P<0.001) and decreased slow ADC values (P<0.001) compared with tumors without metastatic axillary lymph nodes. The Fast ADC values of tumor tissues in estrogen receptor (ER) and progesterone receptor (PR) negative groups were higher than in positive groups (P<0.001), and the slow ADC values of tumor tissues were lower in ER and PR negative groups than positive groups (P<0.001). Luminal B (HER2- negative) tumor (P<0.001) and peritumor (P<0.001) tissues exhibited decreased fraction of fast ADC values, in comparison with other subtypes. Triple-negative breast cancer (TNBC) tumor tissue exhibited increased fast ADC (P<0.001) and fraction of fast ADC values (P<0.001), and decreased slow ADC values (P<0.001), when compared with other subtypes. The TNBC tumor edge tissues had increased fraction of fast ADC values compared with other subtypes (P<0.01) and TNBC tumor tissues (P<0.05). Therefore, the IVIM parameters of tumor, tumor edge and peritumor tissues in various subtypes of breast cancer may be useful for differentiation of breast cancer subtypes and to assess the invasive extent of the tumors.

Quantitative evaluation of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for differential diagnosis and grading prediction of benign and malignant breast lesions

BACKGROUND

That breast carcinoma is the most common malignant lesion in women. This study aimed to differentiate benign from malignant breast lesions and to predict grading of the latter by comparing the diagnostic value of different parameters in intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

MATERIALS AND METHODS

Retrospective analysis was performed utilizing imaging and pathological data from 112 patients with 124 breast lesions that underwent IVIM-DWI examination with 3.0 T MRI. Out of 124, 47 benign and 77 malignant lesions were confirmed by pathological diagnosis. The diagnostic performance of f, D, and D* value to distinguish benign from malignant breast lesions, was evaluated using pathological results as the gold standard. Correlation between D value and Ki-67 index was evaluated to predict grading of malignant breast lesions.

RESULTS

The D value (0.99 ± 0.21) of patients with malignant lesions was significantly lower than that (1.34 ± 0.18) of patients harboring benign lesions (P = .00). The D* value (7.60 ± 2.10) in malignant lesion group was higher than that (6.83 ± 2.13) of the benign lesion group (P = .113). The f value (8.50 ± 2.13) in malignant lesion group was remarkably higher than that (7.68 ± 1.98) of benign lesion group (P = .035). For differential diagnosis of benign from malignant breast lesions, optimal diagnostic threshold of D value and f value were 1.21 and 7.86, respectively. The areas of D and f values under the ROC curve were 0.883 and 0.601, respectively. The sensitivity, specificity, and accuracy of D value were 83.0%, 86.7%, and 85.5%, respectively. Accordingly, those indexes of f value were 64.9%, 57.4%, and 62.1%, respectively. Furthermore, the Ki-67 staining index of malignant lesions was robustly negatively correlated with D value (r = -0.395, P < .01).

CONCLUSION

Concrete parameters of IVIM-DWI can help to improve the specificity and accuracy in differential diagnosis of breast benign and malignant lesions. D value is most relevant and valuable in predicting the grading of malignant breast lesions.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging of Primary Rectal Carcinoma: Correlation with Histopathology

BACKGROUND Comprehensive and precise assessment of rectal carcinoma is crucial before surgery to plan an individual treatment strategy. New functional techniques, such as intravoxel incoherent motion (IVIM), have emerged and could lead to more detailed information. The aim of this study was to evaluate the difference between the rectal tumor parenchyma and normal wall by IVIM and to explore the correlations of IVIM parameters and histopathology. MATERIAL AND METHODS We prospectively enrolled 128 patients with pathologically proven rectal non-mucinous carcinoma with differentiation degree and 16 patients with mucinous carcinoma. All patients underwent routine MR examination and IVIM sequence. The IVIM maps were automatically generated and 3 ROIs were drawn on the maximal rectal tumor parenchyma and normal rectal wall. The Wilcoxon signed rank test, t test, Mann-Whitney U test, and Spearman's rank correlation test were performed. RESULTS All IVIM parameters demonstrated the difference between rectal tumor parenchyma and normal wall (PD<0.001; PD*=0.014; Pf<0.001). Poorly differentiated carcinoma had a significantly lower f value (Pf=0.049) than well/moderately-differentiated carcinoma. In addition, mucinous carcinoma had a higher D (PD=0.001) and a lower D* value (PD*=0.001) than non-mucinous carcinoma. Correlation analysis between IVIM parameters and histopathology showed that D (|r|=0.538, PD=0.000) and D* (|r|=0.267, PD*=0.001) had statistically significant correlations with histological type and f (|r|=0.175, Pf=0.048) was significantly correlated with differentiation degree. CONCLUSIONS The IVIM parameters of rectal tumor parenchyma and normal wall were significantly different. D appears to be a valid and promising parameter to indicate histological features of rectal carcinoma.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging of Primary Rectal Carcinoma: Correlation with Histopathology

Background

Comprehensive and precise assessment of rectal carcinoma is crucial before surgery to plan an individual treatment strategy. New functional techniques, such as intravoxel incoherent motion (IVIM), have emerged and could lead to more detailed information. The aim of this study was to evaluate the difference between the rectal tumor parenchyma and normal wall by IVIM and to explore the correlations of IVIM parameters and histopathology.

Material/Methods

We prospectively enrolled 128 patients with pathologically proven rectal non-mucinous carcinoma with differentiation degree and 16 patients with mucinous carcinoma. All patients underwent routine MR examination and IVIM sequence. The IVIM maps were automatically generated and 3 ROIs were drawn on the maximal rectal tumor parenchyma and normal rectal wall. The Wilcoxon signed rank test, t test, Mann-Whitney U test, and Spearman’s rank correlation test were performed.

Results

All IVIM parameters demonstrated the difference between rectal tumor parenchyma and normal wall (P_D<0.001; P_D*=0.014; P_f<0.001). Poorly differentiated carcinoma had a significantly lower f value (P_f=0.049) than well/moderately-differentiated carcinoma. In addition, mucinous carcinoma had a higher D (P_D=0.001) and a lower D* value (P_D*=0.001) than non-mucinous carcinoma. Correlation analysis between IVIM parameters and histopathology showed that D (|r|=0.538, P_D=0.000) and D* (|r|=0.267, P_D*=0.001) had statistically significant correlations with histological type and f (|r|=0.175, P_f=0.048) was significantly correlated with differentiation degree.

Conclusions

The IVIM parameters of rectal tumor parenchyma and normal wall were significantly different. D appears to be a valid and promising parameter to indicate histological features of rectal carcinoma.

Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging

PURPOSE

An emerging topic in diffusion magnetic resonance is imaging blood microcirculation alongside water diffusion using the intravoxel incoherent motion (IVIM) model. Recently, a combined IVIM diffusion tensor imaging (IVIM-DTI) model was proposed, which accounts for both anisotropic pseudo-diffusion due to blood microcirculation and anisotropic diffusion due to tissue microstructures. In this article, we propose a robust IVIM-DTI approach for simultaneous diffusion and pseudo-diffusion tensor imaging.

METHODS

Conventional IVIM estimation methods can be broadly divided into two-step (diffusion and pseudo-diffusion estimated separately) and one-step (diffusion and pseudo-diffusion estimated simultaneously) methods. Here, both methods were applied on the IVIM-DTI model. An improved one-step method based on damped Gauss-Newton algorithm and a Gaussian prior for the model parameters was also introduced. The sensitivities of these methods to different parameter initializations were tested with realistic in silico simulations and experimental in vivo data.

RESULTS

The one-step damped Gauss-Newton method with a Gaussian prior was less sensitive to noise and the choice of initial parameters and delivered more accurate estimates of IVIM-DTI parameters compared to the other methods.

CONCLUSION

One-step estimation using damped Gauss-Newton and a Gaussian prior is a robust method for simultaneous diffusion and pseudo-diffusion tensor imaging using IVIM-DTI model. Magn Reson Med 79:2367-2378, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Variability of non-Gaussian diffusion MRI and intravoxel incoherent motion (IVIM) measurements in the breast

We prospectively examined the variability of non-Gaussian diffusion magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) measurements with different numbers of b-values and excitations in normal breast tissue and breast lesions. Thirteen volunteers and fourteen patients with breast lesions (seven malignant, eight benign; one patient had bilateral lesions) were recruited in this prospective study (approved by the Internal Review Board). Diffusion-weighted MRI was performed with 16 b-values (0–2500 s/mm² with one number of excitations [NEX]) and five b-values (0–2500 s/mm², 3 NEX), using a 3T breast MRI. Intravoxel incoherent motion (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm² [ADC₀] and kurtosis [K]) parameters were estimated from IVIM and Kurtosis models using 16 b-values, and synthetic apparent diffusion coefficient (sADC) values were obtained from two key b-values. The variabilities between and within subjects and between different diffusion acquisition methods were estimated. There were no statistical differences in ADC₀, K, or sADC values between the different b-values or NEX. A good agreement of diffusion parameters was observed between 16 b-values (one NEX), five b-values (one NEX), and five b-values (three NEX) in normal breast tissue or breast lesions. Insufficient agreement was observed for IVIM parameters. There were no statistical differences in the non-Gaussian diffusion MRI estimated values obtained from a different number of b-values or excitations in normal breast tissue or breast lesions. These data suggest that a limited MRI protocol using a few b-values might be relevant in a clinical setting for the estimation of non-Gaussian diffusion MRI parameters in normal breast tissue and breast lesions.

Differentiation between phyllodes tumours and fibroadenomas using intravoxel incoherent motion magnetic resonance imaging: comparison with conventional diffusion-weighted imaging

OBJECTIVE

To investigate whether the parameters derived from intravoxel incoherent motion (IVIM) MRI could differentiate phyllodes tumours (PTs) from fibroadenomas (FAs) by comparing the apparent diffusion coefficient (ADC) values.

METHODS

This retrospective study included 7 FAs, 10 benign PTs (BPTs), 4 borderline PTs, and one malignant PT. Biexponential analyses of IVIM were performed using a 3 T MRI scanner. Quantitative IVIM parameters [pure diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and fraction (f)] were calculated. The ADC was also calculated using monoexponential fitting.

RESULTS

The D and ADC values showed an increasing tendency in the order of FA, BPT, and borderline or malignant PT (BMPT). No significant difference was found in the D value among the three groups. The ADC value of the BMPT group was significantly higher than that of the FA group (p = 0.048). The D* value showed an increasing tendency in the order of BMPT, BPT, and FA, and the D* value of the BMPT group was significantly lower than that of the FA group (p = 0.048).

CONCLUSION

The D* derived from IVIM and the ADC were helpful for differentiating between FA and BMPT. Advances in knowledge: IVIM MRI examination showed that the perfusion-related diffusion coefficient is lower in borderline and malignant PTs than in FAs and the opposite is true for the ADC.

What can we see with IVIM MRI?

Intravoxel Incoherent Motion (IVIM) refers to translational movements which within a given voxel and during the measurement time present a distribution of speeds in orientation and/or amplitude. The IVIM concept has been used to estimate perfusion in tissues as blood flow in randomly oriented capillaries mimics a pseudo-diffusion process. IVIM-based perfusion MRI, which does not require contrast agents, has gained momentum recently, especially in the field oncology. In this introductory review the basic concepts, models, technical requirements and limitations inherent to IVIM-based perfusion MRI are outlined, as well as new, non-perfusion applications of IVIM MRI, such as virtual MR Elastography.

Differentiation Between Luminal-A and Luminal-B Breast Cancer Using Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

The study aimed to investigate whether intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can differentiate luminal-B from luminal-A breast cancer MATERIALS AND METHODS: Biexponential analyses of IVIM and DCE MRI were performed using a 3.0-T MRI scanner, involving 134 patients with 137 pathologically confirmed luminal-type invasive breast cancers. Luminal-type breast cancer was categorized as luminal-B breast cancer (LBBC, Ki-67 ≧ 14%) or luminal-A breast cancer (LABC, Ki-67 < 14%). Quantitative parameters from IVIM (pure diffusion coefficient [D], perfusion-related diffusion coefficient [D*], and fraction [f]) and DCE MRI (initial percentage of enhancement and signal enhancement ratio [SER]) were calculated. The apparent diffusion coefficient (ADC) was also calculated using monoexponential fitting. We correlated these data with the Ki-67 status.

RESULTS

The D and ADC values of LBBC were significantly lower than those of LABC (P = 0.028, P = 0.037). The SER of LBBC was significantly higher than that of LABC (P = 0.004). A univariate analysis showed that a significantly lower D (<0.847 x 10^-3 mm²/s), lower ADC (<0.960 × 10^-3 mm²/s), and higher SER (>1.071) values were associated with LBBC (all P values <0.01), compared to LABC. In a multivariate analysis, a higher SER (>1.071; odds ratio: 3.0099, 95% confidence interval: 1.4246-6.3593; P = 0.003) value and a lower D (<0.847 × 10^-3 mm²/s; odds ratio: 2.6878, 95% confidence interval: 1.0445-6.9162; P = 0.040) value were significantly associated with LBBC, compared to LABC.

CONCLUSION

The SER derived from DCE MRI and the D derived from IVIM are associated independently with the Ki-67 status in patients with luminal-type breast cancer.

Relative enhanced diffusivity: noise sensitivity, protocol optimization, and the relation to intravoxel incoherent motion

OBJECTIVE

To explore the relationship between relative enhanced diffusivity (RED) and intravoxel incoherent motion (IVIM), as well as the impact of noise and the choice of intermediate diffusion weighting (b value) on the RED parameter.

MATERIALS AND METHODS

A mathematical derivation was performed to cast RED in terms of the IVIM parameters. Noise analysis and b value optimization was conducted by using Monte Carlo calculations to generate diffusion-weighted imaging data appropriate to breast and liver tissue at three different signal-to-noise ratios.

RESULTS

RED was shown to be approximately linearly proportional to the IVIM parameter f, inversely proportional to D and to follow an inverse exponential decay with respect to D*. The choice of intermediate b value was shown to be important in minimizing the impact of noise on RED and in maximizing its discriminatory power. RED was shown to be essentially a reparameterization of the IVIM estimates for f and D obtained with three b values.

CONCLUSION

RED imaging in the breast and liver should be performed with intermediate b values of 100 and 50 s/mm², respectively. Future clinical studies involving RED should also estimate the IVIM parameters f and D using three b values for comparison.

Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions

Purpose To investigate the performance of integrated approaches that combined intravoxel incoherent motion (IVIM) and non-Gaussian diffusion parameters compared with the Breast Imaging and Reporting Data System (BI-RADS) to establish multiparameter thresholds scores or probabilities by using Bayesian analysis to distinguish malignant from benign breast lesions and their correlation with molecular prognostic factors. Materials and Methods Between May 2013 and March 2015, 411 patients were prospectively enrolled and 199 patients (allocated to training [n = 99] and validation [n = 100] sets) were included in this study. IVIM parameters (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm² [ADC₀] and kurtosis [K]) by using IVIM and kurtosis models were estimated from diffusion-weighted image series (16 b values up to 2500 sec/mm²), as well as a synthetic ADC (sADC) calculated by using b values of 200 and 1500 (sADC_200-1500) and a standard ADC calculated by using b values of 0 and 800 sec/mm² (ADC_0-800). The performance of two diagnostic approaches (combined parameter thresholds and Bayesian analysis) combining IVIM and diffusion parameters was evaluated and compared with BI-RADS performance. The Mann-Whitney U test and a nonparametric multiple comparison test were used to compare their performance to determine benignity or malignancy and as molecular prognostic biomarkers and subtypes of breast cancer. Results Significant differences were found between malignant and benign breast lesions for IVIM and non-Gaussian diffusion parameters (ADC₀, K, fIVIM, fIVIM · D*, sADC_200-1500, and ADC_0-800; P < .05). Sensitivity and specificity for the validation set by radiologists A and B were as follows: sensitivity, 94.7% and 89.5%, and specificity, 75.0% and 79.2% for sADC_200-1500, respectively; sensitivity, 94.7% and 96.1%, and specificity, 75.0% and 66.7%, for the combined thresholds approach, respectively; sensitivity, 92.1% and 92.1%, and specificity, 83.3% and 66.7%, for Bayesian analysis, respectively; and sensitivity and specificity, 100% and 79.2%, for BI-RADS, respectively. The significant difference in values of sADC_200-1500 in progesterone receptor status (P = .002) was noted. sADC_200-1500 was significantly different between histologic subtypes (P = .006). Conclusion Approaches that combined various IVIM and non-Gaussian diffusion MR imaging parameters may provide BI-RADS-equivalent scores almost comparable to BI-RADS categories without the use of contrast agents. Non-Gaussian diffusion parameters also differed by biologic prognostic factors. ^© RSNA, 2017 Online supplemental material is available for this article.

Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging

OBJECTIVE

This study aimed to investigate the reliability of intravoxel incoherent motion (IVIM) model derived parameters D and f and their dependence on b value distributions with a rapid three b value acquisition protocol.

MATERIALS AND METHODS

Diffusion models for brain, kidney, and liver were assessed for bias, error, and reproducibility for the estimated IVIM parameters using b values 0 and 1000, and a b value between 200 and 900, at signal-to-noise ratios (SNR) 40, 55, and 80. Relative errors were used to estimate optimal b value distributions for each tissue scenario. Sixteen volunteers underwent brain DW-MRI, for which bias and coefficient of variation were determined in the grey matter.

RESULTS

Bias had a large influence in the estimation of D and f for the low-perfused brain model, particularly at lower b values, with the same trends being confirmed by in vivo imaging. Significant differences were demonstrated in vivo for estimation of D (P = 0.029) and f (P < 0.001) with [300,1000] and [500,1000] distributions. The effect of bias was considerably lower for the high-perfused models. The optimal b value distributions were estimated to be brain_500,1000, kidney_300,1000, and liver_200,1000.

CONCLUSION

IVIM parameters can be estimated using a rapid DW-MRI protocol, where the optimal b value distribution depends on tissue characteristics and compromise between bias and variability.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study

BACKGROUND

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning.

PURPOSE

To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM).

STUDY TYPE

Prospective.

SUBJECTS

Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+).

FIELD STRENGTH/SEQUENCE

Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values.

ASSESSMENT

Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping.

STATISTICAL TESTS

Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons.

RESULTS

For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity.

DATA CONCLUSION

Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216.

Initial experience of correlating diffusion spectral parameters with histopathologic indexes in murine colorectal tumor homografts

Purpose

To determine the correlation between continuously distributed diffusion-weighted image (DWI)-derived parameters and histopathologic indexes.

Methods

Fifty-four mice bearing HCT-116 colorectal tumors were included for analysis; 12 mice were used for continuous observation, and the other 42 mice were used for break-point observation. All mice were randomly divided into radiotherapy and non-radiotherapy groups. Optical imaging and MRI were performed at different time points according to radiotherapy regimen (baseline, 24 h, 48 h, 72 h, 7 d, 14 d, and 28 d). Continuous observation data were analyzed to show the difference of dynamic changing trends of optical and MR-DWI–derived parameters between radiotherapy and non-radiotherapy groups (photon numbers, D_max, full width half maximum [FWHM], and apparent diffusion coefficient [ADC] value). Break-point observation data were used to analyze the correlation between histopathologic indices and DWI-derived parameters.

Results

There was a significant difference in the changing trends of photon numbers, D_max, FWHM, and ADC value between radiotherapy and non-radiotherapy groups, especially at early time points. There was moderate negative correlation between Ki67 and percentage changes of D_max, FWHM, and ADC values (the correlation coefficients were 0.632, 0.449, and 0.586, P<0.001, P=0.008, and P<0.001, respectively). There was moderate negative correlation between survivin and percentage changes of D_max and ADC values (correlation coefficients were 0.496 and 0.473, P=0.004 and P=0.006, respectively).

Conclusion

The continuously distributed DWI-derived parameters could reflect histological behavior to some extent and, thus, are potential markers for early noninvasive monitoring of tumor cell apoptosis and proliferation.

Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients

OBJECTIVE

To examine the prognostic capabilities of intravoxel incoherent motion (IVIM) metrics and their ability to predict response to neoadjuvant treatment (NAT). Additionally, to observe changes in IVIM metrics between pre- and post-treatment MRI.

METHODS

This IRB-approved, HIPAA-compliant retrospective study observed 31 breast cancer patients (32 lesions). Patients underwent standard bilateral breast MRI along with diffusion-weighted imaging before and after NAT. Six patients underwent an additional IVIM-MRI scan 12-14 weeks after initial scan and 2 cycles of treatment. In addition to apparent diffusion coefficients (ADC) from monoexponential decay, IVIM mean values (tissue diffusivity D_t, perfusion fraction f_p, and pseudodiffusivity D_p) and histogram metrics were derived using a biexponential model. An additional filter identified voxels of highly vascular tumor tissue (VTT), excluding necrotic or normal tissue. Clinical data include histology of biopsy and clinical response to treatment through RECIST assessment. Comparisons of treatment response were made using Wilcoxon rank-sum tests.

RESULTS

Average, kurtosis, and skewness of pseudodiffusion D_p significantly differentiated RECIST responders from nonresponders. ADC and D_t values generally increased (∼70%) and VTT% values generally decreased (∼20%) post-treatment.

CONCLUSION

D_p metrics showed prognostic capabilities; slow and heterogeneous pseudodiffusion offer poor prognosis. Baseline ADC/D_t parameters were not significant predictors of response. This work suggests that IVIM mean values and heterogeneity metrics may have prognostic value in the setting of breast cancer NAT.

Intravoxel incoherent motion diffusion-weighted imaging in assessing bladder cancer invasiveness and cell proliferation

BACKGROUND

Nonmuscle-invasive bladder cancer (NMIBC, Stage T1 or lower) is treated with transurethral resection (TUR), while muscle-invasive bladder cancer (MIBC, Stage T2 or more) requires neoadjuvant chemotherapy before radical cystectomy. Hence, preoperative differentiation is vital.

PURPOSE

To investigate whether intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) can differentiate NMIBC from MIBC and to assess whether there were correlations between IVIM parameters and the Ki-67 labeling index (LI).

STUDY TYPE

Retrospective.

SUBJECTS

Thirty-six patients diagnosed with bladder cancer confirmed by histopathological findings.

FIELD STRENGTH/SEQUENCE

3.0T magnetic resonance imaging (MRI) DWI with eight b-values ranging from 0 to 1000 s/mm² .

ASSESSMENT

Molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADC) were calculated by biexponential and monoexponential models fits, respectively.

STATISTICAL TESTS

Comparisons were made between the MIBC and NMIBC group, and differences were analyzed by comparing the areas under the receiver-operating characteristic curves (AUCs). The correlations between these parameters and Ki-67 LI were assessed by Spearman's rank correlation analysis.

RESULTS

The ADC and D value were significantly lower in patients with MIBC compared to those with NMIBC (P < 0.01). No significant (P > 0.05) differences were observed in D* and f. The AUC of D value (0.894) was significantly (P < 0.05) larger than the ADC value (0.786), with sensitivities and specificities of 95% and 87.5% (D) and 80% and 68.7% (ADC), respectively. In addition, the D and ADC values were significantly correlated with Ki-67 LI (r = -0.785, r = -0.643, respectively; both P < 0.01).

DATA CONCLUSION

The D value obtained from IVIM exhibited better performance than conventional DWI for distinguishing NMIBC from MIBC and may serve as a potential imaging biomarker for bladder cancer invasion.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1054-1060.

Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations

OBJECTIVES

To assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T.

METHODS

Thirty-six female patients (median age 53, range 32-75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight b values (0, 30, 60, 90, 120, 300, 600, 900 s/mm2). Apparent diffusion coefficient (ADC), tissue diffusion coefficient (D t), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha (α) parameters were derived. Then t tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels.

RESULTS

All patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups (p < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) (p = 0.03, p = 0.04). Overall, stretched-exponential parameters showed excellent repeatability.

CONCLUSION

DW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes.

KEY POINTS

• Baseline diffusion coefficients demonstrated significant differences between complete pathological responders and non-responders. • Increase in ADC and DDC at mid-treatment can discriminate responders and non-responders. • The ƒ fraction at mid-treatment decreased in responders whereas increased in non-responders. • The mono- and stretched-exponential models showed excellent inter- and intrarater repeatability. • Treatment effects can potentially be assessed by non-mono-exponential diffusion models.

Intravoxel Incoherent Motion Diffusion for Identification of Breast Malignant and Benign Tumors Using Chemometrics

The aim of the paper is to identify the breast malignant and benign lesions using the features of apparent diffusion coefficient (ADC), perfusion fraction f, pseudodiffusion coefficient D^⁎, and true diffusion coefficient D from intravoxel incoherent motion (IVIM). There are 69 malignant cases (including 9 early malignant cases) and 35 benign breast cases who underwent diffusion-weighted MRI at 3.0 T with 8 b-values (0~1000 s/mm²). ADC and IVIM parameters were determined in lesions. The early malignant cases are used as advanced malignant and benign tumors, respectively, so as to assess the effectiveness on the result. A predictive model was constructed using Support Vector Machine Binary Classification (SVMBC, also known Support Vector Machine Discriminant Analysis (SVMDA)) and Partial Least Squares Discriminant Analysis (PLSDA) and compared the difference between them both. The D value and ADC provide accurate identification of malignant lesions with b = 300, if early malignant tumor was considered as advanced malignant (cancer). The classification accuracy is 93.5% for cross-validation using SVMBC with ADC and tissue diffusivity only. The sensitivity and specificity are 100% and 87.0%, respectively, r²_cv = 0.8163, and root mean square error of cross-validation (RMSECV) is 0.043. ADC and IVIM provide quantitative measurement of tissue diffusivity for cellularity and are helpful with the method of SVMBC, getting comprehensive and complementary information for differentiation between benign and malignant breast lesions.

Optimization of intra-voxel incoherent motion measurement in diffusion-weighted imaging of breast cancer

Purpose

The purpose of this study was to optimize intra‐voxel incoherent motion (IVIM) measurement in diffusion‐weighted imaging (DWI) of breast cancer by separating perfusion and diffusion effects through the determination of an optimal threshold b‐value, thus benign and cancerous breast tissues can be accurately differentiated using IVIM‐derived diffusion and perfusion parameters.

Materials and Methods

Twenty‐eight patients, with biopsy‐confirmed breast cancers, were studied with a 3T MRI scanner, using T1‐weighted dynamic contrast‐enhanced MRI images, and diffusion‐weighted images with nine b‐values, ranging from 0 to 1000 s/mm². IVIM‐derived parameter maps for tissue diffusion coefficients D, perfusion fraction f, and pseudo‐diffusion coefficients D* were computed using the segmented fitting method with optimized threshold b‐value, and the sum of squared residuals (SSR) were calculated for IVIM‐derived parameters in different breast lesions.

Results

The IVIM analysis method developed in this work can separate perfusion and diffusion effects with the optimal threshold b‐value of 300 s/mm², and the results of diffusion and perfusion parameters from IVIM analysis can be used to differentiate pathological changes in breast tissues. It was found that the averages and standard deviations of the diffusion and perfusion parameters, D, f, D*, are the following, for malignant, benign and normal breast tissues respectively: D (0.813 ± 0.225 × 10⁻³ mm²/s, 1.437 ± 0.538 × 10⁻³ mm²/s, 1.838 ± 0.213 × 10⁻³ mm²/s), f (10.73 ± 3.44%, 7.86 ± 3.70%, 8.92 ± 3.72%), D* (15.23 ± 12.17×10⁻³ mm²/s, 12.02 ± 3.19 × 10⁻³ mm²/s, 12.03 ± 7.21 × 10⁻³ mm²/s).

Conclusion

IVIM‐derived diffusion and perfusion parameter maps depend highly on the choice of threshold b‐value. Using the methodology developed in this work, and with the optimized threshold b‐value, the diffusion and perfusion parameters of breast tissues can be accurately assessed, making IVIM MRI a technique of choice for differential diagnosis of breast cancer.

Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study

The diffusion signal in breast tissue has primarily been modelled using apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion tensor (DT) models, which may be too simplistic to describe the underlying tissue microstructure. Formalin-fixed breast cancer samples were scanned using a wide range of gradient strengths, durations, separations and orientations. A variety of one- and two-compartment models were tested to determine which best described the data. Models with restricted diffusion components and anisotropy were selected in most cancerous regions and there were no regions in which conventional ADC or DT models were selected. Maps of ADC generally related to cellularity on histology, but maps of parameters from more complex models suggest that both overall cell volume fraction and individual cell size can contribute to the diffusion signal, affecting the specificity of ADC to the tissue microstructure. The areas of coherence in diffusion anisotropy images were small, approximately 1 mm, but the orientation corresponded to stromal orientation patterns on histology.

Comparison of non-Gaussian and Gaussian diffusion models of diffusion weighted imaging of rectal cancer at 3.0 T MRI

Water molecular diffusion in vivo tissue is much more complicated. We aimed to compare non-Gaussian diffusion models of diffusion-weighted imaging (DWI) including intra-voxel incoherent motion (IVIM), stretched-exponential model (SEM) and Gaussian diffusion model at 3.0 T MRI in patients with rectal cancer, and to determine the optimal model for investigating the water diffusion properties and characterization of rectal carcinoma. Fifty-nine consecutive patients with pathologically confirmed rectal adenocarcinoma underwent DWI with 16 b-values at a 3.0 T MRI system. DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models (IVIM-mono, IVIM-bi and SEM) on primary tumor and adjacent normal rectal tissue. Parameters of standard apparent diffusion coefficient (ADC), slow- and fast-ADC, fraction of fast ADC (f), α value and distributed diffusion coefficient (DDC) were generated and compared between the tumor and normal tissues. The SEM exhibited the best fitting results of actual DWI signal in rectal cancer and the normal rectal wall (R² = 0.998, 0.999 respectively). The DDC achieved relatively high area under the curve (AUC = 0.980) in differentiating tumor from normal rectal wall. Non-Gaussian diffusion models could assess tissue properties more accurately than the ADC derived Gaussian diffusion model. SEM may be used as a potential optimal model for characterization of rectal cancer.

Voxelwise analysis of simultaneously acquired and spatially correlated 18 F-fluorodeoxyglucose (FDG)-PET and intravoxel incoherent motion metrics in breast cancer

PURPOSE

Diffusion-weighted imaging (DWI) and ¹⁸ F-fluorodeoxyglucose-positron emission tomography (¹⁸ F-FDG-PET) independently correlate with malignancy in breast cancer, but the relationship between their structural and metabolic metrics is not completely understood. This study spatially correlates diffusion, perfusion, and glucose avidity in breast cancer with simultaneous PET/MR imaging and compares correlations with clinical prognostics.

METHODS

In this Health Insurance Portability and Accountability Act-compliant prospective study, with written informed consent and approval of the institutional review board and using simultaneously acquired FDG-PET and DWI, tissue diffusion (D_t ), and perfusion fraction (f_p ) from intravoxel incoherent motion (IVIM) analysis were registered to FDG-PET within 14 locally advanced breast cancers. Lesions were analyzed using 2D histograms and correlation coefficients between D_t , f_p , and standardized uptake value (SUV). Correlations were compared with prognostics from biopsy, metastatic burden from whole-body PET, and treatment history.

RESULTS

SUV||D_t correlation coefficient significantly distinguished treated (0.11 ± 0.24) from nontreated (-0.33 ± 0.26) patients (P = 0.005). SUV||f_p correlations were on average negative for the whole cohort (-0.17 ± 0.13).

CONCLUSION

Simultaneously acquired and registered FDG-PET/DWI allowed quantifiable descriptions of breast cancer microenvironments that may provide a framework for monitoring and predicting response to treatment. Magn Reson Med 78:1147-1156, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.

Intravoxel incoherent motion diffusion-weighted imaging for characterizing focal hepatic lesions: Correlation with lesion enhancement

PURPOSE

To evaluate the value of intravoxel incoherent motion (IVIM) parameters for characterizing focal hepatic lesions, and to assess the correlation between IVIM parameters and arterial nodule enhancement.

MATERIALS AND METHODS

We retrospectively evaluated 161 lesions (91 hepatocellular carcinomas [HCCs], 27 intrahepatic cholangiocarcinomas [IHCCs], 20 hemangiomas, 9 combined hepatocellular-cholangiocarcinomas, 9 metastases, and 5 other tumors) in 161 patients (105 men and 56 women; mean age, 56.4 years). Diffusion-weighted imaging was performed using nine b-values (0-900 s/mm² ) at 1.5T. Apparent diffusion coefficient (ADC), molecular diffusion coefficient (D_slow ), perfusion fraction (f), and perfusion-related diffusion coefficient (D_fast ) were compared among the hepatic lesions using analysis of variance (ANOVA). Receiver-operating-characteristic analysis was performed to assess diagnostic performance. The enhancement fraction (EF) and the relative enhancement (RE) of the hepatic lesions on arterial phase gadoxetic acid-enhanced images were correlated with the IVIM parameters using Spearman's test.

RESULTS

For the differentiation of hemangiomas from malignant tumors, D_slow showed the largest area under the curve (0.933) among all parameters. Although ADC did not show any difference among malignant lesions (P ≥ 0.28), HCCs showed a significantly lower D_slow than IHCC (P < 0.001) and a higher f than did IHCC (P < 0.001) and metastasis (P = 0.027); f had a significant positive correlation with EF (r = 0.420, P < 0.001) and RE (r = 0.264, P = 0.001).

CONCLUSION

IVIM parameters are more helpful in characterizing malignant hepatic lesions than ADC; f may reflect the extent and degree of hepatic nodule enhancement in the arterial phase, and may allow for differentiation of HCC from IHCC and metastasis.

LEVEL OF EVIDENCE

3 J. MAGN. RESON. IMAGING 2017;45:1589-1598.

Multiparametric MR Imaging of Breast Cancer

Breast MR imaging has increased in popularity over the past 2 decades due to evidence of its high sensitivity for cancer detection. Current clinical MR imaging approaches rely on the use of a dynamic contrast-enhanced acquisition that facilitates morphologic and semiquantitative kinetic assessments of breast lesions. The use of more functional and quantitative parameters holds promise to broaden the utility of MR imaging and improve its specificity. Because of wide variations in approaches for measuring these parameters and the considerable technical challenges, robust multicenter data supporting their routine use are not yet available, limiting current applications of many of these tools to research purposes.

Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion-weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up-to-date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM-sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions. J. Magn. Reson. Imaging 2016;44:521-540.

Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses

BACKGROUND

The aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterization of benign and malignant breast masses compared with conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI).

PATIENTS AND METHODS

This study included 23 benign and 31 malignant breast masses of 48 patients. Main indicators were initial enhancement ratio (IER), time-signal intensity curve (TIC), apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). The discriminative abilities of the different models were compared by means of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) analysis.

RESULTS

D had the highest AUC (0.980), sensitivity (93.55%), specificity (100%), and diagnostic accuracy (96.36%). Both D and TIC could provide the independent predicted features for malignant breast masses. The combination of D and TIC had an AUC of up to 0.990.

CONCLUSION

D of IVIM can effectively complement existing conventional DCE-MRI and DW-MRI in differentiating malignant from benign breast masses. IVIM combined with DCE-MRI is a robust means of evaluating breast masses.

Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors

BACKGROUND

The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model.

METHODS

3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis.

RESULTS

For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and bi-exponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis.

CONCLUSIONS

Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice.

Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue

PURPOSE

To explore the application of diffusion tensor imaging (DTI) for breast tissue and breast pathologies using a stimulated-echo acquisition mode (STEAM) with variable diffusion times.

MATERIALS AND METHODS

In this Health Insurance Portability and Accountability Act-compliant study, approved by the local institutional review board, eight patients and six healthy volunteers underwent an MRI examination at 3 Tesla including STEAM-DTI with several diffusion times ranging from 68.5 to 902.5 ms. A DTI model was fitted to the data for each diffusion time, and parametric maps of mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for healthy fibroglandular tissue (FGT) and lesions. The median value of radial diffusivity for FGT was fitted to a linear decay to obtain an estimation of the surface-to-volume ratio, from which the radial diameter was calculated.

RESULTS

For healthy FGT, radial diffusivity presented a linear decay with the square root of the diffusion time resulting in a range of estimated radial diameters from 202 to 496 µm, while axial diffusivity presented a nearly time-independent diffusion. Residual fat signal was reduced at longer diffusion times due to the shorter T1 of fat. Residual fat signal to the overall signal in the healthy volunteers' FGT was found to range from 2.39% to 2.55% (shortest mixing time), and from 0.40% to 0.51% (longest mixing time) for the b500 images.

CONCLUSION

The use of variable diffusion times may provide an in vivo noninvasive tool to probe diffusion lengths in breast tissue and breast pathology, and might aid by improving fat suppression at longer diffusion times.

LEVEL OF EVIDENCE

2 J. Magn. Reson. Imaging 2017;45:84-93.

Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity

OBJECTIVE

To investigate the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) measured by diffusion tensor imaging (DTI), tissue cellularity and their relationship in breast malignant/benign lesions.

METHODS

88 patients with 88 breast lesions who underwent DTI and dynamic contrast-enhanced MR scanning between November 2013 and December 2014 were retrospectively analyzed. The diagnosis was confirmed pathologically. ADC and FA values as well as histopathological cellularity of different pathological types of lesions were analyzed and compared statistically. The Pearson's correlation between cellularity and ADC and FA was calculated.

RESULTS

There were 59 cases of breast cancer and 29 cases of benign lesions included in the study. ADC values of breast cancers were statistically lower than that of benign lesions (p < 0.001). FA and cellularity were higher in cancers than in benign lesions with statistical significance (p < 0.05 and p < 0.001, respectively). The mean FA values in the patients with invasive ductal carcinoma (IDC) were higher than that in the patients with ductal carcinoma in situ (DCIS) without statistical difference (p > 0.05). The ADC and the cellularity in the IDC of grade III were statistically lower (p < 0.05) and higher (p < 0.05) than that in the DCIS and IDC of grade I-II, respectively. ADC was negatively correlated to cellularity (r = -0.8319, p < 0.001) and FA was positively correlated to cellularity (r = 0.4231, p < 0.001).

CONCLUSION

ADC and FA values were statistically different between benign and malignant breast lesions and were significantly correlated to tissue cellularity. ADC and FA may help to discriminate malignant from benign breast lesions and to predict cellularity. ADC is helpful in the prediction of the grade of breast cancer.

ADVANCES IN KNOWLEDGE

ADC and FA values were statistically different between benign and malignant breast lesions and were significantly correlated to tissue cellularity.

Intravoxel incoherent motion diffusion-weighted imaging of the pancreas: Characterization of benign and malignant pancreatic pathologies

PURPOSE

To evaluate the diagnostic value of apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters in differentiating patients with either a normal pancreas (NP), pancreatic ductal adenocarcinoma (PDAC), neuroendocrine tumor (NET), solid pseudopapillary tumor (SPT), acute pancreatitis (AcP), vs. autoimmune pancreatitis (AIP).

MATERIALS AND METHODS

In all, 84 pathologically confirmed pancreatic tumors (60 PDACs, 15 NETs, 9 SPTs), 20 pancreatitis (13 AcPs, 7 AIPs), and 30 NP subjects underwent IVIM diffusion-weighted imaging using 10 b-values (0-900 sec/mm² ) at 1.5T. The ADC, pure molecular diffusion coefficient (D_slow ), perfusion fraction (f), and perfusion-related diffusion coefficient (D_fast ) were calculated and compared using a Kruskal-Wallis test and post-hoc Dunn procedure. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance.

RESULTS

The f and D_fast of the PDAC were significantly lower than that of the NP (f = 0.10 vs. 0.24; D_fast  = 42.21 vs. 71.74 × 10^-3 mm² /sec; P < 0.05). In ROC analysis, f showed the best diagnostic performance (area-under-the-curve, 0.919) among all parameters in differentiating PDAC from NP (P ≤ 0.001). The f values of AcP (0.11) and AIP (0.13) and the D_fast values of SPT (20.48 × 10^-3 mm² /sec) and AcP (24.49 × 10^-3 mm² /sec) were significantly lower compared with NP (f = 0.24; D_fast  = 71.74 × 10^-3 mm² /sec; P < 0.05). For NET, the f (0.21) was significantly higher than that of PDAC (0.10, P < 0.01).

CONCLUSION

Perfusion-related parameters f and D_fast are more helpful in characterizing pancreatic diseases than ADC or D_slow . The PDCA, SPT, AcP, and AIP were characterized by reduced f and D_fast values compared with normal pancreas. The f value might help in differentiating between PDAC and NET.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:260-269.