Diffusion-tensor MRI at 3 T: differentiation of central gland prostate cancer from benign prostatic hyperplasia. AJR Am J Roentgenol. 2014;202:W254-W262. [PMID: 24555622 DOI: 10.2214/ajr.13.11015]

Diffusion tensor imaging and fiber tractography of the normal epididymis

PURPOSE

To evaluate the feasibility of diffusion tensor imaging (DTI) and fiber tractography (FT) of the normal epididymis and to determine normative apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values.

METHODS

Twenty-eight healthy volunteers underwent MRI of the scrotum, including DTI on a 3.0 T system. For each anatomic part of the epididymis (head, body and tail) free-hand regions of interest were drawn and the mean ADC and FA were measured by two radiologists in consensus. Parametric statistical tests were used to determine intersubject differences in ADC and FA between the anatomic parts of each normal epididymis and between bilateral epididymides. Fiber tracts of the epididymis were reconstructed using the MR Diffusion tool.

RESULTS

The mean ADC and FA of the normal epididymis was 1.31 × 10-3 mm2/s and 0.20, respectively. No differences in ADC (p = 0.736) and FA (p = 0.628) between the anatomic parts of each normal epididymis were found. Differences (p = 0.020) were observed in FA of the body between the right and the left epididymis. FT showed the fiber tracts of the normal epididymis. Main study's limitations include the following: small number of participants with narrow age range, absence of histologic confirmation and lack of quantitative assessment of the FT reconstructions.

CONCLUSION

DTI and FT of the normal epididymis is feasible and allow the noninvasive assessment of the structural and geometric organization of the organ.

Multiparametric Magnetic Resonance Imaging With Comprehensive Assessment of Prostate Volume, Morphology, and Composition Better Reflects the Correlation With International Prostate Symptom Score

OBJECTIVE

To investigate the application of multiparametric magnetic resonance imaging (mp-MRI) for comprehensive evaluation of the correlation between the characteristics of the transitional zone and the International Prostate Symptom Score (IPSS) in patients with benign prostatic hyperplasia (BPH).

MATERIALS AND METHODS

We retrospectively recruited 210 patients with biopsy-proven BPH who underwent preoperative mp-MRI and were assigned an IPSS. The evaluation indicators included prostate volumetric parameters (total prostate volume [TPV], transition zone volume [TZV], and transition zone index [TZI, TZI=TZV/TPV]), prostate morphological parameters (intravesical prostatic protrusion, and presumed circle area ratio) and prostate compositional parameters (apparent diffusion coefficient [ADC], and mean signal intensity of T2WI [mean-SI-T2WI]). The Pearson (r) correlation coefficient, one-way analysis of variance, and multiple linear regression analysis were used to build a regression model for evaluating the correlation between MRI-derived parameters and IPSS, IPSS-storage symptom, IPSS-voiding symptom.

RESULTS

Significant correlations were found between IPSS, IPSS-storage symptom, IPSS-voiding symptom, and prostate MRI-derived parameters, including TPV (r = 0.350; r = 0.466; r = 0.225, P < .001), TZV (r = 0.374; r = 0.492; r = 0.243, P < .001), TZI (r = 0.383; r = 0.313; r = 0.354, P < .001), presumed circle area ratio (r = 0.481; r = 0.356; r = 0.469, P < .001), ADC(r = -0.198; r = -0.053; r = -0.239, P < .05) and mean-SI-T2WI (r = -0.626; r = -0.310; r = -0.687, P < .001), respectively. Based on multiple linear regression analysis, the impact of mean-SI-T2WI and TZI on total IPSS were statistically significant (P < .05), and the regression equation established with the analysis (IPSS= 39.224 + 8.469 ×TZI+ (-0.09)× (mean-SI-T2WI)) was statistically significant (F=104.995, P < .001).

CONCLUSION

Mp-MRI could be used to evaluate the volume and morphology of BPH. In particular, mean-SI-T2WI and ADC could be used to describe the internal composition of the prostate. The imaging parameters were effective for evaluating BPH.

Relationship between Apparent Diffusion Coefficient Distribution and Cancer Grade in Prostate Cancer and Benign Prostatic Hyperplasia

The aim of this paper was to assess the associations between prostate cancer aggressiveness and histogram-derived apparent diffusion coefficient (ADC) parameters and determine which ADC parameters may help distinguish among stromal hyperplasia (SH), glandular hyperplasia (GH), and low-grade, intermediate-grade, and high-grade prostate cancers. The mean, median, minimum, maximum, and 10th and 25th percentile ADC values were determined from the ADC histogram and compared among two benign prostate hyperplasia (BPH) groups and three Gleason score (GS) groups. Seventy lesions were identified in 58 patients who had undergone proctectomy. Thirty-nine lesions were prostate cancers (GS 6 = 7 lesions, GS 7 = 19 lesions, GS 8 = 11 lesions, GS 9 = 2 lesions), and thirty-one lesions were BPH (SH = 15 lesions, GH = 16 lesions). There were statistically significant differences in 10th percentile and 25th percentile ADC values when comparing GS 6 to GS 7 (p < 0.05). The 10th percentile ADC values yielded the highest area under the curve (AUC). Tenth and 25th percentile ADCs can be used to more accurately differentiate lesions with GS 6 from those with GS 7 than other ADC parameters. Our data indicate that the major challenge with ADC mapping is to differentiate between SH and GS 6, and SH and GS 7.

Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ₁) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system

The emergence of diffusion, structural, and functional neuroimaging methods has enabled major multi-site efforts to map the human connectome, which has heretofore been defined as containing all neural connections in the central nervous system (CNS). However, these efforts are not structured to examine the richness and complexity of the peripheral nervous system (PNS), which arguably forms the (neglected) rest of the connectome. Despite increasing interest in an atlas of the spinal cord (SC) and PNS which is simultaneously stereotactic, interactive, electronically dissectible, scalable, population-based and deformable, little attention has thus far been devoted to this task of critical importance. Nevertheless, the atlasing of these complete neural structures is essential for neurosurgical planning, neurological localization, and for mapping those components of the human connectome located outside of the CNS. Here we recommend a modification to the definition of the human connectome to include the SC and PNS, and argue for the creation of an inclusive atlas to complement current efforts to map the brain's human connectome, to enhance clinical education, and to assist progress in neuroscience research. In addition to providing a critical overview of existing neuroimaging techniques, image processing methodologies and algorithmic advances which can be combined for the creation of a full connectome atlas, we outline a blueprint for ultimately mapping the entire human nervous system and, thereby, for filling a critical gap in our scientific knowledge of neural connectivity.

Prostate MRI: practical guidelines for interpreting and reporting according to PI-RADS version 2.1

The increasing precision of multiparametric magnetic resonance imaging of the prostate, together with greater experience and standardization in its interpretation, has given this technique an important role in the management of prostate cancer, the most prevalent non-cutaneous cancer in men. This article reviews the concepts in PI-RADS version 2.1 for estimating the probability and zonal location of significant tumors of the prostate, using a practical approach that includes current considerations about the prerequisites for carrying out the test and recommendations for interpreting the findings. It emphasizes benign findings that can lead to confusion and the criteria for evaluating the probability of local spread, which must be included in the structured report.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

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

Purpose

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

Materials and Methods

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

Results

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

Conclusion

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

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.

Diffusion tensor imaging beyond brains: Applications in abdominal and pelvic organs

Functional magnetic resonance imaging (MRI) provided critical functional information in addition to the anatomic profiles offered by conventional MRI, and has been enormously used in the initial diagnosis and followed evaluation of various diseases. Diffusion tensor imaging (DTI) is a newly developed and advanced technique that measures the diffusion properties including both diffusion motion and its direction in situ, and has been extensively applied in central nerve system with acknowledged success. Technical advances have enabled DTI in abdominal and pelvic organs. Its application is increasing, yet remains less understood. A systematic overview of clinical application of DTI in abdominal and pelvic organs such as liver, pancreas, kidneys, prostate, uterus, etc., is therefore presented. Exploration of techniques with less artifacts and more normative post-processing enabled generally satisfactory image quality and repeatability of measurement. DTI appears to be more valuable in the evaluation of diffused diseases of organs with highly directionally arranged structures, such as the assessment of function impairment of native and transplanted kidneys. However, the utility of DTI to diagnose focal lesions, such as liver mass, pancreatic and prostate tumor, remains limited. Besides, diffusion of different layers of the uterus and the fiber structure disruption can be depicted by DTI. Finally, a discussion of future directions of research is given. The underlying heterogeneous pathologic conditions of certain diseases need to be further differentiated, and it is suggested that DTI parameters might potentially depict certain pathologic characterization such as cell density. Nevertheless, DTI should be better integrated into the current multi-modality evaluation in clinical practice.

Stiffness of benign and malignant prostate tissue measured by shear-wave elastography: a preliminary study

OBJECTIVES

To measure benign and malignant prostate tissue stiffness using shear-wave elastography (SWE).

METHODS

Thirty consecutive patients underwent transrectal SWE in the axial and sagittal planes before prostatectomy. After reviewing prostatectomy specimens, two radiologists measured stiffness in regions corresponding to cancers, lateral and median benign peripheral zone (PZ) and benign transition zone (TZ).

RESULTS

Cancers were stiffer than benign PZ and TZ. All tissue classes were stiffer on sagittal than on axial imaging, in TZ than in PZ, and in median PZ than in lateral PZ. At multivariate analysis, the nature of tissue (benign or malignant; P < 0.00001), the imaging plane (axial or sagittal; P < 0.00001) and the location within the prostate (TZ, median PZ or lateral PZ; P = 0.0065) significantly and independently influenced tissue stiffness. On axial images, the thresholds maximising the Youden index in TZ, lateral PZ and median PZ were respectively 62 kPa, 33 kPa and 49 kPa. On sagittal images, the thresholds were 76 kPa, 50 kPa and 72 kPa, respectively.

CONCLUSIONS

SWE can distinguish prostate malignant and benign tissues. Tissue stiffness is influenced by the imaging plane and the location within the gland.

KEY POINTS

• Prostate cancers were stiffer than the benign peripheral zone • All tissue classes were stiffer on sagittal than on axial imaging • All tissue classes were stiffer in the transition zone than in the peripheral zone • All tissue classes were stiffer in the median than in the lateral peripheral zone • Taking into account imaging plane and zonal anatomy can improve cancer detection.

Diffusion tensor imaging in abdominal organs

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

In vivo MRI based prostate cancer localization with random forests and auto-context model

Prostate cancer is one of the major causes of cancer death for men. Magnetic resonance (MR) imaging is being increasingly used as an important modality to localize prostate cancer. Therefore, localizing prostate cancer in MRI with automated detection methods has become an active area of research. Many methods have been proposed for this task. However, most of previous methods focused on identifying cancer only in the peripheral zone (PZ), or classifying suspicious cancer ROIs into benign tissue and cancer tissue. Few works have been done on developing a fully automatic method for cancer localization in the entire prostate region, including central gland (CG) and transition zone (TZ). In this paper, we propose a novel learning-based multi-source integration framework to directly localize prostate cancer regions from in vivo MRI. We employ random forests to effectively integrate features from multi-source images together for cancer localization. Here, multi-source images include initially the multi-parametric MRIs (i.e., T2, DWI, and dADC) and later also the iteratively-estimated and refined tissue probability map of prostate cancer. Experimental results on 26 real patient data show that our method can accurately localize cancerous sections. The higher section-based evaluation (SBE), combined with the ROC analysis result of individual patients, shows that the proposed method is promising for in vivo MRI based prostate cancer localization, which can be used for guiding prostate biopsy, targeting the tumor in focal therapy planning, triage and follow-up of patients with active surveillance, as well as the decision making in treatment selection. The common ROC analysis with the AUC value of 0.832 and also the ROI-based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method.

Benign causes of diffusion restriction foci in the peripheral zone of the prostate: diagnosis and differential diagnosis

Multiparametric-MRI is an important tool in the diagnosis of prostate cancer (PCa), particularly diffusion-weighted imaging for peripheral zone (PZ) cancer in the untreated prostate. However, there are many benign entities that demonstrate diffusion restriction in the PZ mimicking PCa resulting in diagnostic challenges. Fortunately, these benign entities usually have unique MR features that may help to distinguish them from PCa. The purpose of this pictorial review is to discuss benign entities with diffusion restriction in the PZ and to emphasize the key MR features of these entities that may help to differentiate them from PCa.

Assessing Detection, Discrimination, and Risk of Breast Cancer According to Anisotropy Parameters of Diffusion Tensor Imaging

BACKGROUND The aim of this study was to investigate whether the anisotropy parameters are helpful in the detection and discrimination of breast cancers, and to determine its value in predicting the risk of cancers. MATERIAL AND METHODS There were 56 patients with 56 lesions (34 malignant, 22 benign) included in the study. DTI was performed in every patient and apparent diffusion coefficient (ADC), fractional anisotropy (FA), and eigenvalues E1, E2, and E3 were measured in every lesion and the normal breast tissue. RESULTS ADC, FA, and eigenvalues of E1, E2, E3, and E1-E3 in breast cancers were all significantly lower than in normal tissue (P<0.001 for all) with mean reduction of (32 ± 17)%, (24 ± 13)%, (33 ± 19)%, (32 ± 17)%, (31 ± 18)%, and (37 ± 20)% for ADC, FA, E1, E2, E3, and E1-E3, respectively. These parameters were also statistically lower in cancers than in benign lesions (P<0.01 for all), except FA (P>0.05). ADC, E1, E2, and E3 were very similar in discriminating breast cancers and benign lesions, with area under the curve (AUC) 0.885-0.898, sensitivity 73.5-85.3%, and specificity 90.9-100%. CONCLUSIONS ADC, E1, E2, E3, and E1-E3 are much lower in breast cancers than in normal tissue and benign lesions. The reduction of ADC, E1, E2, E3, and E1-E3 of a mass in the breast is highly associated with the risk of breast cancer, but the FA has no utility in breast cancer risk prediction.

Diffusion anisotropy in fresh and fixed prostate tissue ex vivo

PURPOSE

To investigate diffusion anisotropy in whole human prostate specimens

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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