Restriction spectrum imaging improves MRI-based prostate cancer detection

Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model

BACKGROUND

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF).

PURPOSE

To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model.

METHODS

Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF.

RESULTS

According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471 × 10-3, 1.653 × 10-3, 9.487 × 10-3, and > 30 × 10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002).

CONCLUSION

The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

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.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

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.

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.

Quantitative Assessment of Restriction Spectrum MR Imaging for the Diagnosis of Breast Cancer and Association With Prognostic Factors

BACKGROUND

Restriction spectrum imaging (RSI) is an advanced quantitative diffusion-weighted magnetic resonance imaging (DWI) technique to assess breast cancer.

PURPOSE

To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions.

FIELD STRENGTH/SEQUENCE

3-T; RSI-based DWI sequence with echo-planar imaging technique.

ASSESSMENT

The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f₁ , hindered diffusion f₂ , free diffusion f₃ , and signal fractions f₁ f₂ ) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer.

STATISTICAL TESTS

Mann-Whitney U test or Student's t-test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P-value <0.05 was considered statistically significant.

RESULTS

Malignant breast lesions showed significantly lower ADC and f₃ values, and significantly higher f₁ and f₁ f₂ values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI-derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC = 0.973). The f₃ value was significantly differed between estrogen receptor (ER)-positive and ER-negative tumors. The ADC, f₁ , f₃ , and f₁ f₂ values were significantly different progesterone receptor (PR)-positive and PR-negative status.

DATA CONCLUSION

The RSI-derived parameters (f₁ , f₃ , and f₁ f₂ ) may facilitate the differential diagnosis between benign and malignant breast lesions.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

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

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

Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists' measures

PURPOSE

To validate prostate tissue composition measured using hybrid multi-dimensional MRI (HM-MRI) by comparing with reference standard (ground truth) results from pathologists' interpretation of clinical histopathology slides following whole mount prostatectomy.

MATERIALS AND METHODS

36 prospective participants with biopsy-confirmed prostate cancer underwent 3 T MRI prior to radical prostatectomy. Axial HM-MRI was acquired with all combinations of echo times of 57, 70, 150, 200 ms and b-values of 0, 150, 750, 1500 s/mm² and data were fitted using a 3-compartment signal model using custom software to generate volumes for each tissue component (stroma, epithelium, lumen). Three experienced genitourinary pathologists independently as well as in consensus reviewed each histology image and provide an estimate of percentage of epithelium and lumen for regions-of-interest corresponding to MRI (n = 165; 64 prostate cancers and 101 benign tissue). Agreement statistics using total deviation index (TDI_0.9) was performed for tissue composition measured using HM-MRI and reference standard results from pathologists' consensus.

RESULTS

Based on the initial results showing typical variation among pathologists TDI_0.9 = 25%, we determined we will declare acceptable agreement if the 95% one-sided upper confident limit of TDI_0.9 is less than 30%. The results of tissue composition measurement from HM-MRI compared to ground truth results from the consensus of 3 pathologists, reveal that ninety percent of absolute paired differences (TDI_0.9) were within 18.8% and 22.4% in measuring epithelium and lumen, respectively. We are 95% confident that 90% of absolute paired differences were within 20.6% and 24.2% in measuring epithelium and lumen, respectively. These were less than our criterion of 30% and inter-pathologists' agreement (22.3% for epithelium and 24.2% for lumen) and therefore we accept the agreement performance of HM-MRI. The results revealed excellent area under the ROC curve for differentiating cancer from benign tissue based on epithelium (HM-MRI: 0.87, pathologists: 0.97) and lumen volume (HM-MRI: 0.85, pathologists: 0.77).

CONCLUSION

The agreement in tissue composition measurement using hybrid multidimensional MRI and consensus of pathologists is on par with the inter-raters (pathologists) agreement.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

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.

Prostate magnetic resonance imaging technique

Multiparametric magnetic resonance (MR) imaging of the prostate is an excellent tool to detect clinically significant prostate cancer, and it has widely been incorporated into clinical practice due to its excellent tissue contrast and image resolution. The aims of this article are to describe the prostate MR imaging technique for detection of clinically significant prostate cancer according to PI-RADS v2.1, as well as alternative sequences and basic aspects of patient preparation and MR imaging artifact avoidance.

Magnetic Resonance Imaging for the Detection of High Grade Cancer in the Canary Prostate Active Surveillance Study

PURPOSE

We investigated the ability of prostate magnetic resonance imaging to detect Gleason Grade Group 2 or greater cancer in a standardized, multi-institutional active surveillance cohort.

MATERIALS AND METHODS

We evaluated men enrolled in Canary Prostate Active Surveillance Study with Gleason Grade Group less than 2 and who underwent biopsy within 12 months of multiparametric magnetic resonance imaging. Our primary outcome was biopsy reclassification to Gleason Grade Group 2 or greater. We evaluated the performance of magnetic resonance imaging PI-RADS® score and clinical factors. Multivariable logistic regression models were fit with magnetic resonance imaging and clinical factors and used to perform receiver operating curve analyses.

RESULTS

There were 361 participants with 395 prostate magnetic resonance imaging studies with a median followup of 4.1 (IQR 2.0-7.6) years. Overall 108 (27%) biopsies showed reclassification. Defining positive magnetic resonance imaging as PI-RADS 3-5, the negative predictive value and positive predictive value for detecting Gleason Grade Group 2 or greater cancer was 83% (95% CI 76-90) and 31% (95% CI 26-37), respectively. PI-RADS was significantly associated with reclassification (PI-RADS 5 vs 1 and 2 OR 2.71, 95% CI 1.21-6.17, p=0.016) in a multivariable model but did not improve upon a model with only clinical factors (AUC 0.768 vs 0.762). In 194 fusion biopsies higher grade cancer was found in targeted cores in 21 (11%) instances, while 25 (13%) had higher grade cancer in the systematic cores.

CONCLUSIONS

This study adds the largest cohort data to the body of literature for magnetic resonance imaging in active surveillance, recommending systematic biopsy in patients with negative magnetic resonance imaging and the inclusion of systematic biopsy in patients with positive magnetic resonance imaging.

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.

An improved model for prostate diffusion incorporating the results of Monte Carlo simulations of diffusion in the cellular compartment

The purpose of this work was to refine a previously published model of prostate diffusion by incorporating improved estimates of cellular diffusivity obtained by Monte Carlo simulation. Stromal and epithelial cell size and intracellular volume fraction in different grades of cancer were determined from histological images. Diffusion in different mixtures of cells, corresponding to different tumor grades, was simulated and cellular apparent diffusion coefficient and kurtosis values determined. These values were incorporated into the previously published model of prostate diffusion and model predictions compared with values found in the literature. Stromal cell radius and intracellular volume fraction were 3.74 ± 0.96 μm and 13 ± 3% respectively in normal peripheral zone (PZ), and were similar in all grades of cancer. Epithelial cell radius and intracellular volume fraction were 3.40 ± 0.15 μm and 45 ± 5% respectively in normal PZ, rising to 4.75 ± 0.20 μm and 70 ± 8% in high grade cancer. Cellular apparent diffusion coefficient and kurtosis were 1.02 μm² ms^-1 and 0.58 respectively in normal PZ, and 0.61 μm² ms^-1 and 1.15 in high grade cancer (variation in simulation values are less than 0.1%). Agreement between model predictions and measurements were good, with a mean square error of 0.22 μm² ms^-1 . Incorporation of cellular diffusion coefficient and kurtosis values obtained by Monte Carlo simulation into a model of prostate diffusion gives good agreement with published results.

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

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

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.