In vivo prostate cancer detection and grading using restriction spectrum imaging-MRI

Tri-Compartmental Restriction Spectrum Imaging Based on 18F-FDG PET/MR for Identification of Primary Benign and Malignant Lung Lesions

BACKGROUND

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions.

OBJECTIVE

To explore how well the tri-compartmental RSI performs in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy.

STUDY TYPE

Prospective.

POPULATION

137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years).

FIELD STRENGTH/SEQUENCE

T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system.

ASSESSMENT

The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusionf 1 $$ {f}_1 $$ , hindered diffusionf 2 $$ {f}_2 $$ , and free diffusionf 3 $$ {f}_3 $$ ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant.

RESULTS

Thef 1 $$ {f}_1 $$ , SUVmax were significantly higher, andf 3 $$ {f}_3 $$ , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC (f 3 $$ {f}_3 $$ ) > AUC (f 1 $$ {f}_1 $$ ) > AUC (ADC) > AUC (f 2 $$ {f}_2 $$ ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging.

CONCLUSION

RSI-derived parameters (f 1 $$ {f}_1 $$ ,f 3 $$ {f}_3 $$ ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility off 2 $$ {f}_2 $$ was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy.

LEVEL OF EVIDENCE: 2

TECHNICAL EFFICACY

Stage 2.

MRI-based virtual pathology of the prostate

Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.

Diagnostic Utility of Restriction Spectrum Imaging in Head and Neck Tumors: A Pilot Study

OBJECTIVE

Imaging is crucial in the assessment of head and neck cancers for site, extension, and enlarged lymph nodes. Restriction spectrum imaging (RSI) is a new diffusion-weighted magnetic resonance imaging (MRI) technique that enhances the ability to differentiate aggressive cancer from low-grade or benign tumors and helps guide treatment and biopsy. Its contribution to imaging of brain and prostate tumors has been previously published. However, there are no prior studies using RSI sequence in head and neck tumors. The purpose of this study was to evaluate the feasibility of performing RSI in head and neck cancer.

METHODS

An additional RSI sequence was added in the routine MRI neck protocol for 13 patients diagnosed with head and neck cancer between November 2018 and April 2019. Restriction spectrum imaging sequence was performed with b values of 0, 500, 1500, and 3000 s/mm 2 and 29 directions on 1.5T magnetic resonance scanners.Diffusion-weighted imaging (DWI) images and RSI images were compared according to their ability to detect the primary malignancy and possible metastatic lymph nodes.

RESULTS

In 71% of the patients, RSI outperformed DWI in detecting the primary malignancy and possible metastatic lymph nodes, whereas in the remaining cases, the 2 were comparable. In 66% of the patients, RSI detected malignant lymph nodes that DWI/apparent diffusion coefficient failed to detect.

CONCLUSIONS

This is the first study of RSI in head and neck imaging and showed its superiority over the conventional DWI sequence. Because of its ability to differentiate benign and malignant lymph nodes in some cases, the addition of RSI to routine head and neck MRI should be considered.

Improving reader accuracy and specificity with the addition of hybrid multidimensional-MRI to multiparametric-MRI in diagnosing clinically significant prostate cancers

PURPOSE

Compare reader performance when adding the Hybrid Multidimensional-MRI (HM-MRI) map to multiparametric MRI (mpMRI+HM-MRI) versus mpMRI alone and inter-reader agreement in diagnosing clinically significant prostate cancers (CSPCa).

METHODS

All 61 patients who underwent mpMRI (T2-, diffusion-weighted (DWI), and contrast-enhanced scans) and HM-MRI (with multiple TE/b-value combinations) before prostatectomy or MRI-fused-transrectal ultrasound-guided biopsy between August, 2012 and February, 2020, were retrospectively analyzed. Two experienced readers (R1, R2) and two less-experienced readers (less than 6-year MRI prostate experience) (R3, R4) interpreted mpMRI without/with HM-MRI in the same sitting. Readers recorded the PI-RADS 3-5 score, lesion location, and change in score after adding HM-MRI. Each radiologist's mpMRI+HM-MRI and mpMRI performance measures (AUC, sensitivity, specificity, PPV, NPV, and accuracy) based on pathology, and Fleiss' kappa inter-reader agreement was calculated and compared.

RESULTS

Per-sextant R3 and R4 mpMRI+HM-MRI accuracy (82% 81% vs. 77%, 71%; p=.006, <.001) and specificity (89%, 88% vs. 84%, 75%; p=.009, <.001) were higher than with mpMRI. Per-patient R4 mpMRI+HM-MRI specificity improved (48% from 7%; p<.001). R1 and R2 mpMRI+HM-MRI specificity per-sextant (80%, 93% vs. 81%, 93%; p=.51,>.99) and per-patient (37%, 41% vs. 48%, 37%; p=.16, .57) remained similar to mpMRI. R1 and R2 per-patient AUC with mpMRI+HM-MRI (0.63, 0.64 vs. 0.67, 0.61; p=.33, .36) remained similar to mpMRI, but R3 and R4 mpMRI+HM-MRI AUC (0.73, 0.62) approached R1 and R2 AUC. Per-patient inter-reader agreement, mpMRI+HM-MRI Fleiss Kappa, was higher than mpMRI (0.36 [95% CI 0.26, 0.46] vs. 0.17 [95% CI 0.07, 0.27]); p=.009).

CONCLUSION

Adding HM-MRI to mpMRI (mpMRI+HM-MRI) improved specificity and accuracy for less-experienced readers, improving overall inter-reader agreement.

Comparing Radiologist Performance in Diagnosing Clinically Significant Prostate Cancer with Multiparametric versus Hybrid Multidimensional MRI

Background Variability of acquisition and interpretation of prostate multiparametric MRI (mpMRI) persists despite implementation of the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 due to the range of reader experience and subjectivity of lesion characterization. A quantitative method, hybrid multidimensional MRI (HM-MRI), may introduce objectivity. Purpose To compare performance, interobserver agreement, and interpretation time of radiologists using mpMRI versus HM-MRI to diagnose clinically significant prostate cancer. Materials and Methods In this retrospective analysis, men with prostatectomy or MRI-fused transrectal US biopsy-confirmed prostate cancer underwent mpMRI (triplanar T2-weighted, diffusion-weighted, and dynamic contrast-enhanced imaging) and HM-MRI (with multiple echo times and b value combinations) from August 2012 to February 2020. Four readers with 1-20 years of experience interpreted mpMRI and HM-MRI examinations independently, with a 4-week washout period between interpretations. PI-RADS score, lesion location, and interpretation time were recorded. mpMRI and HM-MRI interpretation time, interobserver agreement (Cronbach alpha), and performance of area under the receiver operating characteristic curve (AUC) analysis were compared for each radiologist with use of bootstrap analysis. Results Sixty-one men (mean age, 61 years ± 8 [SD]) were evaluated. Per-patient AUC was higher for HM-MRI for reader 4 compared with mpMRI (AUCs for readers 1-4: 0.61, 0.71, 0.59, and 0.64 vs 0.66, 0.60, 0.50, and 0.46; P = .57, .20, .32, and .04, respectively). Per-patient specificity was higher for HM-MRI for readers 2-4 compared with mpMRI (specificity for readers 1-4: 48%, 78%, 48%, and 46% vs 37%, 26%, 0%, and 7%; P = .34, P < .001, P < .001, and P < .001, respectively). Diagnostic performance improved for the reader least experienced with HM-MRI, reader 4 (AUC, 0.64 vs 0.46; P = .04). HM-MRI interobserver agreement (Cronbach alpha = 0.88 [95% CI: 0.82, 0.92]) was higher than that of mpMRI (Cronbach alpha = 0.26 [95% CI: 0.10, 0.52]; α > .60 indicates reliability; P = .03). HM-MRI mean interpretation time (73 seconds ± 43 [SD]) was shorter than that of mpMRI (254 seconds ± 133; P = .03). Conclusion Radiologists had similar or improved diagnostic performance, higher interobserver agreement, and lower interpretation time for clinically significant prostate cancer with hybrid multidimensional MRI than multiparametric MRI. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Diffusion-weighted imaging in prostate cancer

Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging

BACKGROUND

Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI₄ ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI₄ -C₁ , yielded greatest tumor conspicuity.

PURPOSE

To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI₄ -C₁ ) as a quantitative voxel-level classifier of PCa.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.

FIELD STRENGTH/SEQUENCE

A 3 T, multishell diffusion-weighted and axial T2-weighted sequences.

ASSESSMENT

High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI₄ -C₁ and conventional ADC. Classifier images were also generated.

STATISTICAL TESTS

Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI₄ -C₁ was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR₉₀ ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.

RESULTS

RSI₄ -C₁ outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR₉₀ (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).

DATA CONCLUSION

RSI₄ -C₁ yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

New prostate MRI techniques and sequences

Prostate MRI has seen increasing interest in recent years and has led to the development of new MRI techniques and sequences to improve prostate cancer (PCa) diagnosis which are reviewed in this article. Numerous studies have focused on improving image quality (segmented DWI) and faster acquisition (compressed sensing, k-t-SENSE, PROPELLER). An increasing number of studies have developed new quantitative and computer-aided diagnosis methods including artificial intelligence (PROSTATEx challenge) that mitigate the subjective nature of mpMRI interpretation. MR fingerprinting allows rapid, simultaneous generation of quantitative maps of multiple physical properties (T1, T2), where PCa are characterized by lower T1 and T2 values. New techniques like luminal water imaging (LWI), restriction spectrum imaging (RSI), VERDICT and hybrid multi-dimensional MRI (HM-MRI) have been developed for microstructure imaging, which provide information similar to histology. The distinct MR properties of tissue components and their change with the presence of cancer is used to diagnose prostate cancer. LWI is a T2-based imaging technique where long T2-component corresponding to luminal water is reduced in PCa. RSI and VERDICT are diffusion-based techniques where PCa is characterized by increased signal from intra-cellular restricted water and increased intracellular volume fraction, respectively, due to increased cellularity. VERDICT also reveal loss of extracellular-extravascular space in PCa due to loss of glandular structure. HM-MRI measures volumes of prostate tissue components, where PCa has reduced lumen and stromal and increased epithelium volume similar to results shown in histology. Similarly, molecular imaging using hyperpolarized ¹³C imaging has been utilized.

Precision Medicine: Functional Advancements

Precision medicine was conceptualized on the strength of genomic sequence analysis. High-throughput functional metrics have enhanced sequence interpretation and clinical precision. These technologies include metabolomics, magnetic resonance imaging, and I rhythm (cardiac monitoring), among others. These technologies are discussed and placed in clinical context for the medical specialties of internal medicine, pediatrics, obstetrics, and gynecology. Publications in these fields support the concept of a higher level of precision in identifying disease risk. Precise disease risk identification has the potential to enable intervention with greater specificity, resulting in disease prevention-an important goal of precision medicine.

Utility of Restriction Spectrum Imaging Among Men Undergoing First-Time Biopsy for Suspected Prostate Cancer

OBJECTIVE. The purpose of this article is to evaluate restriction spectrum imaging (RSI) in men undergoing MRI-ultrasound fusion biopsy for suspected prostate cancer (PCa) and to compare the performance of RSI with that of conventional DWI. MATERIALS AND METHODS. One hundred ninety-eight biopsy-naïve men enrolled in a concurrent prospective clinical trial evaluating MRI-targeted prostate biopsy underwent multiparametric MRI with RSI. Clinical and imaging features were compared between men with and without clinically significant (CS) PCa (MRI-ultrasound fusion biopsy Gleason score ≥ 3 + 4). RSI z score and apparent diffusion coefficient (ADC) were correlated, and their diagnostic performances were compared. RESULTS. CS PCa was detected in 109 of 198 men (55%). Using predefined thresholds of ADC less than or equal to 1000 μm²/s and RSI z score greater than or equal to 3, sensitivity and specificity for CS PCa were 86% and 38%, respectively, for ADC and 61% and 70%, respectively, for RSI. In the transition zone (n = 69), the sensitivity and specificity were 94% and 17%, respectively, for ADC and 59% and 69%, respectively, for RSI. Among lesions with CS PCa, RSI z score and ADC were significantly inversely correlated in the peripheral zone (ρ = -0.4852; p < 0.01) but not the transition zone (ρ = -0.2412; p = 0.17). Overall diagnostic accuracies of RSI and DWI were 0.70 and 0.68, respectively (p = 0.74). CONCLUSION. RSI and DWI achieved equivalent diagnostic performance for PCa detection in a large population of men undergoing first-time prostate biopsy for suspected PCa, but RSI had superior specificity for transition zone lesions.

Voxel Level Radiologic-Pathologic Validation of Restriction Spectrum Imaging Cellularity Index with Gleason Grade in Prostate Cancer

PURPOSE

Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions.

EXPERIMENTAL DESIGN

Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented as normalized z-score maps. In total, 2,795 tiles were analyzed and compared with RSI-MRI cellularity.

RESULTS

RSI-MRI cellularity index was found to distinguish between prostate cancer and benign tumor (t = 25.48, P < 0.00001). Significant differences were also found between benign tissue and prostate cancer classified as low-grade (GG = 3; t = 11.56, P < 0.001) or high-grade (GG ≥ 4; t = 24.03, P < 0.001). Furthermore, RSI-MRI differentiated between low and high-grade prostate cancer (t = 3.23; P = 0.003).

CONCLUSIONS

Building on our previous findings of correlation between GG and the RSI-MRI among whole tumors, our current study reveals a similar correlation at voxel resolution within tumors. Because it can detect variations in tumor grade with voxel-level precision, RSI-MRI may become an option for planning targeted procedures where identifying the area with the most aggressive disease is important. Clin Cancer Res; 22(11); 2668-74. ©2016 AACR.

Technique of Multiparametric MR Imaging of the Prostate

Multiparametric MR imaging provides detailed anatomic assessment of the prostate as well as information that allows the detection and characterization of prostate cancer. To obtain high-quality MR imaging of the prostate, radiologists must understand sequence optimization to overcome commonly encountered technical challenges. This review discusses the techniques that are used in state-of-the-art MR imaging of the prostate, including imaging protocols, hardware considerations, and important aspects of patient preparation, with an emphasis on the recommendations provided in the prostate imaging-reporting and data system version 2 guidelines.

Restriction Spectrum Imaging Improves Risk Stratification in Patients with Glioblastoma

BACKGROUND AND PURPOSE

ADC as a marker of tumor cellularity has been promising for evaluating the response to therapy in patients with glioblastoma but does not successfully stratify patients according to outcomes, especially in the upfront setting. Here we investigate whether restriction spectrum imaging, an advanced diffusion imaging model, performed after an operation but before radiation therapy, could improve risk stratification in patients with newly diagnosed glioblastoma relative to ADC.

MATERIALS AND METHODS

Pre-radiation therapy diffusion-weighted and structural imaging of 40 patients with glioblastoma were examined retrospectively. Restriction spectrum imaging and ADC-based hypercellularity volume fraction (restriction spectrum imaging-FLAIR volume fraction, restriction spectrum imaging-contrast-enhanced volume fraction, ADC-FLAIR volume fraction, ADC-contrast-enhanced volume fraction) and intensities (restriction spectrum imaging-FLAIR 90th percentile, restriction spectrum imaging-contrast-enhanced 90th percentile, ADC-FLAIR 10th percentile, ADC-contrast-enhanced 10th percentile) within the contrast-enhanced and FLAIR hyperintensity VOIs were calculated. The association of diffusion imaging metrics, contrast-enhanced volume, and FLAIR hyperintensity volume with progression-free survival and overall survival was evaluated by using Cox proportional hazards models.

RESULTS

Among the diffusion metrics, restriction spectrum imaging-FLAIR volume fraction was the strongest prognostic metric of progression-free survival (P = .036) and overall survival (P = .007) in a multivariate Cox proportional hazards analysis, with higher values indicating earlier progression and shorter survival. Restriction spectrum imaging-FLAIR 90th percentile was also associated with overall survival (P = .043), with higher intensities, indicating shorter survival. None of the ADC metrics were associated with progression-free survival/overall survival. Contrast-enhanced volume exhibited a trend toward significance for overall survival (P = .063).

CONCLUSIONS

Restriction spectrum imaging-derived cellularity in FLAIR hyperintensity regions may be a more robust prognostic marker than ADC and conventional imaging for early progression and poorer survival in patients with glioblastoma. However, future studies with larger samples are needed to explore its predictive ability.

Imaging of local recurrence in prostate cancer

Diagnosis of prostate cancer (PCa) recurrence after therapy with curative intent currently depends primarily on biochemical serum analyses. When recurrence is suspected, further treatment decisions rely heavily on the confirmation of disease presence and determination of its extent. This is complicated by the fact that benign conditions can mimic biochemical recurrence, and serum studies do not reliably discriminate between local and distant recurrence. This review discusses the contemporary imaging paradigm for the evaluation of local PCa recurrence. The multidisciplinary implications for urologists, radiation oncologists and radiologists are examined. Emerging techniques and future directions of PCa imaging research are discussed.

Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI

Restriction spectrum imaging (RSI) is a novel diffusion-weighted MRI technique that uses the mathematically distinct behavior of water diffusion in separable microscopic tissue compartments to highlight key aspects of the tissue microarchitecture with high conspicuity. RSI can be acquired in less than 5 min on modern scanners using a surface coil. Multiple field gradients and high b-values in combination with postprocessing techniques allow the simultaneous resolution of length-scale and geometric information, as well as compartmental and nuclear volume fraction filtering. RSI also uses a distortion correction technique and can thus be fused to high resolution T2-weighted images for detailed localization, which improves delineation of disease extension into critical anatomic structures. In this review, we discuss the acquisition, postprocessing, and interpretation of RSI for prostate MRI. We also summarize existing data demonstrating the applicability of RSI for prostate cancer detection, in vivo characterization, localization, and targeting.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2017;45:323-336.