Beyond Prostate Imaging Reporting and Data System: Combining Magnetic Resonance Imaging Prostate Imaging Reporting and Data System and Prostate-Specific Membrane Antigen-Positron Emission Tomography/Computed Tomography PRIMARY Score in a Composite (P) Score for More Accurate Diagnosis of Clinically Significant Prostate Cancer.

PURPOSE: The Prostate Imaging Reporting and Data System (PI-RADS) score is standard of care for clinically significant prostate cancer (csPCa) diagnosis. The PRIMARY score (prostate-specific membrane antigen [PSMA]-positron emission tomography [PET]/CT) also has high diagnostic accuracy for csPCa. This study aimed to develop an easily calculated combined (P) score for csPCa detection (International Society of Urological Pathology [ISUP] ≥2) incorporating separately read PI-RADS and PRIMARY scores, with external validation. MATERIALS AND METHODS: Two datasets of men with suspected PCa, no prior biopsy, recent MRI and 68Ga-PSMA-11-PET/CT, and subsequent transperineal biopsy were evaluated. These included the development sample (n = 291, 56% csPCa) a prospective trial and the validation sample (n = 227, 67% csPCa) a multicenter retrospective database. Primary outcome was detection of csPCa (ISUP ≥2), with ISUP ≥ 3 cancer detection a secondary outcome. Score performance was evaluated by area under the curve, sensitivity, specificity, and decision curve analysis. RESULTS: The 5-point combined (P) score was developed in a prospective dataset. In the validation dataset, csPCa was identified in 0%, 20%, 52%, 96%, and 100% for P score 1 to 5. The area under the curve was 0.93 (95% CI: 0.90-0.96), higher than PI-RADS 0.89 (95% CI: 0.85-0.93, P = .039) and PRIMARY score alone 0.84 (95% CI: 0.79-0.89, P < .001). Splitting scores at 1/2 (negative) vs 3/4/5 (positive), P score sensitivity was 94% (95% CI: 89-97) compared to PI-RADS 89% (95% CI: 83-93) and PRIMARY score 86% (95% CI: 79-91). For ISUP ≥ 3, P score sensitivity was 99% (95% CI: 95-100) vs 94% (95% CI: 88-98) and 92% (95% CI: 85-97) for PI-RADS and PRIMARY scores respectively. A maximum standardized uptake value > 12 (P score 5) was ISUP ≥ 2 in all cases with 93% ISUP ≥ 3. CONCLUSIONS: The P score is easily calculated and improves accuracy for csPCa over both PI-RADS and PRIMARY scores. It should be considered when PSMA-PET is undertaken for diagnosis.

Investigating MRI-Associated Biological Aspects of Racial Disparities in Prostate Cancer for African American and White Men.

BACKGROUND: Understanding the characteristics of multiparametric MRI (mpMRI) in patients from different racial/ethnic backgrounds is important for reducing the observed gaps in clinical outcomes. PURPOSE: To investigate the diagnostic performance of mpMRI and quantitative MRI parameters of prostate cancer (PCa) in African American (AA) and matched White (W) men. STUDY TYPE: Retrospective. SUBJECTS: One hundred twenty-nine patients (43 AA, 86 W) with histologically proven PCa who underwent mpMRI before radical prostatectomy. FIELD STRENGTH/SEQUENCE: 3.0 T, T2-weighted turbo spin echo imaging, a single-shot spin-echo EPI sequence diffusion-weighted imaging, and a gradient echo sequence dynamic contrast-enhanced MRI with an ultrafast 3D spoiled gradient-echo sequence. ASSESSMENT: The diagnostic performance of mpMRI in AA and W men was assessed using detection rates (DRs) and positive predictive values (PPVs) in zones defined by the PI-RADS v2.1 prostate sector map. Quantitative MRI parameters, including Ktrans and ve of clinically significant (cs) PCa (Gleason score ≥ 7) tumors were compared between AA and W sub-cohorts after matching age, prostate-specific antigen (PSA), and prostate volume. STATISTICAL TESTS: Weighted Pearson's chi-square and Mann-Whitney U tests with a statistically significant level of 0.05 were used to examine differences in DR and PPV and to compare parameters between AA and matched W men, respectively. RESULTS: A total number of 264 PCa lesions were identified in the study cohort. The PPVs in the peripheral zone (PZ) and posterior prostate of mpMRI for csPCa lesions were significantly higher in AA men than in matched W men (87.8% vs. 68.1% in PZ, and 89.3% vs. 69.6% in posterior prostate). The Ktrans of index csPCa lesions in AA men was significantly higher than in W men (0.25 ± 0.12 vs. 0.20 ± 0.08 min-1; P < 0.01). DATA CONCLUSION: This study demonstrated race-related differences in the diagnostic performances and quantitative MRI measures of csPCa that were not reflected in age, PSA, and prostate volume. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

A rapid multi-parametric quantitative MR imaging method to assess Parkinson's disease: a feasibility study.

BACKGROUND: MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson's disease (PD). METHODS: 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs. RESULTS: The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values < 0.05 after FDR correction). The receiver operating characteristic analysis showed all these four quantitative parameters significantly contributed to PD diagnosis (all p values < 0.01 after FDR correction). Furthermore, the two quantitative parameters in subthalamic nucleus showed hemicerebral differences in regard to the clinically dominant side among PD patients. CONCLUSIONS: MULTIPLEX might be feasible for clinical application to assist in PD diagnosis and provide possible pathological information of PD patients' subcortical nucleus and dopaminergic midbrain regions.

Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.

BACKGROUND: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier. OBJECTIVE: To evaluate the performance of multi-parametric magnetic resonance imaging for grading renal injury in a rat model of renal cold ischemia-reperfusion injury using a machine learning approach. METHODS: Eighty male SD rats were selected to establish a renal cold ischemia -reperfusion model, and all performed multiparametric MRI scans (DWI, IVIM, DKI, BOLD, T1mapping and ASL), followed by pathological analysis. A total of 25 parameters of renal cortex and medulla were analyzed as features. The pathology scores were divided into 3 groups using K-means clustering method. Lasso regression was applied for the initial selecting of features. The optimal features and the best techniques for pathological grading were obtained. Multiple classifiers were used to construct models to evaluate the predictive value for pathology grading. RESULTS: All rats were categorized into mild, moderate, and severe injury group according the pathologic scores. The 8 features that correlated better with the pathologic classification were medullary and cortical Dp, cortical T2*, cortical Fp, medullary T2*, ∆T1, cortical RBF, medullary T1. The accuracy(0.83, 0.850, 0.81, respectively) and AUC (0.95, 0.93, 0.90, respectively) for pathologic classification of the logistic regression, SVM, and RF are significantly higher than other classifiers. For the logistic model and combining logistic, RF and SVM model of different techniques for pathology grading, the stable and perform are both well. Based on logistic regression, IVIM has the highest AUC (0.93) for pathological grading, followed by BOLD(0.90). CONCLUSION: The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury.

Multi-Parametric MRI for Evaluating Variations in Renal Structure, Function, and Endogenous Metabolites in an Animal Model With Acute Kidney Injury Induced by Ischemia Reperfusion.

BACKGROUND: Ischemia reperfusion injury (IRI)-induced acute kidney injury (AKI) may occur after renal ischemic injury. There is a lack of an accurate and comprehensive detection technique for IRI-AKI. PURPOSE: To longitudinally evaluate IRI-AKI in rats by renal structure, function, and metabolites using multi-parametric MRI (mpMRI). STUDY TYPE: Prospective. ANIMAL MODEL: Forty-eight rats undergoing IRI-AKI. FIELD STRENGTH/SEQUENCE: 7-T, T1 mapping, and arterial spin labeling (ASL): echo planar imaging (EPI) sequence; blood oxygen level-dependent (BOLD): gradient recalled echo (GRE) sequence; T2 mapping, quantitative magnetization transfer (qMT), and chemical exchange saturation transfer (CEST): rapid acquisition with relaxation enhancement (RARE) sequence. ASSESSMENT: The mpMRI for IRI-AKI was conducted at 0 (control), 1, 3, 7, 14, and 28 days, all included eight rats. The longitudinal mpMRI signal of manually outlined cortex, outer stripe of the outer medulla (OSOM), inner stripe of the outer medulla, and medulla plus pelvis were calculated and compared, their diagnosis performance for IRI-AKI also been evaluated. STATISTICAL TESTS: Pearson correlations analysis for correlation between mpMRI signal and renal injury, unpaired t-tests for comparing the signal changes, and receiver operating characteristics (ROC) analysis was used to identify most sensitive indicator of mpMRI. A P-value <0.05 was considered statistically significant. RESULTS: Compared with control kidneys, the T1 and T2 values of the cortex and medulla in IRI kidneys increased and reached their highest values on day 14, and the kidneys also showed the most severe edema and segments blurred. The RBF in the cortex and OSOM showed a significant decline after day 3. The BOLD signal in the OSOM largest increased on day 28. The cortical PSR and the amine-CEST both decreased with IRI-AKI progression, and amine-CEST achieved the highest AUC for the diagnosis (0.899). DATA CONCLUSION: Multi-parametric MRI may show comprehensive variations in IRI-AKI, and amine-CEST may exhibit the highest accuracy for diagnosis of IRI-AKI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

New setup for multi-parametric MRI in young and old rat gastrocnemius at 4.7 and 7 T during muscle stimulation.

T1 and T2 relaxation times combined with 31 P spectroscopy have been proven efficient for muscular disease characterization as well as for pre- and post-muscle stimulation measurements. Even though 31 P spectroscopy can already be performed during muscle exercise, no method for T1 and T2 measurement enables this possibility. In this project, a complete setup and protocol for multi-parametrical MRI of the rat gastrocnemius before, during and after muscle stimulation at 4.7 and 7 T is presented. The setup is fully MRI compatible and is composed of a cradle, an electro-stimulator and an electronic card in order to synchronize MRI sequences with muscle stimulation. A 2D triggered radial-encoded Look-Locker sequence was developed, and enabled T1 measurements in less than 2 min on stimulated muscle. Also, a multi-slice multi-echo sequence was adapted and synchronized for T2 measurements as well as 31 P spectroscopy acquisitions in less than 4 min in both cases on stimulated muscle. Methods were validated on young rats using different stimulation paradigms. Then they were applied on older rats to compare quantification results, using the different stimulation paradigms, and allowed observation of metabolic changes related to aging with good reproducibility. The robustness of the whole setup shows wide application opportunities.

Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data.

Multi-parametric magnetic resonance imaging (mpMRI) has been playing an increasingly important role in the detection of prostate cancer (PCa). Various computer-aided detection algorithms were proposed for automated PCa detection by combining information in multiple mpMRI parameters. However, there are specific features of mpMRI, including between-voxel correlation within each prostate and heterogeneity across patients, that have not been fully explored but could potentially improve PCa detection if leveraged appropriately. This article proposes novel Bayesian approaches for voxel-wise PCa classification that accounts for spatial correlation and between-patient heterogeneity in the mpMRI data. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we propose three scalable approaches based on Nearest Neighbor Gaussian Process (NNGP), reduced-rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance, respectively. Our simulation study shows that properly modeling the spatial correlation and between-patient heterogeneity can substantially improve PCa classification. Application to in vivo data illustrates that classification is improved by all three spatial modeling approaches considered, while modeling the between-patient heterogeneity does not further improve our classifiers. Among the proposed models, the NNGP-based model is recommended given its high classification accuracy and computational efficiency.

Minimally Invasive Procedures in the Diagnosis and Treatment of Localized Prostate Cancer: an Interventional Radiologist's Perspective.

PURPOSE OF REVIEW: Minimal invasive procedures, including targeted biopsy (TB) and focal therapy (FT), are increasingly used in diagnosis and treatment of localized prostate cancer. Here, we review the current role of these procedures, from a perspective of an interventional radiologist. RECENT FINDINGS: TB is an established part of current guidelines for diagnosis of PCa. Several modalities of FT are gaining prevalence in recent years, as a tissue-preserving alternative for definitive treatment of localized PCa. FT is currently at early research stages, offered to selected patients in clinical trials settings. TB and FT are minimally invasive procedures used by multidisciplinary teams for diagnosis and treatment of localized PCa.

The key role of levator ani thickness for early urinary continence recovery in patients undergoing robot-assisted radical prostatectomy: A multi-institutional study.

BACKGROUND: Urinary continence (UC) recovery dramatically affects quality of life after robot-assisted radical prostatectomy (RARP). Membranous urethral length (MUL) has been the most studied anatomical variable associated with UC recovery. OBJECTIVE: To investigate whether levator ani thickness (LAT), assessed with multi-parametric magnetic resonance imaging (mpMRI), correlates with UC recovery after RARP. DESIGN, SETTING, AND PARTICIPANTS: The study included 209 patients treated with RARP by expert surgeons with extensive robotic experience from 2017 to 2019. All patients had complete, clinical, mpMRI, pathological, and postoperative data including pelvic floor muscle training (PFMT) protocols. INTERVENTION: After a radiologist-specific training, two urologists independently examined the files, blinded to clinical and pathological findings as well as to postoperative continence status. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: On mpMRI, LAT, bladder neck (BN) shape, MUL, and apex overlapping (AO) were measured. UC recovery was defined as use of 0 or 1 safety pad at follow-up. Multivariable models were used to assess the association between variables and UC recovery. RESULTS AND LIMITATIONS: Overall, 173 (82.8%) patients were continent after a median follow-up of 23 months (interquartile range [IQR]: 17-28). Of these, 98 (46.9%) recovered within 3 months after surgery, 42 (20.1%) from 3 to 6 months, and 33 (15.8%) from 6 months onwards. A significant higher rate of patients with LAT > 10 mm (88.1 vs.75.8%; p = 0.03) experienced UC recovery, compared to those with LAT < 10 mm. This difference was observed in the first 3 months after surgery. At multivariable analysis, LAT (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.02-1.37; p = 0.02), Preoperative ICIQ score (OR: 0.91, 95% CI: 0.82-0.98, p = 0.03) and PFMT (OR: 1.98, 95% CI: 1.01-3.93; p = 0.04) independently predict higher UC recovery within 3 months, after accounting for age, BMI, preoperative PSA, D'Amico risk group, MUL, BN shape and AO. CONCLUSIONS: LAT greater than 1 cm was associated with greater UC recovery. Specifically, LAT greater than 1 cm seems to be associated with higher UC rate at 3 months after RARP, compared to those with LAT < 1 cm. PATIENT SUMMARY: Magnetic resonance features can help in predicting the risk of incontinence after robot-assisted radical prostatectomy and should be taken into account when counseling patients before surgery.

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging.

The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tone-difference matrix (NGTDM) and run-length matrix (RLM). Receiver-operating-characteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D* (AUC = 0.80) and f (AUC = 0.70) at t1. 3D statistical TA features might be useful as imaging-based markers for characterizing tumor aggressiveness and predicting chemotherapeutic response in patients with osteosarcoma.

Quantitative Prostate MRI Analysis Following Fluvastatin Therapy for Localized Prostate Cancer - A Pilot Study.

PURPOSE: To assess the role of multi-parametric MRI (mpMRI) in assessment of tumor response to fluvastatin administered prior to radical prostatectomy. METHODS: Men with MRI-visible, clinically significant prostate cancer and due to be treated with radical prostatectomy were prospectively enrolled. mpMRI was performed at baseline and following 6-7 week of neoadjuvant oral statin therapy (40 mg fluvastatin, twice daily), prior to prostatectomy. MRI assessment included tumor size, T2 relaxation time, ADC value, K-trans (volume transfer constant), Kep (reflux constant), and Ve (fractional volume) parameters at the 2 time points. Initial prostate needle biopsy cores, prior to starting oral statin therapy, corresponding to site of tumor on radical prostatectomy specimens were selected for analysis. The effect of fluvastatin on tumor proliferation (marker Ki67) and on tumor cell apoptosis (marker cleaved Caspase-3, CC3) were analyzed and correlated with MRI findings. RESULTS: Nine men with paired MRI studies were included in the study. Binary histopathological data was available for 6 of the participants. No significant change in tumor size (P = 0.898), T2 relaxation time (P = 0.213), ADC value (P = 0.455), K-trans (P = 0.613), Kep (P = 0.547) or Ve (P = 0.883) between the time of biopsy and prostatectomy were observed. No significant change in tumor proliferation (%Ki67-positive cells, P = 0.766) was observed by immunohistochemistry analysis. However, there was a significant increase in tumor cell apoptosis (%CC3-positive cells, P = 0.047). CONCLUSION: mpMRI techniques may not be sufficiently sensitive to detect the types (or magnitude) of tumor cell changes observed following 6-7 weeks of fluvastatin therapy for prostate cancer.

Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising.

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

MRI in acute muscle tears in athletes: can quantitative T2 and DTI predict return to play better than visual assessment?

OBJECTIVES: To assess the ability of quantitative T2, diffusion tensor imaging (DTI) and radiologist's scores to detect muscle changes following acute muscle tear in soccer and rugby players. To assess the ability of these parameters to predict return to play times. METHODS: In this prospective, longitudinal study, 13 male athletes (age 19 to 34 years; mean 25 years) underwent MRI within 1 week of suffering acute muscle tear. Imaging included measurements of T2 and DTI parameters. Images were also assessed using modified Peetrons and British athletics muscle injury classification (BAMIC) scores. Participants returned for a second scan within 1 week of being determined fit to return to play. MRI measurements were compared between visits. Pearson's correlation between visit 1 measurements and return to play times was assessed. RESULTS: There were significant differences between visits in BAMIC scores (Z = - 2.088; p = 0.037), modified Peetrons (Z = - 2.530; p = 0.011) and quantitative MRI measurements; T2, 13.12 ms (95% CI, 4.82 ms, 21.42 ms; p = 0.01); mean diffusivity (0.22 (0.04, 0.39); p = 0.02) and fractional anisotropy (0.07 (0.01, 0.14); p = 0.03). BAMIC scores showed a significant correlation with return to play time (Rs = 0.64; p = 0.02), but modified Peetrons scores and quantitative parameters did not. CONCLUSIONS: T2 and DTI measurements in muscle can detect changes due to healing following muscle tear. Although BAMIC scores correlated well with return to play times, in this small study, quantitative MRI values did not, suggesting that T2 and DTI measurements are inferior predictors of return to play time compared with visual scoring. KEY POINTS: • Muscle changes following acute muscle tear can be measured using T2 and diffusion measurements on MRI. • Measurements of T2 and diffusion using MRI are not as good as a radiologist's visual report at predicting return to play time after acute muscle tear.

Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network.

OBJECTIVE: To present a deep learning-based approach for semi-automatic prostate cancer classification based on multi-parametric magnetic resonance (MR) imaging using a 3D convolutional neural network (CNN). METHODS: Two hundred patients with a total of 318 lesions for which histological correlation was available were analyzed. A novel CNN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted, apparent diffusion coefficient (ADC), diffusion-weighted images, and K-trans) and the effect of different sequences on the network's performance was tested and discussed. The particular choice of modeling approach was justified by testing all relevant data combinations. The model was trained and validated using eightfold cross-validation. RESULTS: In terms of detection of significant prostate cancer defined by biopsy results as the reference standard, the 3D CNN achieved an area under the curve (AUC) of the receiver operating characteristics ranging from 0.89 (88.6% and 90.0% for sensitivity and specificity respectively) to 0.91 (81.2% and 90.5% for sensitivity and specificity respectively) with an average AUC of 0.897 for the ADC, DWI, and K-trans input combination. The other combinations scored less in terms of overall performance and average AUC, where the difference in performance was significant with a p value of 0.02 when using T2w and K-trans; and 0.00025 when using T2w, ADC, and DWI. Prostate cancer classification performance is thus comparable to that reported for experienced radiologists using the prostate imaging reporting and data system (PI-RADS). Lesion size and largest diameter had no effect on the network's performance. CONCLUSION: The diagnostic performance of the 3D CNN in detecting clinically significant prostate cancer is characterized by a good AUC and sensitivity and high specificity. KEY POINTS: • Prostate cancer classification using a deep learning model is feasible and it allows direct processing of MR sequences without prior lesion segmentation. • Prostate cancer classification performance as measured by AUC is comparable to that of an experienced radiologist. • Perfusion MR images (K-trans), followed by DWI and ADC, have the highest effect on the overall performance; whereas T2w images show hardly any improvement.

Use of Imaging to Optimise Prostate Cancer Tumour Volume Assessment for Focal Therapy Planning.

PURPOSE OF REVIEW: Rapid advances in imaging of the prostate have facilitated the development of focal therapy and provided a non-invasive method of estimating tumour volume. Focal therapy relies on an accurate estimate of tumour volume for patient selection and treatment planning so that the optimal energy dose can be delivered to the target area(s) of the prostate while minimising toxicity to surrounding structures. This review provides an overview of different imaging modalities which may be used to optimise tumour volume assessment and critically evaluates the published evidence for each modality. RECENT FINDINGS: Multi-parametric MRI (mp-MRI) has become the standard tool for patient selection and guiding focal therapy treatment. The current evidence suggests that mp-MRI may underestimate tumour volume, although there is a large variability in results. There remain significant methodological challenges associated with pathological processing and accurate co-registration of histopathological data with mp-MRI. Advances in different ultrasound modalities are showing promise but there has been limited research into tumour volume estimation. The role of PSMA PET/CT is still evolving and further investigation is needed to establish if this is a viable technique for prostate tumour volumetric assessment. mp-MRI provides the necessary tumour volume information required for selecting patients and guiding focal therapy treatment. The potential for underestimation of tumour volume should be taken into account and an additional margin applied to ensure adequate treatment coverage. At present, there are no other viable image-based alternatives although advances in new technologies may refine volume estimations in the future.

Use of multiparametric magnetic resonance imaging and fusion-guided biopsies to properly select and follow African-American men on active surveillance.

OBJECTIVES: To determine the rate of Gleason Grade Group (GGG) upgrading in African-American (AA) men with a prior diagnosis of low-grade prostate cancer (GGG 1 or GGG 2) on 12-core systematic biopsy (SB) after multiparametric magnetic resonance imaging (mpMRI) and fusion biopsy (FB); and whether AA men who continued active surveillance (AS) after mpMRI and FB fared differently than a predominantly Caucasian (non-AA) population. PATIENTS AND METHODS: A database of men who had undergone mpMRI and FB was queried to determine rates of upgrading by FB amongst men deemed to be AS candidates based on SB prior to referral. After FB, Kaplan-Meier curves were generated for AA men and non-AA men who then elected AS. The time to GGG upgrading and time continuing AS were compared using the log-rank test. RESULTS: AA men referred with GGG 1 disease on previous SB were upgraded to GGG ≥3 by FB more often than non-AA men, 22.2% vs 12.7% (P = 0.01). A total of 32 AA men and 258 non-AA men then continued AS, with a median (interquartile range) follow-up of 39.19 (24.24-56.41) months. The median time to progression was 59.7 and 60.5 months, respectively (P = 0.26). The median time continuing AS was 61.9 months and not reached, respectively (P = 0.80). CONCLUSIONS: AA men were more likely to be upgraded from GGG 1 on SB to GGG ≥3 on initial FB; however, AA and non-AA men on AS subsequently progressed at similar rates following mpMRI and FB. A greater tendency for SB to underestimate tumour grade in AA men may explain prior studies that have shown AA men to be at higher risk of progression during AS.

[Combination of serum PSA assay, multi-parametric MRI and targeted prostate biopsy for prostate cancer screening in Nanjing].

OBJECTIVE: To investigate the exact prevalence of PCa among males in Nanjing and search for a mode of PCa screening suitable for the specific conditions. METHODS: From January to December 2018, we collected serum samples and clinical information from 6 903 men aged ≥50 years taking physical examination in 16 community health service centers in Nanjing. We proposed multi-parametric MRI (mpMRI) for those with serum PSA ≥4 µg/L, transperineal systematic biopsy and MRI/ultrasound fusion targeted prostate biopsy for those who scored ≥3 points on the Prostate Imaging-Reporting and Data System Version 2 (PI-RADS v2), transperineal systematic biopsy only for those with a PI-RADS v2 score of <3 and serum PSA ≥10 µg/L, and follow-up examinations every 6 months for those with a PI-RADS v2 score of <3 and serum PSA <4 µg/L. RESULTS: Among the 6 903 male subjects, 835 (12.1%) were found with serum PSA≥4 µg/L; 229 (77.4%) of the 296 men that received mpMRI scored ≥3 points on PI-RADS v2; and 79 (53.4%) of the 148 males that underwent prostate biopsy were diagnosed with PCa, with a total detection rate of 1.14% in all the subjects. Of the 77 patients with complete pathological data, 73 (94.8%) were found with clinically significant PCa, 30 (39.0%) with localized, 41 (53.2%) with locally advanced and 6 (7.8%) with metastatic malignancy, 6 (7.8%) in stage Ⅰ, 21 (27.3%) in stage Ⅱ, 34 (44.2%) in stage Ⅲ and 16 (20.8%) in stage Ⅳ. There were 47 (66.2%) high-risk, 18 (25.4%) moderate-risk and 6 (8.5%) low-risk cases among those with localized or locally advanced PCa. CONCLUSIONS: The prevalence of PCa in Nanjing deserves considerable attention, and PCa screening is highly necessary in the high-risk population, for which the combination of serum PSA assay, mpMRI and targeted prostate biopsy may be an ideal method.

Assessment of unilateral ureter obstruction with multi-parametric MRI.

PURPOSE: Quantitative multi-parametric MRI (mpMRI) methods may allow the assessment of renal injury and function in a sensitive and objective manner. This study aimed to evaluate an array of MRI methods that exploit endogenous contrasts including relaxation rates, pool size ratio (PSR) derived from quantitative magnetization transfer (qMT), chemical exchange saturation transfer (CEST), nuclear Overhauser enhancement (NOE), and apparent diffusion coefficient (ADC) for their sensitivity and specificity in detecting abnormal features associated with kidney disease in a murine model of unilateral ureter obstruction (UUO). METHODS: MRI scans were performed in anesthetized C57BL/6N mice 1, 3, or 6 days after UUO at 7T. Paraffin tissue sections were stained with Masson trichrome following MRI. RESULTS: Compared to contralateral kidneys, the cortices of UUO kidneys showed decreases of relaxation rates R1 and R2 , PSR, NOE, and ADC. No significant changes in CEST effects were observed for the cortical region of UUO kidneys. The MRI parametric changes in renal cortex are related to tubular cell death, tubular atrophy, tubular dilation, urine retention, and interstitial fibrosis in the cortex of UUO kidneys. CONCLUSION: Measurements of multiple MRI parameters provide comprehensive information about the molecular and cellular changes produced by UUO. Magn Reson Med 79:2216-2227, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

National implementation of multi-parametric magnetic resonance imaging for prostate cancer detection - recommendations from a UK consensus meeting.

OBJECTIVES: To identify areas of agreement and disagreement in the implementation of multi-parametric magnetic resonance imaging (mpMRI) of the prostate in the diagnostic pathway. MATERIALS AND METHODS: Fifteen UK experts in prostate mpMRI and/or prostate cancer management across the UK (involving nine NHS centres to provide for geographical spread) participated in a consensus meeting following the Research and Development Corporation and University of California-Los Angeles (UCLA-RAND) Appropriateness Method, and were moderated by an independent chair. The experts considered 354 items pertaining to who can request an mpMRI, prostate mpMRI protocol, reporting guidelines, training, quality assurance (QA) and patient management based on mpMRI levels of suspicion for cancer. Each item was rated for agreement on a 9-point scale. A panel median score of ≥7 constituted 'agreement' for an item; for an item to reach 'consensus', a panel majority scoring was required. RESULTS: Consensus was reached on 59% of items (208/354); these were used to provide recommendations for the implementation of prostate mpMRI in the UK. Key findings include prostate mpMRI requests should be made in consultation with the urological team; mpMRI scanners should undergo QA checks to guarantee consistently high diagnostic quality scans; scans should only be reported by trained and experienced radiologists to ensure that men with unsuspicious prostate mpMRI might consider avoiding an immediate biopsy. CONCLUSIONS: Our consensus statements demonstrate a set of criteria that are required for the practical dissemination of consistently high-quality prostate mpMRI as a diagnostic test before biopsy in men at risk.

MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection.

BACKGROUND: Quantitative radiomic features provide a plethora of minable data extracted from multi-parametric magnetic resonance imaging (MP-MRI) which can be used for accurate detection and localization of prostate cancer. While most cancer detection algorithms utilize either voxel-based or region-based feature models, the complexity of prostate tumour phenotype in MP-MRI requires a more sophisticated framework to better leverage available data and exploit a priori knowledge in the field. METHODS: In this paper, we present MPCaD, a novel Multi-scale radiomics-driven framework for Prostate Cancer Detection and localization which leverages radiomic feature models at different scales as well as incorporates a priori knowledge of the field. Tumour candidate localization is first performed using a statistical texture distinctiveness strategy that leverages a voxel-resolution feature model to localize tumour candidate regions. Tumour region classification via a region-resolution feature model is then performed to identify tumour regions. Both voxel-resolution and region-resolution feature models are built upon and extracted from six different MP-MRI modalities. Finally, a conditional random field framework that is driven by voxel-resolution relative ADC features is used to further refine the localization of the tumour regions in the peripheral zone to improve the accuracy of the results. RESULTS: The proposed framework is evaluated using clinical prostate MP-MRI data from 30 patients, and results demonstrate that the proposed framework exhibits enhanced separability of cancerous and healthy tissue, as well as outperforms individual quantitative radiomics models for prostate cancer detection. CONCLUSION: Quantitative radiomic features extracted from MP-MRI of prostate can be utilized to detect and localize prostate cancer.