Investigation of prostate cancer using diffusion-weighted intravoxel incoherent motion imaging

Intravoxel incoherent motion and diffusion kurtosis imaging and their machine-learning-based texture analysis for detection and assessment of prostate cancer severity at 3 T

OBJECTIVES

To evaluate the role of combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) and their machine-learning-based texture analysis for the detection and assessment of severity in prostate cancer (PCa).

MATERIALS AND METHODS

Eighty-eight patients underwent MRI on a 3 T scanner after giving informed consent. IVIM-DKI data were acquired using 13 b values (0-2000 s/mm2) and analyzed using the IVIM-DKI model with the total variation (TV) method. PCa patients were categorized into two groups: clinically insignificant prostate cancer (CISPCa) (Gleason grade ≤ 6) and clinically significant prostate cancer (CSPCa) (Gleason grade ≥ 7). One-way analysis-of-variance, t test, and receiver operating characteristic analysis was performed to measure the discriminative ability to detect PCa using IVIM-DKI parameters. A chi-square test was used to select important texture features of apparent diffusion coefficient (ADC) and IVIM-DKI parameters. These selected texture features were used in an artificial neural network for PCa detection.

RESULTS

ADC and diffusion coefficient (D) were significantly lower (p < 0.001), and kurtosis (k) was significantly higher (p < 0.001), in PCa as compared with benign prostatic hyperplasia (BPH) and normal peripheral zone (PZ). ADC, D, and k showed high areas under the curves (AUCs) of 0.92, 0.89, and 0.88, respectively, in PCa detection. ADC and D were significantly lower (p < 0.05) as compared with CISPCa versus CSPCa. D for detecting CSPCa was high, with an AUC of 0.63. A negative correlation of ADC and D with GS (ADC, ρ = -0.33; D, ρ = -0.35, p < 0.05) and a positive correlation of k with GS (ρ = 0.22, p < 0.05) were observed. Combined IVIM-DKI texture showed high AUC of 0.83 for classification of PCa, BPH, and normal PZ.

CONCLUSION

D, f, and k computed using the IVIM-DKI model with the TV method were able to differentiate PCa from BPH and normal PZ. Texture features of combined IVIM-DKI parameters showed high accuracy and AUC in PCa detection.

Magnetic Resonance Perfusion Imaging of Prostate

Magnetic resonance (MR) perfusion imaging, both with and without exogenous contrast agents, has the potential to assess tissue perfusion and vascularity in prostate cancer. Dynamic contrast-enhanced (DCE) MRI is an important element of the clinical non-invasive multiparametric MRI, which can be used to differentiate benign from malignant lesions, to stage tumors, and to monitor response to therapy. The arterial spin labeled (ASL) and intravoxel incoherent motion (IVIM) diffusion-weighted MRI have the advantage of quantitative perfusion measurements without the concerns of gadolinium-based contrast agent safety and retention issues. The adoption of these non-contrast techniques in clinical practice needs more research and clinical evaluation.

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.

Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI

This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction f_ic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R² = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.

A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules

Objective

This study aimed to compare the diagnostic capacity between IVIM and DKI in differentiating malignant from benign thyroid nodules.

Material and Methods

This study is based on magnetic resonance imaging data of the thyroid with histopathology as the reference standard. Spearman analysis was used to assess the relationship of IVIM-derived parameters D, f, D* and the DKI-derived parameters D_app and K_app. The parameters of IVIM and DKI were compared between the malignant and benign groups. Binary logistic regression analysis was performed to establish the diagnostic model, and receiver operating characteristic (ROC) curve analysis was subsequently performed. The DeLong test was used to compare the diagnostic effectiveness of different prediction models. Spearman analysis was used to assess the relationship of Ki-67 expression and parameters of IVIM and DKI.

Results

Among the 93 nodules, 46 nodules were malignant, and 47 nodules were benign. The D_app of DKI-derived parameter was related to the D (P < 0.001, r = 0.863) of IVIM-derived parameter. The K_app of DKI-derived parameter was related to the D (P < 0.001, r = -0.831) of IVIM-derived parameters. The malignant group had a significantly lower D value (P < 0.001) and f value (P = 0.013) than the benign group. The malignant group had significantly higher K_app and lower D_app values (all P < 0.001). The D+f had an area under the curve (AUC) of 0.951. The D_app+K_app had an AUC of 0.943. The D+f+D_app+K_app had an AUC of 0.954. The DeLong test showed no statistical significance among there prediction models. The D (P = 0.007) of IVIM-derived parameters and D_app (P = 0.045) of DKI-derived parameter were correlated to the Ki-67 expression.

Conclusions

IVIM and DKI were alternative for each other in in differentiating malignant from benign thyroid nodules.

The Classification Power of Classical and Intra-voxel Incoherent Motion (IVIM) Fitting Models of Diffusion-weighted Magnetic Resonance Images: An Experimental Study

This study aims at investigating the classification power of different b-values of the diffusion-weighted magnetic resonance images (DWI) as indicator of prostate cancer. This paper investigates several techniques for analyzing data from DWI acquired at a range of b-values for the purpose of detecting prostate cancer. In the first phase of experiments, we analyze the available data by producing two main parametric maps using two common models, namely: the intra-voxel incoherent motion (IVIM) model and the mono-exponential ADC model. Accordingly, we evaluated the benign/malignant tissue classification potential of several parametric maps produced using different combinations of b-values and fitting models. In the second phase, we utilized the maps that performed best in the first phase of experiments to design a machine learning-based computer-assisted diagnosis system for the detection of early stage prostate cancer. The system performance was cross-validated using data from 20 patients. On a fivefold cross-validation scheme, a maximum accuracy and an area under the receiver operating characteristic (AUC) of 90% and 0.978, respectively, was achieved by a system that uses ADC maps fitted using the mono-exponential model at 11 different b-values. The results suggest that the proposed machine learning-based diagnosis system is potentially powerful in differentiating between malignant and benign prostate tissues when combined with carefully generated ADC maps.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

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

Objectives

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

Materials and Methods

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

Results

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

Conclusion

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

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

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

IVIM-DKI for differentiation between prostate cancer and benign prostatic hyperplasia: comparison of 1.5 T vs. 3 T MRI

OBJECTIVE

To implement an advanced spatial penalty-based reconstruction to constrain the intravoxel incoherent motion (IVIM)-diffusion kurtosis imaging (DKI) model and investigate whether it provides a suitable alternative at 1.5 T to the traditional IVIM-DKI model at 3 T for clinical characterization of prostate cancer (PCa) and benign prostatic hyperplasia (BPH).

MATERIALS AND METHODS

Thirty-two patients with biopsy-proven PCa were recruited for MRI examination (n = 16 scanned at 1.5 T, n = 16 scanned at 3 T). Diffusion-weighted imaging (DWI) with 13 b values (b = 0 to 2000 s/mm² up to 3 averages, 1.5 T: TR = 5.774 s, TE = 81 ms and 3 T: TR = 4.899 s, TE = 100 ms), T2-weighted, and T1-weighted imaging were used on the 1.5 T and 3 T MRI scanner, respectively. The IVIM-DKI signal was modeled using the traditional IVIM-DKI model and a novel model in which the total variation (TV) penalty function was combined with the traditional model to optimize non-physiological variations. Paired and unpaired t-tests were used to compare intra-scanner and scanner group differences in IVIM-DKI parameters obtained using the novel and the traditional models. Analysis of variance with post hoc test and receiver operating characteristic (ROC) curve analysis were used to assess the ability of parameters obtained using the novel model (at 1.5 T) and the traditional model (at 3 T) to characterize prostate lesions.

RESULTS

IVIM-DKI modeled using novel model with TV spatial penalty function at 1.5 T, produced parameter maps with 50-78% lower coefficient of variation (CV) than traditional model at 3 T. Novel model estimated higher D with lower D*, f and k values at both field strengths compared to traditional model. For scanner differences, the novel model at 1.5 T estimated lower D* and f values as compared to traditional model at 3 T. At 1.5 T, D and f values were significantly lower with k values significantly higher in tumor than BPH and healthy tissue. D (AUC: 0.98), f (AUC: 0.82), and k (AUC: 0.91) parameters estimated using novel model showed high diagnostic performance in cancer lesion detection at 1.5 T.

DISCUSSION

In comparison with the IVIM-DKI model at 3 T, IVIM-DKI signal modeled with the TV penalty function at 1.5 T showed lower estimation errors. The proposed novel model can be utilized for improved detection of prostate lesions.

A comparative study of methods for determining Intravoxel incoherent motion parameters in cervix cancer

Background

To compare different fitting methods for determining IVIM (Intravoxel Incoherent Motion) parameters and to determine whether the use of different IVIM fitting methods would affect differentiation of cervix cancer from normal cervix tissue.

Methods

Diffusion-weighted echo-planar imaging of 30 subjects was performed on a 3.0 T scanner with b-values of 0, 30, 100, 200, 400, 1000 s/mm². IVIM parameters were estimated using the segmented (two-step) fitting method and by simultaneous fitting of a bi-exponential function. Segmented fitting was performed using two different cut-off b-values (100 and 200 s/mm²) to study possible variations due to the choice of cut-off. Friedman’s test and Student’s t-test were respectively used to compare IVIM parameters derived from different methods, and between cancer and normal tissues.

Results

No significant difference was found between IVIM parameters derived from the segmented method with b-value cutoff of 200 s/mm² and the simultaneous fitting method (P>0.05). Tissue diffusivity (D) and perfusion fraction (f) were significantly lower in cervix cancer than normal tissue (P< 0.05).

Conclusions

IVIM parameters derived using fitting methods with small cutoff b-values could be different, however, the segmented method with b-value cutoff of 200 s/mm² are consistent with the simultaneous fitting method and both can be used to differentiate between cervix cancer and normal tissue.

Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI

BACKGROUND

 Diffusion-weighted imaging (DWI) is an essential component of the multiparametric MRI exam for the diagnosis and assessment of prostate cancer (PCa). Over the last two decades, various models have been developed to quantitatively correlate the DWI signal with microstructural characteristics of prostate tissue. The simplest approach (ADC: apparent diffusion coefficient) - currently established as the clinical standard - describes monoexponential decay of the DWI signal. While numerous studies have shown an inverse correlation of ADC values with the Gleason score, the ADC model lacks specificity and is based on water diffusion dynamics that are not true in human tissue. This article aims to explain the biophysical limitations of the standard DWI model and to discuss the potential of more complex, advanced DWI models.

METHODS

 This article is a review based on a selective literature review.

RESULTS

 Four phenomenological DWI models are introduced: diffusion tensor imaging, intravoxel incoherent motion, biexponential model, and diffusion kurtosis imaging. Their parameters may potentially improve PCa diagnostics but show varying degrees of statistical significance with respect to the detection and characterization of PCa in current studies. Phenomenological model parameters lack specificity, which has motivated the development of more descriptive tissue models that directly relate microstructural features to the DWI signal. Finally, we present two of such structural models, i. e. the VERDICT (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors) and RSI (Restriction Spectrum Imaging) model. Both have shown promising results in initial studies regarding the characterization and prognosis of PCa.

CONCLUSION

 Recent developments in DWI techniques promise increasing accuracy and more specific statements about microstructural changes of PCa. However, further studies are necessary to establish a standardized DWI protocol for the diagnosis of PCa.

KEY POINTS

  · DWI is paramount to the mpMRI exam for the diagnosis of PCa.. · Though of clinical value, the ADC model lacks specificity and oversimplifies tissue complexities.. · Advanced phenomenological and structural models have been developed to describe the DWI signal.. · Phenomenological models may improve diagnostics but show inconsistent results regarding PCa assessment.. · Structural models have demonstrated promising results in initial studies regarding PCa characterization..

CITATION FORMAT

· Wichtmann BD, Zöllner FG, Attenberger UI et al. Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI. Fortschr Röntgenstr 2021; 193: 399 - 409.

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.

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

BACKGROUND

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

PURPOSE

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

STUDY TYPE

Retrospective.

SUBJECTS

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

FIELD STRENGTH/SEQUENCE

3T multishell diffusion-weighted sequence.

ASSESSMENT

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

STATISTICAL TESTS

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

RESULTS

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

DATA CONCLUSION

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

Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy

PURPOSE

Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multi-parametric MR (mp-MR) for prostate cancer (PCa). The aim of this study is to demonstrate HC can diagnose PCa in a straightforward interpretable way, in contrast to deep learning (DL) techniques.

METHODS

HC was constructed using mp-MR including intravoxel incoherent motion, diffusion kurtosis imaging, and dynamic contrast-enhanced MRI from 40 tumor and normal tissues in peripheral zone (PZ) and 23 tumor and normal tissues in transition zone (TZ). HC model was optimized by assessing the combinations of several dissimilarity and linkage methods. Goodness of HC model was validated by internal methods.

RESULTS

Accuracy for differentiating tumor and normal tissue by optimal HC model was 96.3% in PZ and 97.8% in TZ, comparable to current clinical standards. Relationship between input (DWI and permeability parameters) and output (tumor and normal tissue cluster) was shown by heat maps, consistent with literature.

CONCLUSION

HC can accurately differentiate PCa and normal tissue, comparable to state-of-the-art diffusion based parameters. Contrary to DL techniques, HC is an operator-independent ML technique producing results that can be interpreted such that the results can be knowledgeably judged.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa.

Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D^*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities.

Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D^* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D^*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D^* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76).

Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.

Comparative Study of Monoexponential, Intravoxel Incoherent Motion, Kurtosis, and IVIM-Kurtosis Models for the Diagnosis and Aggressiveness Assessment of Prostate Cancer

Objective: This study aimed to compare the potential of monoexponential model (MEM), intravoxel incoherent motion (IVIM) model, kurtosis model, and IVIM–kurtosis model in the diagnosis and aggressiveness assessment of prostate cancer (PCa).

Materials and Methods: Thirty-six patients were recruited. Diffusion-weighted images were acquired on a 3.0-T magnetic resonance imaging (MRI) system using 0 b values up to 2,000 s/mm² and analyzed using four models: MEM (ADC_MEM), IVIM (D_IVIM, D*_IVIM, f_IVIM), kurtosis (D_kurtosis, K_kurtosis), and IVIM–kurtosis (D_{IVIM−kurtosis}, D^*_{IVIM−kurtosis}, f_{IVIM−kurtosis}, D_{IVIM−kurtosis}) models. The values of these parameters were calculated and compared between PCa, benign prostatic hyperplasia (BPH), and prostatitis. Correlations between these parameters and the Gleason score (GS) of PCa were evaluated using the Pearson test.

Results: Forty-five lesions were studied, including 18 PCa, 12 prostatitis, and 15 BPH lesions. The ADC_MEM, D_IVIM, f_IVIM, D_kurtosis, and D_{IVIM−kurtosis} values were significantly lower and K_kurtosis and K_{IVIM−kurtosis} values were significantly higher in PCa compared with prostatitis and BPH. The area under the curve (AUC) of ADC_MEM showed significantly higher values than that of f_IVIM and K_{IVIM−kurtosis}, but no statistical differences were found between the other parameters. The D^*_{IVIM−kurtosis} value correlated negatively and f_{IVIM−kurtosis} and K_{IVIM−kurtosis} values correlated positively with the GS.

Conclusion: The MEM, IVIM, kurtosis, and IVIM–kurtosis models were all useful for the diagnosis of PCa, and the diagnostic efficacy seemed to be similar. The IVIM–kurtosis model may be superior to the MEM, IVIM, and kurtosis models in the grading of PCa.

Intravoxel incoherent motion (IVIM) 3 T MRI for orbital lesion characterization

OBJECTIVES

To determine the diagnostic accuracy of MRI intravoxel incoherent motion (IVIM) when characterizing orbital lesions, which is challenging due to a wide range of locations and histologic types.

METHODS

This IRB-approved prospective single-center study enrolled participants presenting with an orbital lesion undergoing a 3-T MRI prior to surgery from December 2015 to July 2019. An IVIM sequence with 15 b values ranging from 0 to 2000 s/mm² was performed. Two neuroradiologists, blinded to clinical data, individually analyzed morphological MRIs. They drew one region of interest inside each orbital lesion, providing apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion fraction (f), and pseudodiffusion coefficient (D*) values. T test, Mann-Whitney U test, and receiver operating characteristic curve analyses were performed to discriminate between orbital lesions and to determine the diagnostic accuracy of the IVIM parameters.

RESULTS

One hundred fifty-six participants (84 women and 72 men, mean age 54.4 ± 17.5 years) with 167 orbital lesions (98/167 [59%] benign lesions including 54 orbital inflammations and 69/167 [41%] malignant lesions including 32 lymphomas) were included in the study. ADC and D were significantly lower in malignant than in benign lesions: 0.8 × 10^-3 mm²/s [0.45] versus 1.04 × 10^-3 mm²/s [0.33], p < 0.001, and 0.75 × 10^-3 mm²/s [0.40] versus 0.98 × 10^-3 mm²/s [0.42], p < 0.001, respectively. D* was significantly higher in malignant lesions than in benign ones: 12.8 × 10^-3 mm²/s [20.17] versus 7.52 × 10^-3 mm²/s [7.57], p = 0.005. Area under curve was of 0.73, 0.74, 0.72, and 0.81 for ADC, D, D*, and a combination of D, f, and D*, respectively.

CONCLUSIONS

Our study showed that IVIM might help better characterize orbital lesions.

KEY POINTS

• Intravoxel incoherent motion (IVIM) helps clinicians to assess patients with orbital lesions. • Intravoxel incoherent motion (IVIM) helps clinicians to characterize orbital lymphoma versus orbital inflammation. • Management of patients becomes more appropriate.

Accelerated Segmented Diffusion-Weighted Prostate Imaging for Higher Resolution, Higher Geometric Fidelity, and Multi-b Perfusion Estimation

PURPOSE

The aim of this study was to improve the geometric fidelity and spatial resolution of multi-b diffusion-weighted magnetic resonance imaging of the prostate.

MATERIALS AND METHODS

An accelerated segmented diffusion imaging sequence was developed and evaluated in 25 patients undergoing multiparametric magnetic resonance imaging examinations of the prostate. A reduced field of view was acquired using an endorectal coil. The number of sampled diffusion weightings, or b-factors, was increased to allow estimation of tissue perfusion based on the intravoxel incoherent motion (IVIM) model. Apparent diffusion coefficients measured with the proposed segmented method were compared with those obtained with conventional single-shot echo-planar imaging (EPI).

RESULTS

Compared with single-shot EPI, the segmented method resulted in faster acquisition with 2-fold improvement in spatial resolution and a greater than 3-fold improvement in geometric fidelity. Apparent diffusion coefficient values measured with the novel sequence demonstrated excellent agreement with those obtained from the conventional scan (R = 0.91 for bmax = 500 s/mm and R = 0.89 for bmax = 1400 s/mm). The IVIM perfusion fraction was 4.0% ± 2.7% for normal peripheral zone, 6.6% ± 3.6% for normal transition zone, and 4.4% ± 2.9% for suspected tumor lesions.

CONCLUSIONS

The proposed accelerated segmented prostate diffusion imaging sequence achieved improvements in both spatial resolution and geometric fidelity, along with concurrent quantification of IVIM perfusion.

Relative Enhanced Diffusivity in Prostate Cancer: Protocol Optimization and Diagnostic Potential

BACKGROUND

Relative enhanced diffusivity (RED) is a potential biomarker for indirectly measuring perfusion in tissue using diffusion-weighted magnetic resonance imaging (MRI) with 3 b values.

PURPOSE

To optimize the RED MRI protocol for the prostate, and to investigate its potential for prostate cancer (PCa) diagnosis.

STUDY TYPE

Prospective.

POPULATION

Ten asymptomatic healthy volunteers and 35 patients with clinical suspicion of PCa.

SEQUENCE

3T T₂ - and diffusion-weighted MRI with b values: b = 0, 50, [100], 150, [200], 250, [300], 400, 800 s/mm² (values in brackets were only used for patients).

ASSESSMENT

Monte Carlo simulations were performed to assess noise sensitivity of RED as a function of intermediate b value. Volunteers were scanned 3 times to assess repeatability of RED. Patient data were used to investigate RED's potential for discriminating between biopsy-confirmed cancer and healthy tissue, and between true and false positive radiological findings.

STATISTICAL TESTS

Within-subject coefficient of variation (WCV) to assess repeatability and receiver-operating characteristic curve analysis and logistic regression to assess diagnostic performance of RED.

RESULTS

The repeatability was acceptable (WCV = 0.2-0.3) for all intermediate b values tested, apart from b = 50 s/mm² (WCV = 0.3-0.4). The simulated RED values agreed well with the experimental data, showing that an intermediate b value between 150-250 s/mm² minimizes noise sensitivity in both peripheral zone (PZ) and transition zone (TZ). RED calculated with the b values 0, 150 and 800 s/mm² was significantly higher in tumors than in healthy tissue in both PZ (P < 0.001, area under the curve [AUC] = 0.85) and PZ + TZ (P < 0.001, AUC = 0.84). RED was shown to aid apparent diffusion coefficient (ADC) in differentiating between false-positive findings and true-positive PCa in the PZ (AUC; RED = 0.71, ADC = 0.74, RED+ADC = 0.77).

DATA CONCLUSION

RED is a repeatable biomarker that may have value for prostate cancer diagnosis. An intermediate b value in the range of 150-250 s/mm² minimizes the influence of noise and maximizes repeatability.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1900-1910.

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

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

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).

Analysis of Diffusion-weighted MR Images Based on a Gamma Distribution Model to Differentiate Prostate Cancers with Different Gleason Score

Purpose:

Prostate cancer management includes identification of clinically significant cancers that may require curative treatment. Statistical models based on gamma distribution can describe diffusion signal decay curves of prostate cancer. The purpose of this study was to evaluate the ability of parameters obtained with the gamma model in differentiating prostate cancers with different Gleason score values.

Methods:

This study included 155 patients with prostate cancer who underwent multiparametric magnetic resonance imaging prior to prostate biopsy (127 patients) or radical prostatectomy (28 patients) between January 2015 and June 2017; 159 foci of prostate cancer were included in our study. We compared cases scored as Gleason score (GS) 3 + 3 and GS ≥ 3 + 4, and analyzed cases scored as GS ≤ 3+ 4 and GS ≥ 4 + 3 based on the gamma model (Frac < 1.0, Frac < 0.8, Frac < 0.5, Frac < 0.3, and Frac > 3.0), and apparent diffusion coefficient (ADC).

Results:

Among 159 cancerous lesions in 155 patients, 13 (8.2%) were GS 3 + 3 prostate cancers, 51 (32.0%) were GS 3 + 4 prostate cancers, 30 (18.2%) were GS 4 + 3 cancers, and 65 (40.9%) were GS ≥ 4 + 4 cancers. Frac < 0.3, Frac < 0.5, Frac < 0.8, and Frac < 1.0 were significantly higher and ADC values were significantly lower in GS ≥ 4 + 3 cancers than in GS ≤ 3 + 4 cancers (P < 0.01, P < 0.01, P < 0.01, P = 0.01, and P < 0.01, respectively). With receiver operating characteristic (ROC) analysis, Frac < 0.3 and Frac < 0.5 had significantly greater area under the ROC curve for discriminating GS ≥ 4 + 3 cancers from GS ≤ 3 + 4 cancers than ADC (P = 0.03, P < 0.01, respectively).

Conclusion:

Frac < 0.3 and Frac < 0.5 showed higher diagnostic performance than ADC for differentiating GS ≥ 4 + 3 from GS ≤ 3 + 4 cancers. The gamma model may add additional value in discrimination of tumor grades.

Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion-Weighted MRI: A Preliminary Study

BACKGROUND

The gamma distribution (GD) model is based on the statistical distribution of the apparent diffusion coefficient (ADC) parameter. The GD model is expected to reflect the probability of the distribution of water molecule mobility in different regions of tissue, but also the intra- and extracellular diffusion and perfusion components (f₁ , f₂ , f₃ fractions).

PURPOSE

To assess the GD model in the characterization and diagnostic performance of breast lesions.

STUDY TYPE

Prospective.

POPULATION

In all, 48 females with 24 benign and 33 malignant breast lesions.

FIELD STRENGTH/SEQUENCE

A diffusion-weighted sequence (b = 0-3000 s/mm² ) with a 3 T scanner.

ASSESSMENT

For each group of benign, malignant, invasive, and in situ breast lesions, the ADC was obtained. Also, θ and k parameters (scale and shape of the statistic distribution, respectively), f₁ , f₂ , and f₃ fractions were obtained from fitting the GD model to diffusion data.

STATISTICAL TESTS

Lesion types were compared regarding diffusion parameters using nonparametric statistics and receiver operating characteristic curve diagnostic performance.

RESULTS

The majority of GD parameters (k, f₁ , f₂ , f₃ fractions) showed significant differences between benign and malignant lesions, and between in situ and invasive lesions (f₁ , f₂ , f₃ fractions) (P ≤ 0.001). The best diagnostic performances were obtained with ADC and f₁ fraction in benign vs. malignant lesions (area under curve [AUC] = 0.923 and 0.913, sensitivity = 93.9% and 81.8%, specificity = 79.2% and 91.7%, accuracy = 87.7% and 86.0%, respectively). In invasive lesions vs. in situ lesions, the best diagnostic performance was obtained with f₁ fraction, which outperformed ADC results (AUC = 0.978 and 0.941, and sensitivity = 91.3% for both parameters, specificity = 100.0% and 90.0%, accuracy = 93.9% and 90.9%, respectively).

DATA CONCLUSION

This work shows that the GD model provides information in addition to the ADC parameter, suggesting its potential in the diagnosis of breast lesions. Level of Evidence 2: Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:230-238.

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

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

STROBE-A preliminary investigation of IVIM-DWI in cardiac imaging

This study aims to explore the possibility to apply intravoxel incoherent motion-magnetic resonance imaging (IVIM-MRI) in cardiac imaging.Multi-b-value diffusion-weighted imaging (DWI) sequence scanning was performed on 12 healthy volunteers. A double exponential model was adopted, and the b-value sequence was 0, 20, 60, 80, 120, 200, and 600 second/mm. The D-value, D*-value, and f-value of the anterior posterior and lateral walls of the ventricular septum were respectively measured on the short axis section of the heart, the parameters of the myocardium in different blood supply areas in each segment were recorded, and the measured data of these different segments were compared using analysis of variance.Among these 12 healthy volunteers, the D-value, D*-value, and f-value of these 72 segments were not exactly equal, the D-values of the myocardium in the 5th and 11th segment were lower than those in the 2nd, 3rd, 8th, and 9th segments, and the pairwise differences were statistically significant (P < .001). Furthermore, the difference in D-value between the 5th and 11th segments was not statistically significant (P = 1.000). The D*-value and f-value of the myocardium in the 5th and 11th segment were higher than those in the 2nd, 3rd, 8th, and 9th segments, and the pairwise differences were statistically significant (P < .001). Furthermore, the differences in D*-value and f-value between the 5th and 11th segments was not statistically significant (P = .214, .787).The intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) technique can quantitatively reflect the diffusion and blood perfusion status of the myocardium.

Development of a robust diffusion-MR elastography (dMRE) technique to mitigate intravoxel phase dispersion

Diffusion-magnetic resonance elastography (dMRE) is an emerging practical technique that can acquire diffusion magnetic resonance imaging and MRE simultaneously. However, a signal loss attributable to intravoxel phase dispersion (IVPD) interferes with the calculation of the apparent diffusion coefficient (ADC). This study presents an approach to dMRE that reduces the influence of IVPD by introducing a new pulse sequence. The existing and proposed techniques were performed using a phantom comprising five rods with different elasticities at 60 Hz vibration to investigate the accuracy of previous and proposed dMRE techniques. The measures of ADC and stiffness, obtained by using both dMRE techniques, were compared with conventional spin-echo (SE) diffusion and SE-MRE. Then, we evaluated those differences by using the mean of absolute differences (MAD) in each rod within the phantom. The results of the MAD of the stiffness from both dMRE techniques showed almost no difference. In contrast, the value of the ADC MAD (MAD ≒ 0.16 × 10^-3 mm²/s), obtained in the soft region within the phantom with the previous dMRE technique, was large. This value was about 2.7 times that of the value produced by the proposed dMRE technique. This difference must reflect the degree of influence of IVPD in both techniques. These results demonstrate that our dMRE technique is a robust method for addressing the signal loss attributable to IVPD.

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.

Repeatability of apparent diffusion coefficient and intravoxel incoherent motion parameters at 3.0 Tesla in orbital lesions

OBJECTIVES

To evaluate repeatability of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in the orbit.

METHODS

From December 2015 to March 2016, 22 patients were scanned twice using an IVIM sequence with 15b values (0-2,000 s/mm2) at 3.0T. Two readers independently delineated regions of interest in an orbital mass and in different intra-orbital and extra-orbital structures. Short-term test-retest repeatability and inter-observer agreement were assessed using the intra-class correlation coefficient (ICC), the coefficient of variation (CV) and Bland-Altman limits of agreements (BA-LA).

RESULTS

Test-retest repeatability of IVIM parameters in the orbital mass was satisfactory for ADC and D (mean CV 12% and 14%, ICC 95% and 93%), poor for f and D*(means CV 43% and 110%, ICC 90% and 65%). Inter-observer repeatability agreement was almost perfect in the orbital mass for all the IVIM parameters (ICC = 95%, 93%, 94% and 90% for ADC, D, f and D*, respectively).

CONCLUSIONS

IVIM appeared to be a robust tool to measure D in orbital lesions with good repeatability, but this approach showed a poor repeatability of f and D*.

KEY POINTS

• IVIM technique is feasible in the orbit. • IVIM has a good-acceptable repeatability of D (CV range 12-25 %). • IVIM interobserver repeatability agreement is excellent (ICC range 90-95 %). • f or D* provide higher test-retest and interobserver variabilities.

Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study

PURPOSE

To assess the performance of Support Vector Machines (SVM) classification to stratify the Gleason Score (GS) of prostate cancer (PCa) in the central gland (CG) based on image features across multiparametric magnetic resonance imaging (mpMRI).

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board, and informed consent was waived. One hundred fifty-two CG cancerous ROIs were identified through radiological-pathological correlation. Eleven parameters were derived from the mpMRI and histogram analysis, including mean, median, the 10th percentile, skewness and kurtosis, was performed for each parameter. In total, fifty-five variables were calculated and processed in the SVM classification. The classification model was developed with 10-fold cross-validation and was further validated mutually across two separated datasets.

RESULTS

With six variables selected by a feature-selection and variation test, the prediction model yielded an area under the receiver operating characteristics curve (AUC) of 0.99 (95% CI: 0.98, 1.00) when trained in dataset A2 and 0.91 (95% CI: 0.85, 0.95) for the validation in dataset B2. When the data sets were reversed, an AUC of 0.99 (95% CI: 0.99, 1.00) was obtained when the model was trained in dataset B2 and 0.90 (95% CI: 0.85, 0.95) for the validation in dataset A2.

CONCLUSION

The SVM classification based on mpMRI derived image features obtains consistently accurate classification of the GS of PCa in the CG.

Multiparametric magnetic resonance imaging for transition zone prostate cancer: essential findings, limitations, and future directions

OBJECTIVE

Review the multiparametric MRI (mpMRI) findings of transition zone (TZ) prostate cancer (PCa) using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI and to integrate mpMRI findings with clinical history, laboratory values, and histopathology.

CONCLUSION

TZ prostate tumors are challenging to detect clinically and at MRI. mpMRI using the combination of sequences has the potential to improve accuracy of TZ cancer detection and staging.

Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Objective

To differentiate prostate cancer lesions in transition zone by diffusion-weighted-MRI (DW-MRI).

Methods

Data from a total of 63 patients who underwent preoperative DWI (b of 0–1000 s/mm²) were prospectively collected and processed by a monoexponential (DWI) model and compared with a biexponential (IVIM) model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analyses were performed by outlining entire-tumor regions of interest (ROIs). These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on histopathological analysis of Magnetic Resonance/transrectal Ultrasound (MR/TRUS) fusion-guided biopsy. The diagnostic ability of differentiate the PCa from BHP in TZ was analyzed by ROC regression. Histogram analysis of quantitative parameters and Gleason score were assessed with Spearman correlation.

Results

Thirty (30 foci) cases of PCa in PZ and 33 (36 foci) cases of BPH were confirmed by pathology. Mean ADC, median ADC, 10th percentile ADC, 90th percentile ADC, kurtosis and skewness of ADC and mean D values, median D and 90th percentile D differed significantly between PCa and BHP in TZ. The highest classification accuracy was achieved by the mean ADC (0.841) and mean D (0.809). A logistic regression model based on mean ADC and mean D led to an AUC of 0.873, however, the difference is not significant. There were 7 Gleason 6 areas, 9 Gleason 7 areas, 8 Gleason 8 areas, 5 Gleason 9 areas and 2 Gleason 10 areas detected from the 31 prostate cancer areas, the mean Gleason value was(7.5 ± 1.2). The mean ADC and mean D had correlation with Gleason score(r = −0.522 and r = −0.407 respectively, P < 0.05).

Conclusion

The diagnosis efficiency of IVIM parameters was not superior to ADC in the diagnosis of PCa in TZ. Moreover, the combination of mean ADC and mean D did not perform better than the parameters alone significantly; It is feasible to stratify the pathological grade of prostate cancer by mean ADC.

Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging of focal vertebral bone marrow lesions: initial experience of the differentiation of nodular hyperplastic hematopoietic bone marrow from malignant lesions

OBJECTIVE

To evaluate the ability of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) parameters to differentiate nodular hyperplastic hematopoietic bone marrow (HHBM) from malignant vertebral bone marrow lesions (VBMLs).

MATERIALS AND METHODS

A total of 33 patients with 58 VBMLs, including 9 nodular HHBM lesions, 39 bone metastases, and 10 myelomas, were retrospectively assessed. All diagnoses were confirmed either pathologically or via image assessment. IVIM diffusion-weighted MRI with 11 b values (from 0 to 800 s/mm²) were obtained using a 3.0-T MR imager. The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), perfusion fraction (f), and pseudodiffusion coefficient (D*) were calculated. ADC and IVIM parameters were compared using the Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performances of ADC, D, f, and D* in terms of VBML characterization. The diagnostic performance of morphological MR sequences was also assessed for comparison.

RESULTS

The ADC and D values of nodular HHBM were significantly lower than those of malignant VBML (both p values < 0.001), whereas the f value was significantly higher (p < 0.001). However, there were no significant differences in D* between the two groups (p = 0.688). On ROC analysis, the area under the curve (AUC) for D was 1.000, which was significantly larger than that for ADC (AUC = 0.902).

CONCLUSION

Intravoxel incoherent motion diffusion-weighted MRI can be used to differentiate between nodular HHBM and malignant VBML. The D value was significantly lower for nodular HHBM, and afforded a better diagnostic performance than the ADC, f, and D* values in terms of such differentiation.

ADCtotal ratio and D ratio derived from intravoxel incoherent motion early after TACE are independent predictors for survival in hepatocellular carcinoma

PURPOSE

To explore the threshold of intravoxel incoherent motion (IVIM) parameters, apparent diffusion coefficient [ADC_total and ADC_(0,500) ] ratios 24-48 hours after transarterial chemoembolization (TACE) to assess early response in patients with unresectable hepatocellular carcinoma (HCC) and to compare the association between diffusion-weighted imaging with the intravoxel incoherent motion (IVIM-DWI) and mRECIST with survival.

MATERIALS AND METHODS

Institutional Review Board approval and informed consent were obtained for this prospective study. There were 30 patients undergoing 1.5T magnetic resonance imaging (MRI) with IVIM-DWI of 12 b values (0, 10, 20, 30, 40, 50, 70, 100, 200, 300, 500, 800 s/mm² ) 1 week before and 24-48 hours after TACE. Response was assessed with the change of true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (PF), ADC_total , and ADC_(0,500) values relative to baseline and with mRECIST. Receiver operating characteristic (ROC) curve analysis was used to explore the threshold of these parameters ratios. Kaplan-Meier, log-rank tests, and the Cox hazard model were used to correlate the response variables with progression-free survival (PFS) and to assess the incidence and potential clinical risk factors for PFS. Mann-Whitney U-test was used to compare the difference in parameters between different groups with progression within and beyond median PFS prior to TACE.

RESULTS

Median PFS was 99 days, within which 16 patients progressed. The threshold of ADC_total ratio, D ratio, and ADC_(0,500) ratio were 13.1% (P = 0.001), 7.0% (P = 0.011), and 3.6% (P = 0.018) with sensitivity and specificity of 78.6% and 87.5%, 85.7% and 62.5%, 78.6% and 75%, respectively. The predictive utility of ADC_total ratio, D ratio, and ADC_(0,500) ratio for PFS were 0.848, 0.772, and 0.754, respectively. Survival analyses showed ADC_total ratio, D ratio, ADC_(0,500) ratio, liver cirrhosis, and mRECIST had a significant effect on PFS (P < 0.05). ADC_total ratio and D ratio were independent predictors for 99-day PFS (P = 0.025, P = 0.036). There were no significant differences in pretreatment IVIM-DWI parameters between PFS > 99-day group and PFS ≤ 99-day group with P values of 0.547 for D, 0.394 for D*, 0.575 for PF, 0.901 for ADC_(0,500) , and 0.506 for ADC_total , respectively.

CONCLUSION

The ADC_total ratio and D ratio 24-48 hours after TACE were independent predictors for response to TACE for HCC, and showed stronger association with PFS than mRECIST.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:820-830.

Ultrahigh b-values MRI in normal human prostate: Initial research on reproducibility and age-related differences

PURPOSE

To investigate the reproducibility of diffusion-weighted imaging (DWI) with ultrahigh b-values, and analyze the age-related differences in normal prostates.

MATERIALS AND METHODS

In all, 67 healthy participants were divided into three age groups (group A, 15-30 years; group B, 31-50 years; group C, ≥51 years), and underwent DWI scanning twice with 15 b-factors from 0 to 3000 at 3.0T. Triexponential fits were applied to calculate the molecular diffusion coefficient (D), the pseudo-diffusion coefficient (D*), the ultrahigh apparent diffusion coefficient (ADC_uh ), and perfusion fraction (f). The interobserver and short-term interscan reproducibility were evaluated, and the change in these parameters with age were assessed.

RESULTS

The D, ADC_uh , and f values presented good to excellent reproducibility. With increasing age, a trend of increasing D values was observed, with significant difference in both peripheral zone (PZ, P = 0.01) and central gland (CG, P = 0.01) of normal prostate tissue. The f value increased in the CG beginning at 50 years of age while the ADC_uh value decreased in the PZ after 50 years of age; all of them showed significant differences between groups A and C and groups B and C (P = 0.01/0.01).

CONCLUSION

The D, ADC_uh , and f values have good to excellent reproducibility in the normal prostate, and these values change with age. The ultrahigh b-values magnetic resonance imaging (MRI) can provide additional information (ADC_uh ), which is different from the IVIM (intravoxel incoherent motion)-derived parameters.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:801-812.

Intravoxel Incoherent Motion (IVIM) Diffusion Weighted Imaging (DWI) in the Periferic Prostate Cancer Detection and Stratification

The aim of this study was to compare the Intravoxel Incoherent Motion (IVIM) parameters between healthy Peripheral Zone (PZ), Benign Prostatic Hyperplasia (BPH) and Prostate Cancer (PCa) and compare them to assess whether there was correlation with Gleason Score (GS) grading system. Thirty-one patients with suspect of PCa underwent 1.5T Multi-Parametric Magnetic Resonance Imaging (MP-MRI) with endorectal coil with a protocol including T2WI, DWI using 10 b values (0, 10, 20, 30, 50, 80, 100, 200, 400, 1000 s/mm²) and DCE. Monoexponential and IVIM model fits were used to calculate both apparent diffusion coefficient (ADC) and the following IVIM parameters: molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*) and perfusion fraction (f). The ADC and D values were significantly lower in the PCa (0.70 ± 0.16 × 10^-3 mm²/s and 0.88 ± 0.31 × 10^-3 mm²/s) compared to those found in the PZ (1.22 ± 0.20 × 10^-3 mm²/s and 1.78 ± 0.34 × 10^-3 mm²/s) and in the BPH (1.53 ± 0.23 × 10^-3 mm²/s and 1.11 ± 0.28 × 10^-3 mm²/s). The D* parameter was significantly increased in the PCa (5.35 ± 5.12 × 10^-3 mm²/s) compare to the healthy PZ (3.02 ± 2.86 × 10^-3 mm²/s), instead there was not significantly difference in the PCa compare to the BPH (5.61 ± 6.77 × 10^-3 mm²/s). The f was statistically lower in the PCa (9.01 ± 5.20%) compared to PZ (10.57 ± 9.30%), but not significantly different between PCa and BPH (9.29 ± 7.29%). The specificity, sensitivity and accuracy of T2WI associated with DWI and IVIM were higher (100, 98 and 99%, respectively) than for T2WI/DWI and IVIM alone (89, 92 and 90%, respectively). Only for ADC was found a statistical difference between low- and intermediate-/high-grade tumors. Adding IVIM to the MP-MRI could increase the diagnostic performance to detect clinically relevant PCa. ADC values have been found to have a rule to discriminate PCa reliably from normal areas and differed significantly in low- and intermediate-/high-grade PCa. In contrast, IVIM parameters were unable to distinguish between the different GS.

Intravoxel Incoherent Motion Protocol Evaluation and Data Quality in Normal and Malignant Liver Tissue and Comparison to the Literature

OBJECTIVES

Although intravoxel incoherent motion (IVIM) becomes more and more popular, there is currently no clear consensus on the number and distribution of b-values to use. In this work, we (1) tested and evaluated the data quality of a 25-b-value IVIM protocol in patients with malignant liver lesions and normal liver tissue as a standard of reference, (2) calculated an optimal b-value distribution and compared with the standard of reference, and (3) compared the 25-b-value protocol with other proposed protocols in the literature.

MATERIALS AND METHODS

Intravoxel incoherent motion imaging with 25 b-values was performed at 3 T in a total of 15 patients with malignant liver lesions. Reference IVIM parameter maps were calculated in tumor and normal liver tissue. With these parameters, optimal IVIM protocols with reduced numbers of b-values were calculated. These optimal IVIM protocols were again applied to calculate new IVIM parameter maps that were compared with the reference parameter maps by calculating mean relative errors. In addition, 35 other IVIM protocols, as found in literature, were compared in a similar way with the 25-b-value protocol serving as a standard of reference.

RESULTS

The mean relative error depends on the number of b-values and their distribution. In tumor tissue, the error is higher and more variable than in normal-appearing liver tissue. The largest errors occur in tumor tissue and in the protocols having low numbers of b-values in the IVIM protocols. In the calculated optimal IVIM protocols, the mean relative errors decreased by 40% or more when the number of b-values included increased from 4 to 16. The mean relative errors in the protocols adapted from the literature vary substantially between the various b-value distributions. One optimized 16-b-value protocol, which was found in literature, reduced the average relative error by 80% when compared with 4- and 5-b-value protocols listed in literature.

CONCLUSIONS

Including more b-values and applying an optimized b-value distribution significantly reduces errors in the IVIM parameter estimates, thereby increasing its accuracy.This effect is even more pronounced in inhomogeneous tumor compared with that in normal liver tissue. However, when restrictions in acquisition time or patient-related factors apply, a minimum of 16 b-values should be considered for reliable results.

A model describing diffusion in prostate cancer

PURPOSE

Quantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill-defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non-Gaussian behavior of tissue diffusion.

METHOD

The model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two-compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10-fold cross-validation.

RESULTS

Agreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature.

CONCLUSION

A reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316-326, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI

PURPOSE

The aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in "positive-if-all-positive" manner, from different models to localize tumors.

METHODS

A voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D*, D, f, and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65years (range 54-78) and 21.9ng/mL (range 4.84-44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0-2000s/mm² (TE=58ms; TR=3990ms; spatial resolution 2.19×2.19×2.73mm³, slices =26, FOV=140×140mm, slice gap =0.27mm, NSA=2).

RESULTS

The proposed framework detected 24 lesions of which 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%; positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients.

CONCLUSION

Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions.

Limitations and Prospects for Diffusion-Weighted MRI of the Prostate

Diffusion-weighted imaging (DWI) is the most effective component of the modern multi-parametric magnetic resonance imaging (mpMRI) scan for prostate pathology. DWI provides the strongest prediction of cancer volume, and the apparent diffusion coefficient (ADC) correlates moderately with Gleason grade. Notwithstanding the demonstrated cancer assessment value of DWI, the standard measurement and signal analysis methods are based on a model of water diffusion dynamics that is well known to be invalid in human tissue. This review describes the biophysical limitations of the DWI component of the current standard mpMRI protocol and the potential for significantly improved cancer assessment performance based on more sophisticated measurement and signal modeling techniques.

Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future

The concept of diffusion magnetic resonance (MR) imaging emerged in the mid-1980s, together with the first images of water diffusion in the human brain, as a way to probe tissue structure at a microscopic scale, although the images were acquired at a millimetric scale. Since then, diffusion MR imaging has become a pillar of modern clinical imaging. Diffusion MR imaging has mainly been used to investigate neurologic disorders. A dramatic application of diffusion MR imaging has been acute brain ischemia, providing patients with the opportunity to receive suitable treatment at a stage when brain tissue might still be salvageable, thus avoiding terrible handicaps. On the other hand, it was found that water diffusion is anisotropic in white matter, because axon membranes limit molecular movement perpendicularly to the nerve fibers. This feature can be exploited to produce stunning maps of the orientation in space of the white matter tracts and brain connections in just a few minutes. Diffusion MR imaging is now also rapidly expanding in oncology, for the detection of malignant lesions and metastases, as well as monitoring. Water diffusion is usually largely decreased in malignant tissues, and body diffusion MR imaging, which does not require any tracer injection, is rapidly becoming a modality of choice to detect, characterize, or even stage malignant lesions, especially for breast or prostate cancer. After a brief summary of the key methodological concepts beyond diffusion MR imaging, this article will give a review of the clinical literature, mainly focusing on current outstanding issues, followed by some innovative proposals for future improvements.

The expanding landscape of diffusion-weighted MRI in prostate cancer

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

Pretreatment Intra-Voxel Incoherent Motion Diffusion-Weighted Imaging (IVIM-DWI) in Predicting Induction Chemotherapy Response in Locally Advanced Hypopharyngeal Carcinoma

The aim of this study was to predict response to induction chemotherapy in patients with locally advanced hypopharyngeal carcinoma by IVIM values.Twenty-eight patients with locally advanced hypopharyngeal carcinoma underwent IVIM studies using 12 different b values (b = 0, 10, 20, 30, 50, 70 100, 150, 200, 400, 800, and 1000 s/ mm). All patients underwent 2 MRI studies: a baseline exam before any treatment and a mid-treatment exam 3 weeks after induction chemotherapy. In the IVIM approach, D, f, and D were extracted from a bi-exponential fit. For comparison, the ADC map were extracted from a mono-exponential fit. At the end of induction chemotherapy, patients were classified as responders or nonresponders group according to the Response Evaluation Criteria in Solid Tumors criteria (RECIST), based on their MRI measurement. The patients were classified into high grade group (G1), moderate grade group (G2), and low grade group (G3) according to the tumor pathological grading. The predictive value of IVIM parameters were examined with Student's t test, analysis of variance (ANOVA), and receiver operating characteristic (ROC) curves.After 2 cycles of induction chemotherapy, 18 patients were categorized into the responder group whereas the other 10 patients were considered nonresponders. Compared with the pretreatment value, the post-treatment ADC value and D value was significantly higher and the posttreatment D value was significantly lower (all P <  0.05). In contrast, post-treatment f parameter only changed slightly (P > 0.05). Compared with nonresponders, a notably lower pretreatment ADC value, D value, posttreatment D value, and higher posttreatment ADC value, D value, ΔADC, ΔD, and ΔD were observed in responders (all P < 0.05), but no significant change in Δ f among the 2 group (P > 0.05). The ROC curve analysis indicated that the cutoff of pretreatment D value in best predicting tumor's chemotherapeutic response was 0.847 × 10 mm/s, and the corresponding AUC, sensitivity, and specificity were 0.806, 75.0%, and 88.9%, respectively. Although pretreatment IVIM-derived parameters had no significant differences between high grade, moderate grade, and low grade group, a trend towards lower D was observed with increasing tumor grading from G3 to G1.IVIM-DWI can potentially predict the treatment response to induction chemotherapy for hypopharyngeal carcinoma.