Diffusion-weighted endorectal MR imaging at 3 T for prostate cancer: tumor detection and assessment of aggressiveness

Quantitative magnetic resonance imaging in prostate cancer: A review of current technology

Prostate cancer (PCa) imaging forms an important part of PCa clinical management. Magnetic resonance imaging is the modality of choice for prostate imaging. Most of the current imaging assessment is qualitative i.e., based on visual inspection and thus subjected to inter-observer disagreement. Quantitative imaging is better than qualitative assessment as it is more objective, and standardized, thus improving interobserver agreement. Apart from detecting PCa, few quantitative parameters may have potential to predict disease aggressiveness, and thus can be used for prognosis and deciding the course of management. There are various magnetic resonance imaging-based quantitative parameters and few of them are already part of PIRADS v.2.1. However, there are many other parameters that are under study and need further validation by rigorous multicenter studies before recommending them for routine clinical practice. This review intends to discuss the existing quantitative methods, recent developments, and novel techniques in detail.

The value of synthetic magnetic resonance imaging in the diagnosis and assessment of prostate cancer aggressiveness

Background

Synthetic magnetic resonance imaging (SyMRI) is a fast, standardized, and robust novel quantitative technique that has the potential to circumvent the subjectivity of interpretation in prostate multiparametric magnetic resonance imaging (mpMRI) and the limitations of existing MRI quantification techniques. Our study aimed to evaluate the potential utility of SyMRI in the diagnosis and aggressiveness assessment of prostate cancer (PCA).

Methods

We retrospectively analyzed 309 patients with suspected PCA who had undergone mpMRI and SyMRI, and pathologic results were obtained by biopsy or PCA radical prostatectomy (RP). Pathological types were classified as PCA, benign prostatic hyperplasia (BPH), or peripheral zone (PZ) inflammation. According to the Gleason Score (GS), PCA was divided into groups of intermediate-to-high risk (GS ≥4+3) and low-risk (GS ≤3+4). Patients with biopsy-confirmed low-risk PCA were further divided into upgraded and nonupgraded groups based on the GS changes of the RP results. The values of the apparent diffusion coefficient (ADC), T1, T2 and proton density (PD) of these lesions were measured on ADC and SyMRI parameter maps by two physicians; these values were compared between PCA and BPH or inflammation, between the intermediate-to-high-risk and low-risk PCA groups, and between the upgraded and nonupgraded PCA groups. The risk factors affecting GS grades were identified via univariate analysis. The effects of confounding factors were excluded through multivariate logistic regression analysis, and independent predictive factors were calculated. Subsequently, the ADC+Sy(T2+PD) combined models for predicting PCA risk grade or GS upgrade were constructed through data processing analysis. The diagnostic performance of each parameter and the ADC+Sy(T2+PD) model was analyzed. The calibration curve was calculated by the bootstrapping internal validation method (200 bootstrap resamples).

Results

The T1, T2, and PD values of PCA were significantly lower than those of BPH or inflammation (P≤0.001) in both the PZ or transitional zone. Among the 178 patients with PCA, intermediate-to-high-risk PCA group had significantly higher T1, T2, and PD values but lower ADC values compared with the low-risk group (P<0.05), and the diagnostic efficacy of each single parameter was similar (P>0.05). The ADC+Sy(T2+PD) model showed the best performance, with an area under the curve (AUC) 0.110 [AUC =0.818; 95% confidence interval (CI): 0.754-0.872] higher than that of ADC alone (AUC =0.708; 95% CI: 0.635-0.774) (P=0.003). Among the 68 patients initially classified as PCA in the low-risk group by biopsy, PCA in the postoperative upgraded GS group had significantly higher T1, T2, and PD values but lower ADC values than did those in the nonupgraded group (P<0.01). In addition, the ADC+Sy(T2+PD) model better predicted the upgrade of GS, with a significant increase in AUC of 0.204 (AUC =0.947; 95% CI: 0.864-0.987) compared with ADC alone (AUC =0.743; 95% CI: 0.622-0.841) (P<0.001).

Conclusions

Quantitative parameters (T1, T2, and PD) derived from SyMRI can help differentiate PCA from non-PCA. Combining SyMRI parameters with ADC significantly improved the ability to differentiate between intermediate-to-high risk PCA from low-risk PCA and could predict the upgrade of low-risk PCA as confirmed by biopsy.

Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis

OBJECTIVES

To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection.

MATERIALS AND METHODS

A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated.

RESULTS

Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74-85%) and 78% (95% CI: 70-85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74-84%) and 80% (95% CI: 71-87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83-0.89) and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84-0.90] and 0.82 [95% CI: 0.79-0.85], respectively).

CONCLUSION

Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers.

CLINICAL RELEVANCE STATEMENT

In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments.

KEY POINTS

MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions.

Integration of PSAd and multiparametric MRI to forecast biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml

Introduction

This study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy.

Methods

This retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models.

Results

The CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed.

Conclusion

The prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.

Early change in apparent diffusion coefficient as a predictor of response to neoadjuvant androgen deprivation and external beam radiation therapy for intermediate- to high-risk prostate cancer

AIM

To determine the role of serial apparent diffusion coefficient (ADC) as a biomarker for response to neoadjuvant androgen deprivation therapy (nADT) followed by external beam radiation therapy (EBRT) in intermediate- to high-risk prostate cancer (PCa) patients.

METHODS

This Health Insurance Portability and Accountability Act (HIPAA)-compliant, institutional review board (IRB)-approved prospective study included 12 patients with intermediate- to high-risk PCa patients prior to nADT and EBRT, who underwent serial serum prostate-specific antigen (PSA) and multiparametric prostate magnetic resonance imaging (mpMRI) at baseline (BL), 8-weeks after nADT initiation (time point [TP]1), 6-weeks into EBRT delivery (TP2), and 6-months after nADT initiation (TP3). Tumour volume (tVOL) and tumour and normal tissue ADC (tADC and nlADC) were determined at all TPs. tADC and nlADC dynamics were correlated with post-treatment PSA using Pearson's correlation coefficient. Paired t-tests compared pre/post-treatment ADC.

RESULTS

There was a sequential decrease in PSA at all TPs, reaching their lowest values at TP3 post-treatment completion. Mean tADC increased significantly from baseline to TP1 (917.8 ± 107.7 × 10-6 versus 1033.8 ± 139.3 × 10-6 mm2/s; p<0.01), with no subsequent change at TP2 or TP3. Both percentage and absolute change in tADC from BL to TP1 correlated with post-treatment PSA (r=-0.666, r=-0.674; p=0.02). Post-treatment PSA in good responders (<0.1 ng/ml) versus poor responders (≥ 0.1 ng/ml) was associated with a greater increase in tADC from BL to TP1 (169.2 ± 122.4 × 10-6 versus 22.9 ± 75.5 × 10-6 mm2/s, p=0.03).

CONCLUSION

This pilot study demonstrates the potential for early ADC metrics as a biomarker of response to nADT and EBRT in intermediate to high-risk PCA.

AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI

Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the model's detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.

A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.

SIGNIFICANCE

Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy.

Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.

Application of targeted diagnosis of PSMA in the modality shift of prostate cancer diagnosis: a review

Prostate cancer (PCa) is a serious threat to the health of men all over the world. The progression of PCa varies greatly among different individuals. In clinical practice, some patients often progress to advanced PCa. Therefore, accurate imaging for diagnosis and staging of PCa is particularly important for clinical management of patients. Conventional imaging examinations such as MRI and CT cannot accurately diagnose the pathological stages of advanced PCa, especially metastatic lymph node (LN) stages. As a result, developing an accurate molecular targeted diagnosis is crucial for advanced PCa. Prostate specific membrane antigen (PSMA) is of great value in the diagnosis of PCa because of its specific expression in PCa. At present, researchers have developed positron emission tomography (PET) targeting PSMA. A large number of studies have confirmed that it not only has a higher tumor detection rate, but also has a higher diagnostic efficacy in the pathological stage of advanced PCa compared with traditional imaging methods. This review summarizes recent studies on PSMA targeted PET in PCa diagnosis, analyzes its value in PCa diagnosis in detail, and provides new ideas for urological clinicians in PCa diagnosis and clinical management.

Up-to-Date Imaging and Diagnostic Techniques for Prostate Cancer: A Literature Review

Prostate cancer (PCa) faces great challenges in early diagnosis, which often leads not only to unnecessary, invasive procedures, but to over-diagnosis and treatment as well, thus highlighting the need for modern PCa diagnostic techniques. The review aims to provide an up-to-date summary of chronologically existing diagnostic approaches for PCa, as well as their potential to improve clinically significant PCa (csPCa) diagnosis and to reduce the proliferation and monitoring of PCa. Our review demonstrates the primary outcomes of the most significant studies and makes comparisons across the diagnostic efficacies of different PCa tests. Since prostate biopsy, the current mainstream PCa diagnosis, is an invasive procedure with a high risk of post-biopsy complications, it is vital we dig out specific, sensitive, and accurate diagnostic approaches in PCa and conduct more studies with milestone findings and comparable sample sizes to validate and corroborate the findings.

Literature review: Imaging in prostate cancer

Imaging plays an increasingly important role in the detection and characterization of prostate cancer (PC). This review summarizes the key conventional and advanced imaging modalities including multiparametric magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging and tries to instruct clinicians in finding the best image modality depending on the patient`s PC-stage. We aim to give an overview of the different image modalities and their benefits and weaknesses in imaging PC. Emphasis is put on primary prostate cancer detection and staging as well as on recurrent and castration resistant prostate cancer. Results from studies using various imaging techniques are discussed and compared. For the different stages of PC, advantages and disadvantages of the different imaging modalities are discussed. Moreover, this review aims to give an outlook about upcoming, new imaging modalities and how they might be implemented in the future into clinical routine. Imaging patients suffering from PC should aim for exact diagnosis, accurate detection of PC lesions and should mirror the true tumor burden. Imaging should lead to the best patient treatment available in the current PC-stage and should avoid unnecessary therapeutic interventions. New image modalities such as long axial field of view PET/CT with photon-counting CT and radiopharmaceuticals like androgen receptor targeting radiopharmaceuticals open up new possibilities. In conclusion, PC imaging is growing and each image modality is aiming for improvement.

Rats, Neuregulins and Radical Prostatectomy: A Conceptual Overview

In the contemporary era of early detection, with mostly curative initial treatment for prostate cancer (PC), mortality rates have significantly diminished. In addition, mean age at initial PC diagnosis has decreased. Despite technical advancements, the probability of erectile function (EF) recovery post radical prostatectomy (RP) has not significantly changed throughout the last decade. Due to virtually unavoidable intraoperative cavernous nerve (CN) lesions and operations with younger patients, post-RP erectile dysfunction (ED) has now begun affecting these younger patients. To address this pervasive limitation, a plethora of CN lesion animal model investigations have analyzed the use of systemic/local treatments for EF recovery post-RP. Most promisingly, neuregulins (NRGs) have demonstrated neurotrophic effects in both neurodegenerative disease and peripheral nerve injury models. Recently, glial growth factor 2 (GGF2) has demonstrated far superior, dose-dependent, neuroprotective/restorative effects in the CN injury rat model, as compared to previous therapeutic counterparts. Although potentially impactful, these initial findings remain limited and under-investigated. In an effort to aid clinicians, our paper reviews post-RP ED pathogenesis and currently available therapeutic tools. To stimulate further experimentation, a standardized preparation protocol and in-depth analysis of applications for the CN injury rat model is provided. Lastly, we report on NRGs, such as GGF2, and their potentially revolutionary clinical applications, in hopes of identifying relevant future research directions.

Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique

BACKGROUND

Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency.

METHODS

This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant.

RESULTS

DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUC_patient: 0.89 vs. 0.86; AUC_lesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (OR_{rectal susceptibility artifact} = 5.46; OR_diameter, = 1.12; OR_ADC = 0.998; all P < .001) and false negatives (OR_{rectal susceptibility artifact} = 3.31; OR_diameter = 0.82; OR_ADC = 1.007; all P ≤ .03) of DL-CAD.

CONCLUSIONS

Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD.

TRIAL REGISTRATION

ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.

Cross-Modality Image Registration Using a Training-Time Privileged Third Modality

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusion-weighted scans with high b-value (DWI [Formula: see text]). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI [Formula: see text]) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWI [Formula: see text], to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI [Formula: see text] and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI [Formula: see text] and T2w in this challenging application.

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.

Development of a nomogram combining multiparametric magnetic resonance imaging and PSA-related parameters to enhance the detection of clinically significant cancer across different region

OBJECTIVE

Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted.

PATIENTS AND METHODS

A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS score and clinical variables (prostate-specific antigen [PSA], prostate volume (PV), age, free/total PSA, and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa. Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ² test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions.

RESULTS

A total of 320 of 688 included patients were diagnosed with csPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitional and apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheral and transitional zones.

CONCLUSION

We produced a novel nomogram predicting csPCa in patients with suspected imaging according to different locations. Our results indicated that PI-RADS score combined with other clinical parameters showed a robust predictive capacity for csPCa before prostate biopsy. The new nomogram, which incorporates prebiopsy data including PSA, PV, age, and PI-RADS score, can be helpful for clinical decision-making to avoid unnecessary biopsy.

Correlation between FA and ADC, number and length of the periprostatic neurovascular fibers

PURPOSE

Our aim was to explore the relation between FA and ADC, number and length of the periprostatic neurovascular fibers (PNF) by means of 1.5 T Diffusion Tensor Imaging (DTI) imaging through a multivariate linear regression analysis model.

METHODS

For this retrospective study, 56 patients (mean age 63.5 years), who underwent 1.5-T prostate MRI, including DTI, were enrolled between October 2014 and December 2018. Multivariate regression analysis was performed to evaluate the statistically significant correlation between FA values (dependent variable) and ADC, the number and the length of PNF (independent variables), if p-value <0.05. A value of 0.5 indicated poor agreement; 0.5-0.75, moderate agreement; 0.75-0.9, good agreement; 0.61-0.80, good agreement; and 0.9-1.00, excellent agreement.

RESULTS

The overall fit of the multivariate regression model was excellent, with R² value of 0.9445 (R² adjusted 0.9412; p < 0.0001). Multivariate linear regression analysis showed a statistically significant correlation (p < 0.05) for all the three independent variables. The r partial value was -0.9612 for ADC values (p < 0.0001), suggesting a strong negative correlation, 0.4317 for the number of fiber tracts (p < 0.001), suggesting a moderate positive correlation, and -0.306 for the length of the fiber tracts (p < 0.05), suggesting a weak negative correlation.

CONCLUSIONS

Our multivariate linear regression model has demonstrated a statistically significant correlation between FA values of PNF with other DTI parameters, in particular with ADC.

A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer

OBJECTIVES

The objectives of this exploratory study were to investigate the feasibility of multidimensional diffusion magnetic resonance imaging (MddMRI) in assessing diffusion heterogeneity at both a macroscopic and microscopic level in prostate cancer (PCa).

MATERIALS AND METHODS

Informed consent was obtained from 46 subjects who underwent 3.0-T prostate multiparametric MRI, complemented with a prototype spin echo-based MddMRI sequence in this institutional review board-approved study. Prostate cancer tumors and comparative normal tissue from each patient were contoured on both apparent diffusion coefficient and MddMRI-derived mean diffusivity (MD) maps (from which microscopic diffusion heterogeneity [MKi] and microscopic diffusion anisotropy were derived) using 3D Slicer. The discriminative ability of MddMRI-derived parameters to differentiate PCa from normal tissue was determined using the Friedman test. To determine if tumor diffusion heterogeneity is similar on macroscopic and microscopic scales, the linear association between SD of MD and mean MKi was estimated using robust regression (bisquare weighting). Hypothesis testing was 2 tailed; P values less than 0.05 were considered statistically significant.

RESULTS

All MddMRI-derived parameters could distinguish tumor from normal tissue in the fixed-effects analysis (P < 0.0001). Tumor MKi was higher (P < 0.05) compared with normal tissue (median, 0.40; interquartile range, 0.29-0.52 vs 0.20-0.18; 0.25), as was tumor microscopic diffusion anisotropy (0.55; 0.36-0.81 vs 0.20-0.15; 0.28). The MKi could not be predicted (no significant association) by SD of MD. There was a significant correlation between tumor volume and SD of MD (R2 = 0.50, slope = 0.008 μm2/ms per millimeter, P < 0.001) but not between tumor volume and MKi.

CONCLUSIONS

This explorative study demonstrates that MddMRI provides novel information on MKi and microscopic anisotropy, which differ from measures at the macroscopic level. MddMRI has the potential to characterize tumor tissue heterogeneity at different spatial scales.

An endovaginal MRI array with a forward-looking coil for advanced gynecological cancer brachytherapy procedures: Design and initial results

PURPOSE

To develop an endovaginal MRI array that provides signal enhancement forward into the posterior parametrium and sideways into the vaginal wall, accelerating multiple-contrast detection of residual tumors that survive external beam radiation. The array's enclosure should form an obturator for cervical cancer brachytherapy, allowing integration with MRI-guided catheter placement, CT, and interstitial radiation dose delivery.

METHODS

The endovaginal array consisted of forward-looking and sideways-looking components. The forward-looking element imaged the cervix and posterior endometrium, and the sideways-looking elements imaged the vaginal wall. Electromagnetic simulation was performed to optimize the geometry of a forward-looking coil placed on a conductive-metallic substrate, extending the forward penetration above the coil's tip. Thereafter, an endovaginal array with one forward-looking coil and four sideways-looking elements was constructed and tested at 1.5 Tesla in saline and gel phantoms, and three sexually mature swine. Each coil's tuning, matching, and decoupling were optimized theoretically, implemented with electronic circuits, and validated with network-analyzer measurements. The array enclosure emulates a conventional brachytherapy obturator, allowing use of the internal imaging array together with tandem coils and interstitial catheters, as well as use of the enclosure alone during CT and radiation delivery. To evaluate the receive magnetic field ( B 1 - ) spatial profile, the endovaginal array's specific absorption-rate (SAR) distribution was simulated inside a gel ASTM phantom to determine extreme heating locations in advance of a heating test. Heating tests were then performed during high SAR imaging in a gel phantom at the predetermined locations, testing compliance with MRI safety standards. To assess array imaging performance, signal-to-noise-ratios (SNR) were calculated in a saline phantom and in vivo. Swine images were acquired with the endovaginal array combined with the scanner's body and spine arrays.

RESULTS

Simulated B 1 - profiles for the forward-looking lobe pattern, obtained while varying several geometric parameters, disclosed that a forward-looking coil placed on a metal-backed substrate could double the effective forward penetration from approximately 25 to ∼40 mm. An endovaginal array, enclosed in an obturator enclosure was then constructed, with all coils tuned, matched, and decoupled. The ASTM gel-phantom SAR test showed that peak local SAR was 1.2 W/kg in the forward-looking coil and 0.3 W/kg in the sideways-looking elements, well within ASTM/FDA/IEC guidelines. A 15-min 4 W/kg average SAR imaging experiment resulted in less than 2^o C temperature increase, also within ASTM/FDA/IEC heating limits. In a saline phantom, the forward-looking coil and sideways-looking array's SNR was four to eight times, over a 20-30 mm field-of-view (FOV), and five to eight times, over a 15-25 mm FOV, relative to the spine array's SNR, respectively. In three sexually mature swine, the forward-looking coil provided a 5 + 0.2 SNR enhancement factor within the cervix and posterior endometrium, and the sideways-looking array provided a 4 + 0.2 SNR gain factor in the vaginal wall, relative to the Siemens spine array, demonstrating that the array could significantly reduce imaging time.

CONCLUSIONS

Higher SNR gynecological imaging is supported by forward-looking and sideways-looking coils. A forward-looking endovaginal coil for cervix and parametrium imaging was built with optimized metal backing. Array placement within an obturator enhanced integration with the brachytherapy procedure and accelerated imaging for detecting postexternal-beam residual tumors.

A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation

In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.

Pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI in PI-RADS category 3 peripheral zone lesions: preliminary study evaluating DCE-MRI as an imaging biomarker for detection of clinically significant prostate cancers

PURPOSE

To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI.

MATERIALS AND METHODS

In the present IRB approved, bi-institutional, retrospective, case-control study, we identified 73 men with 73 PZ PI-RADS version 2.1 category 3 lesions with MRI-directed-TRUS-guided targeted biopsy yielding: 12 PZ CS-PCa (ISUP Grade Group 2; N = 9, ISUP 3; N = 3), 27 ISUP 1 PCa and 34 benign lesions. An expert blinded radiologist segmented lesions on ADC and DCE images; segmentations were overlayed onto pharmacokinetic DCE-MRI maps. Mean values were compared between groups using univariate analysis. Diagnostic accuracy was assessed by ROC.

RESULTS

There were no differences in age, PSA, PSAD or clinical stage between groups (p = 0.265-0.645). Mean and 10th percentile ADC did not differ comparing CS-PCa to ISUP 1 PCa and benign lesions (p = 0.376 and 0.598) but was lower comparing ISUP ≥ 1 PCa to benign lesions (p < 0.001). Mean Ktrans (p = 0.003), Ve (p = 0.003) but not Kep (p = 0.387) were higher in CS-PCa compared to ISUP 1 PCa and benign lesions. There were no differences in DCE-MRI metrics comparing ISUP ≥ 1 PCa and benign lesions (p > 0.05). AUC for diagnosis of CS-PCa using Ktrans and Ve were: 0.69 (95% CI 0.52-0.87) and 0.69 (0.49-0.88).

CONCLUSION

Pharmacokinetic modeling of DCE-MRI parameters in PI-RADS category 3 lesions with subjectively negative DCE-MRI show significant differences comparing CS-PCa to ISUP 1 PCa and benign lesions, in this study outperforming ADC. Studies are required to further evaluate these parameters to determine which patients should undergo targeted biopsy for PI-RADS 3 lesions.

A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI), the use of three multiple imaging sequences, typically T2-weighted, diffusion weighted (DWI) and dynamic contrast enhanced (DCE) images, has a high sensitivity and specificity for detecting significant cancer. Current guidance now recommends its use prior to biopsy. However, the impact of DCE is currently under debate regarding test accuracy. Biparametric MRI (bpMRI), using only T2 and DWI has been proposed as a viable alternative. We conducted a contemporary systematic review and meta-analysis to further examine the diagnostic performance of bpMRI in the diagnosis of any and clinically significant prostate cancer.

METHODS

A systematic review of the literature from 01/01/2017 to 06/07/2019 was performed by two independent reviewers using predefined search criteria. The index test was biparametric MRI and the reference standard whole-mount prostatectomy or prostate biopsy. Quality of included studies was assessed by the QUADAS-2 tool. Statistical analysis included pooled diagnostic performance (sensitivity; specificity; AUC), meta-regression of possible covariates and head-to-head comparisons of bpMRI and mpMRI where both were performed in the same study.

RESULTS

Forty-four articles were included in the analysis. The pooled sensitivity for any cancer detection was 0.84 (95% CI, 0.80-0.88), specificity 0.75 (95% CI, 0.68-0.81) for bpMRI. The summary ROC curve yielded a high AUC value (AUC = 0.86). The pooled sensitivity for clinically significant prostate cancer was 0.87 (95% CI, 0.78-0.93), specificity 0.72 (95% CI, 0.56-0.84) and the AUC value was 0.87. Meta-regression analysis revealed no difference in the pooled diagnostic estimates between bpMRI and mpMRI.

CONCLUSIONS

This meta-analysis on contemporary studies shows that bpMRI offers comparable test accuracies to mpMRI in detecting prostate cancer. These data are broadly supportive of the bpMRI approach but heterogeneity does not allow definitive recommendations to be made. There is a need for prospective multicentre studies of bpMRI in biopsy naïve men.

Modified Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging of the Prostate: Comparison With Reduced Field-of-View Imaging and Single Shot Echo-Planar Imaging

OBJECTIVE

The objective of this study was to compare the image quality and apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) with modified reduced field of view (FOV) based on 2-dimensional (2D)-selective radiofrequency excitations by tilting the excitation plane in prostate with reduced FOV using parallel-transmit-accelerated 2D-selective radiofrequency excitation and single-shot echo planar imaging (ssEPI).

METHODS

Fifty patients who underwent multiparametric magnetic resonance imaging including 3 DWIs were included. Two observers independently performed qualitative image analyses using 5-point scale. Apparent diffusion coefficient measurements were performed for quantitative analysis.

RESULTS

Modified reduced FOV provided the highest qualitative scores for all categories compared with reduced FOV and ssEPI (P < 0.000). Both reduced FOV DWIs showed higher ADC values compared with ssEPI (P < 0.001); however, the ADC ratios between the lesion and peripheral zone were not significantly different (all P > 0.05).

CONCLUSIONS

The modified reduced FOV DWI showed better overall image quality, differentiability of anatomic regions, and lesion conspicuity with fewer artifacts compared with DWI with reduced FOV and ssEPI.

Depiction of periprostatic nerve fibers by means of 1.5 T diffusion tensor imaging

PURPOSE

The knowledge of periprostatic nerve fiber (pNF) is still incomplete by means of conventional MRI. The purpose of our study was to demonstrate if DTI imaging is able to depict anatomical features of pNF.

METHODS

For this retrospective study, fifty-six patients (mean age 63.5 years), who underwent 1.5-T prostate MRI, including 32 directions DTI, were enrolled between October 2014 and December 2018. ANOVA test and Student's t-test were performed between the mean values of the number, FA values, and fiber length of pNF between base and mid-gland, mid-gland and apex, base and apex, right and left side, and anterior and posterior face of the prostate. A qualitative analysis was performed to detect the main orientation of pNF through a colorimetric 3D tractographic reconstruction.

RESULTS

The number of pNF showed a decrease from the base (322) to mid-gland (248) and apex (75) (p < 0.05). The FA values were higher at base and mid-gland (0.435 and 0.456) compared to the apex (0.313) (p < 0.05). The length of pNF was higher at apex (13.4 mm) compared to base (11.5 mm) and mid-gland (11.7 mm) (p < 0.05). The number of pNF was higher on the posterior face compared to the anterior face at base (186 vs 137), (p < 0.001). The FA values were higher on the posterior face compared to the anterior face at base (0.452 vs 0.417), mid-gland (0.483 vs 0.429), and apex (0.42 vs 0.382), (p < 0.05). The length of the pNF was higher in the posterior (14.7 mm) than in the anterior face (12 mm) at apex (p < 0.001). The main orientation of pNF was longitudinal in all patients (56/56, 100%).

CONCLUSIONS

DTI imaging has been demonstrated able to depict anatomical features of pNF.

Dynamic contrast-enhanced MRI perfusion quantification in hepatocellular carcinoma: comparison of gadoxetate disodium and gadobenate dimeglumine

OBJECTIVES

(1) To assess the quality of the arterial input function (AIF) during dynamic contrast-enhanced (DCE) MRI of the liver and (2) to quantify perfusion parameters of hepatocellular carcinoma (HCC) and liver parenchyma during the first 3 min post-contrast injection with DCE-MRI using gadoxetate disodium compared to gadobenate dimeglumine (Gd-BOPTA) in different patient populations.

METHODS

In this prospective study, we evaluated 66 patients with 83 HCCs who underwent DCE-MRI, using gadoxetate disodium (group 1, n = 28) or Gd-BOPTA (group 2, n = 38). AIF qualitative and quantitative features were assessed. Perfusion parameters (based on the initial 3 min post-contrast) were extracted in tumours and liver parenchyma, including model-free parameters (time-to-peak enhancement (TTP), time-to-washout) and modelled parameters (arterial flow (F_a), portal venous flow (F_p), total flow (F_t), arterial fraction, mean transit time (MTT), distribution volume (DV)). In addition, lesion-to-liver contrast ratios (LLCRs) were measured. Fisher's exact tests and Mann-Whitney U tests were used to compare the two groups.

RESULTS

AIF quality, modelled and model-free perfusion parameters in HCC were similar between the 2 groups (p = 0.054-0.932). Liver parenchymal flow was lower and liver enhancement occurred later in group 1 vs group 2 (F_p, p = 0.002; F_t, p = 0.001; TTP, MTT, all p < 0.001), while there were no significant differences in tumour LLCR (max. positive LLCR, p = 0.230; max. negative LLCR, p = 0.317).

CONCLUSION

Gadoxetate disodium provides comparable AIF quality and HCC perfusion parameters compared to Gd-BOPTA during dynamic phases. Despite delayed and decreased liver enhancement with gadoxetate disodium, LLCRs were equivalent between contrast agents, indicating similar tumour conspicuity.

KEY POINTS

• Arterial input function quality, modelled, and model-free dynamic parameters measured in hepatocellular carcinoma are similar in patients receiving gadoxetate disodium or gadobenate dimeglumine during the first 3 min post injection. • Gadoxetate disodium and gadobenate dimeglumine show similar lesion-to-liver contrast ratios during dynamic phases in patients with HCC. • There is lower portal and lower total hepatic flow and longer hepatic mean transit time and time-to-peak with gadoxetate disodium compared to gadobenate dimeglumine.

The utility of ADC parameters in the diagnosis of clinically significant prostate cancer by 3.0-Tesla diffusion-weighted magnetic resonance imaging

Purpose

This study has focused on investigating the relationship between the exponential apparent diffusion coefficient (exp-ADC), selective apparent diffusion coefficient (sel-ADC) values, the ADC ratio (ADCr), and prostate cancer aggressiveness with transrectal ultrasound-guided prostate biopsy in patients with prostate cancer.

Material and methods

All patients underwent a multiparametric magnetic resonance imaging (mpMRI) including tri-planar T2-weighted (T2W), dynamic contrast-enhanced (DCE), diffusion-weighted sequences using a 3.0-Tesla MR scanner (Skyra, Siemens Medical Systems, Germany) with a dedicated 18-channel body coil and a spine coil underneath the pelvis, with the patient in the supine position. Exp-ADC, sel-ADC, and ADCr of defined lesions were evaluated using region-of-interest-based measurements. Exp-ADC, sel-ADC, and ADCr were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy.

Results

Patients were divided into 2 groups. Group I is Gleason score ≥ 3 + 4, group II is Gleason score = 6. Sel-ADC and exp-ADC were statistically significant between 2 groups (0.014 and 0.012, respectively). However, the ADCr difference between nonclinical significant prostate cancer from clinically significant prostate cancer was not significant (p = 0.09).

Conclusions

This study is the first to evaluate exp-ADC and sel-ADC values of prostate carcinoma with ADCr. One limitation of this study might be the limited number of patients. Exp-ADC and sel-ADC values in prostate MRI imaging improved the specificity, accuracy, and area under the curve (AUC) for detecting clinically relevant prostate carcinoma. Adding exp-ADC and sel-ADC values to ADCr can be used to increase the diagnostic accuracy of DWI.

mpMRI of the Prostate (MR-Prostatography): Updated Recommendations of the DRG and BDR on Patient Preparation and Scanning Protocol

The Working Group Uroradiology and Urogenital Diagnosis of the German Roentgen Society (DRG) revised and updated the recommendations for preparation and scanning protocol of the multiparametric MRI of the Prostate in a consensus process and harmonized it with the managing board of German Roentgen Society and Professional Association of the German Radiologist (BDR e. V.). These detailed recommendation define the referenced "validated quality standards" of the German S3-Guideline Prostate Cancer and describe in detail the topic 1. anamnestic datas, 2. termination of examinations and preparation of examinations, 3. examination protocol and 4. MRI-(in-bore)-biopsy. KEY POINTS:: · The recommendations for preparation and scanning protocol of the multiparametric MRI of the Prostate were revised and updated in a consensus process and harmonized with the managing board of German Roentgen Society (DRG) and Professional Asssociation of the German Radiologist (BDR).. · Detailed recommendations are given for topic 1. anamnestic datas, 2. termination and preparation of examinations, 3. examination protocoll and 4. MRI-(in-bore)-biopsy.. · These recommendations define the referenced "validated quality standards" of the German S3-Guideline Prostate Cancer.. CITATION FORMAT: · Franiel T, Asbach P, Beyersdorff D et al. mpMRI of the Prostate (MR-Prostatography): Updated Recommendations of the DRG and BDR on Patient Preparation and Examination Protocol. Fortschr Röntgenstr 2021; 193: 763 - 776.

Measured Multipoint Ultra-High b-Value Diffusion MRI in the Assessment of MRI-Detected Prostate Lesions

OBJECTIVES

The aim of this study was to assess quantitative ultra-high b-value (UHB) diffusion magnetic resonance imaging (MRI)-derived parameters in comparison to standard clinical apparent diffusion coefficient (SD-ADC-2b-1000, SD-ADC-2b-1500) for the prediction of clinically significant prostate cancer, defined as Gleason Grade Group greater than or equal to 2.

MATERIALS AND METHODS

Seventy-three patients who underwent 3-T prostate MRI with diffusion-weighted imaging acquired at b = 50/500/1000/1500s/mm2 and b = 100/500/1000/1500/2250/3000/4000 s/mm2 were included. Magnetic resonance lesions were segmented manually on individual sequences, then matched to targeted transrectal ultrasonography/MRI fusion biopsies. Monoexponential 2-point and multipoint fits of standard diffusion and of UHB diffusion were calculated with incremental b-values. Furthermore, a kurtosis fit with parameters Dapp and Kapp with incremental b-values was obtained. Each parameter was examined for prediction of clinically significant prostate cancer using bootstrapped receiver operating characteristics and decision curve analysis. Parameter models were compared using Vuong test.

RESULTS

Fifty of 73 men (age, 66 years [interquartile range, 61-72]; prostate-specific antigen, 6.6 ng/mL [interquartile range, 5-9.7]) had 64 MRI-detected lesions. The performance of SD-ADC-2b-1000 (area under the curve, 0.82) and SD-ADC-2b-1500 (area under the curve, 0.82) was not statistically different (P = 0.99), with SD-ADC-2b-1500 selected as reference. Compared with the reference model, none of the 19 tested logistic regression parameter models including multipoint and 2-point UHB-ADC, Dapp, and Kapp with incremental b-values of up to 4000 s/mm2 outperformed SD-ADC-2b-1500 (all P's > 0.05). Decision curve analysis confirmed these results indicating no higher net benefit for UHB parameters in comparison to SD-ADC-2b-1500 in the clinically important range from 3% to 20% of cancer threshold probability. Net reduction analysis showed no reduction of MR lesions requiring biopsy.

CONCLUSIONS

Despite evaluation of a large b-value range and inclusion of 2-point, multipoint, and kurtosis models, none of the parameters provided better predictive performance than standard 2-point ADC measurements using b-values 50/1000 or 50/1500. Our results suggest that most of the diagnostic benefits available in diffusion MRI are already represented in an ADC composed of one low and one 1000 to 1500 s/mm2 b-value.

Comparison of single-scanner single-protocol quantitative ADC measurements to ADC ratios to detect clinically significant prostate cancer

BACKGROUND

Mean ADC has high predictive value for the presence of clinically significant prostate cancer (sPC). Measurement variability is introduced by different scanners, protocols, intra-and inter-patient variation. Internal calibration by ADC ratios can address such fluctuations however can potentially lower the biological value of quantitative ADC determination by being sensitive to deviations in reference tissue signal.

PURPOSE

To better understand the predictive value of quantitative ADC measurements in comparison to internal reference ratios when measured in a single scanner, single protocol setup.

MATERIALS AND METHODS

284 consecutive patients who underwent 3 T MRI on a single scanner followed by MRI-transrectal ultrasound fusion biopsy were included. A board-certified radiologist retrospectively reviewed all MRIs blinded to clinical information and placed regions of interest (ROI) on all focal lesions and the following reference regions: normal-appearing peripheral zone (PZNL) and transition zone (TZNL), the urinary bladder (BLA), and right and left internal obturator muscle (RIOM, LIOM). ROI-based mean ADC and ADC ratios to the reference regions were compared regarding their ability to predict the aggressiveness of prostate cancer. Spearman's rank correlation coefficient was used to estimate the correlation between ADC parameters, Gleason score (GS) and ADC ratios. The primary endpoint was presence of sPC, defined as a GS ≥ 3 + 4. Univariable and multivariable logistic regression models were constructed to predict sPC. Receiver operating characteristics curves (ROC) were used for visualization; DeLong test was used to evaluate the differences of the area under the curve (AUC). Bias-corrected AUC values and corresponding 95 %-CI were calculated using bootstrapping with 100 bootstrap samples.

RESULTS

After exclusion of patients who received prior treatment, 259 patients were included in the final cohort of which 220 harbored 351 MR lesions. Mean ADC and ADC ratios demonstrated a negative correlation with the GS. Mean ADC had the strongest correlation with ρ of -0.34, followed by ADCratioPZNL (ρ=-0.32). All ADC parameters except ADCratioLIOM (p = 0.07) were associated with sPC p<0.05). Mean ADC and ADCratioPZNL had the highest ROC AUC of all parameters (0.68). Multivariable models with mean ADC improve predictive performance.

CONCLUSIONS

A highly standardized single-scanner mean ADC measurement could not be improved upon using any of the single ADC ratio parameters or combinations of these parameters in predicting the aggressiveness of prostate cancer.

Rectal gas-induced susceptibility artefacts on prostate diffusion-weighted MRI with epi read-out at 3.0 T: does a preparatory micro-enema improve image quality?

PURPOSE

To assess whether the application of a preparatory micro-enema reduces gas-induced susceptibility artefacts on diffusion-weighted MRI of the prostate.

METHODS

114 consecutive patients who received multiparametric 3 T MRI of the prostate at our institution were retrospectively enrolled. 63 patients self-administered a preparatory micro-enema prior to imaging, and 51 patients underwent MRI without bowel preparation. Two blinded readers independently reviewed the diffusion-weighted sequences regarding gas-induced artefacts. The presence/severity of artefacts was scored ranging from 0 (no artefact) to 3 (severe artefact). A score ≥ 2 was considered a clinically relevant artefact. Maximum rectal width at the level of the prostate was correlated with the administration of a micro-enema. Scores were compared between the scans performed with and without bowel preparation using univariable and multivariable logistic regression, taking into account potential confounding factors (age and prostate volume).

RESULTS

Significantly less artefacts were found on diffusion-weighted sequences after the administration of a micro-enema shortly prior to MR imaging. Clinically relevant artefacts were found in 10% in the patient group after enema, in 41% without enema. If present, artefacts were also significantly less severe. Mean severity score was 0.3 (enema administered) and 1.2 (no enema), and odds ratio was 0.137 (p < 0.0001) in univariable ordinal logistic regression. Inter-observer agreement was excellent (κ 0.801).

CONCLUSION

The use of a preparatory micro-enema prior to 3 T multiparametric prostate MRI significantly reduces both the incidence and severity of gas-induced artefacts on diffusion-weighted sequences and thus improves image quality.

Comparison of diagnostic performance between diffusion kurtosis imaging parameters and mono-exponential ADC for determination of clinically significant cancer in patients with prostate cancer

PURPOSE

To compare the diagnostic performance between diffusion kurtosis imaging (DKI) parameters and mono-exponential apparent diffusion coefficient (ADC) for determination of clinically significant cancer (CSC, Gleason score (GS) ≥ 7) in patients with histologically proven prostate cancer (PCa).

METHODS

A total of 92 patients (mean age: 71.5 years, range: 47-89 years) who had been diagnosed as PCa and undergone 3 T-MRI including DWI (b values, 0, 100, 1000, 2000s/mm²) were included in this study. The DKI parameters, namely apparent diffusion for non-Gaussian distribution (D_app) and apparent kurtosis coefficient (K_app), were calculated by dedicated software using mono-exponential and diffusion kurtosis models for quantitation. The measurement was performed for a whole tumor after segmentation, and pathologic topographic maps or systemic biopsy results served as the reference standard for segmentation. To compare the diagnostic performance of each parameter for determination of CSC, pair-wise comparison of receiver operating characteristic (ROC) curves was performed.

RESULTS

The study population consisted of GS 6 (n = 18), GS 7 (n = 31), GS 8 (n = 25), GS 9 (n = 15) and GS 10 (n = 3) patients. The area under the ROC curve of K_app (0.707, 95% CI 0.603-0.798) for discriminating CSC from non-CSC was not significantly different from those of mono-exponential ADC (0.725, 0.622-0.813, P = 0.2175) or D_app (0.726, 0.623-0.814, P = 0.9628). Diagnostic predictive values of K_app were estimated to a maximum accuracy of 78%, a sensitivity of 86%, and a specificity of 47%, while those of mono-exponential ADC were 75, 81, and 53%, respectively.

CONCLUSION

The DKI parameters showed a diagnostic performance comparable to mono-exponential ADC for determination of CSC in patients with PCa.

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.

Multiparametric MRI as a Biomarker of Response to Neoadjuvant Therapy for Localized Prostate Cancer-A Pilot Study

RATIONALE AND OBJECTIVES

To explore a role for multiparametric MRI (mpMRI) as a biomarker of response to neoadjuvant androgen deprivation therapy (ADT) for prostate cancer (PCa).

MATERIALS AND METHODS

This prospective study was approved by the institutional review board and was HIPAA compliant. Eight patients with localized PCa had a baseline mpMRI, repeated after 6-months of ADT, followed by prostatectomy. mpMRI indices were extracted from tumor and normal regions of interest (TROI/NROI). Residual cancer burden (RCB) was measured on mpMRI and on the prostatectomy specimen. Paired t-tests compared TROI/NROI mpMRI indices and pre/post-treatment TROI mpMRI indices. Spearman's rank tested for correlations between MRI/pathology-based RCB, and between pathological RCB and mpMRI indices.

RESULTS

At baseline, TROI apparent diffusion coefficient (ADC) was lower and dynamic contrast enhanced (DCE) metrics were higher, compared to NROI (ADC: 806 ± 137 × 10^-6 vs. 1277 ± 213 × 10^-6 mm²/sec, p = 0.0005; K^trans: 0.346 ± 0.16 vs. 0.144 ± 0.06 min^-1, p = 0.002; AUC₉₀: 0.213 ± 0.08 vs. 0.11 ± 0.03, p = 0.002). Post-treatment, there was no change in TROI ADC, but a decrease in TROI K^trans (0.346 ± 0.16 to 0.188 ± 0.08 min^-1; p = 0.02) and AUC₉₀ (0.213 ± 0.08 to 0.13 ± 0.06; p = 0.02). Tumor volume decreased with ADT. There was no difference between mpMRI-based and pathology-based RCB, which positively correlated (⍴ = 0.74-0.81, p < 0.05). Pathology-based RCB positively correlated with post-treatment DCE metrics (⍴ = 0.76-0.70, p < 0.05) and negatively with ADC (⍴ = -0.79, p = 0.03).

CONCLUSION

Given the heterogeneity of PCa, an individualized approach to ADT may maximize potential benefit. This pilot study suggests that mpMRI may serve as a biomarker of ADT response and as a surrogate for RCB at prostatectomy.

The role of radiomics in prostate cancer radiotherapy

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.

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

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

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

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

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

Can the combination of biparametric magnetic resonance imaging and PSA-related indicators predict the prostate biopsy outcome?

To assess the value of biparametric magnetic resonance imaging (bpMRI) for detecting and ruling out prostate cancer in patients with elevated prostate-specific antigen (PSA). The basic information and bpMRI images of enrolled patients who took transperineal template saturate biopsy were retrospectively collected for analysis. Based on our results, we found that free/total PSA, and PI-RADS score were independent risk factors of PCa (p < .05), the PSA density, PI-RADS score were the independent risk factors of csPCa (p < .05). PI-RADS score threshold of 3 could achieve the highest Yonder index for predicting PCa, and PI-RADS score threshold of 4 could achieve the highest Yonder index for predicting csPCa. Therefore, we draw a conclusion that PI-RADSv2 score-based bpMRI could diminish the unnecessary prostate biopsies in patients with elevated PSA when combined with other PSA-related indicators.

Utilization of Multiparametric MRI of Prostate in Patients under Consideration for or Already in Active Surveillance: Correlation with Imaging Guided Target Biopsy

This study sought to assess the value of multiparametric magnetic resonance image (mp-MRI) in patients with a prostate cancer (PCa) Gleason score of 6 or less under consideration for or already in active surveillance and to determine the rate of upgrading by target biopsy. Three hundred and fifty-four consecutive men with an initial transrectal ultrasound-guided (TRUS) biopsy-confirmed PCa Gleason score of 6 or less under clinical consideration for or already in active surveillance underwent mp-MRI and were retrospectively reviewed. One hundred and nineteen of 354 patients had cancer-suspicious regions (CSRs) at mp-MRI. Each CSR was assigned a Prostate Imaging Reporting and Data System (PI-RADS) score based on PI-RADS v2. One hundred and eight of 119 patients underwent confirmatory imaging-guided biopsy for CSRs. Pathology results including Gleason score (GS) and percentage of specimens positive for PCa were recorded. Associations between PI-RADS scores and findings at target biopsy were evaluated using logistic regression. At target biopsy, 81 of 108 patients had PCa (75%). Among them, 77 patients had upgrading (22%, 77 of 354 patients). One hundred and forty-six CSRs in 108 patients had PI-RADS 3 n = 28, 4 n = 66, and 5 n = 52. The upgraded rate for each category of CSR was for PI-RADS 3 (5 of 28, 18%), 4 (47 of 66, 71%) and 5 (49 of 52, 94%). Using logistic regression analysis, differences in PI-RADS scores from 3 to 5 are significantly associated with the probability of disease upgrade (20%, 73%, and 96% for PI-RADS score of 3, 4, and 5, respectively). Adding mp-MRI to patients under consideration for or already in active surveillance helps to identify undiagnosed PCa of a higher GS or higher volume resulting in upgrading in 22%.

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network

PURPOSE

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. In this paper, an efficient convolutional neural network (CNN) was developed and the model interpretation at various convolutional layers was systematically analyzed to improve the understanding of how CNN interprets multimodality medical images and the predictive powers of features at each layer. The problem of small sample size was addressed by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine (wELM), with imbalanced distribution among output categories taken into consideration.

METHODS

The training data collection used a retrospective set of prostate MR studies, from SPIE-AAPM-NCI PROSTATEx Challenges held in 2017. Three hundred twenty biopsy samples of lesions from 201 prostate cancer patients were diagnosed and identified as clinically significant (malignant) or not significant (benign). All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE) and diffusion-weighted (DW) imaging. After registration and lesion-based normalization, a CNN with four convolutional layers were developed and trained on tenfold cross validation. The features from intermediate layers were then extracted as input to wELM to test the discriminative power of each individual layer. The best performing model from the tenfolds was chosen to be tested on the holdout cohort from two sources. Feature maps after each convolutional layer were then visualized to monitor the trend, as the layer propagated. Scatter plotting was used to visualize the transformation of data distribution. Finally, a class activation map was generated to highlight the region of interest based on the model perspective.

RESULTS

Experimental trials indicated that the best input for CNN was a modality combination of T2W, apparent diffusion coefficient (ADC) and DWI_b50 . The convolutional features from CNN paired with a weighted extreme learning classifier showed substantial performance compared to a CNN end-to-end training model. The feature map visualization reveals similar findings on natural images where lower layers tend to learn lower level features such as edges, intensity changes, etc, while higher layers learn more abstract and task-related concept such as the lesion region. The generated saliency map revealed that the model was able to focus on the region of interest where the lesion resided and filter out background information, including prostate boundary, rectum, etc. CONCLUSIONS: This work designs a customized workflow for the small and imbalanced dataset of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patient stratification.

Prediction of prostate cancer aggressiveness using 18F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADCk, K), mono- (ADCm), and biexponential functions (f, Dp, Df) while Logan plots were used to calculate volume of distribution (VT). In total, 16 unique PET (VT, SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for VT was 0.85. The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18F-FACBC PET derived parameters (VT, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.

Is dynamic contrast enhancement still necessary in multiparametric magnetic resonance for diagnosis of prostate cancer: a systematic review and meta-analysis

Background

The purpose of this study is to systematically review the literatures assessing the value of dynamic contrast enhancement (DCE) in the multiparametric magnetic resonance imaging (mpMRI) for the diagnosis of prostate cancer (PCa).

Methods

We searched Embase, PubMed and Web of science until January 2019 to extract articles exploring the possibilities whether the pre-biopsy biparametric magnetic resonance imaging (bpMRI) can replace the position of mpMRI in the diagnosis of PCa. The sensitivity and specificity of bpMRI were all included. The study quality was assessed by QUADAS-2. Bivariate random effects meta-analyses and a hierarchical summary receiver operating characteristic plot were performed for further study through Revman 5 and Stata12.

Results

After searching, we acquired 752 articles among which 45 studies with 5,217 participants were eligible for inclusion. The positive likelihood ratio for the detection of PCa was 2.40 (95% CI: 1.50–3.80) and the negative likelihood ratio was 0.31 (95% CI: 0.18–0.53). The sensitivity and specificity were 0.77 (95% CI: 0.73–0.81) and 0.81 (95% CI: 0.76–0.85) respectively. Based on our result, pooled specificity demonstrated little difference between bpMRI and mpMRI [bpMRI, 0.81 (95% CI, 0.76–0.85); mpMRI, 0.82 (95% CI, 0.72–0.88); P=0.169]. The sensitivity, however, indicated a significant difference between these two groups [bpMRI, 0.77 (95% CI, 0.73–0.81); mpMRI, 0.84 (95% CI, 0.78–0.89); P=0.001].

Conclusions

bpMRI with high b-value is a sensitive tool for diagnosing PCa. Consistent results were found in multiple subgroup analysis.

Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer

Background

The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature.

Purpose

To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer.

Material and Methods

Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group.

Results

Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix.

Conclusions

Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.

Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes

OBJECTIVE

To compare observation size and apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) v2.1 category 4 and 5 observations to adverse pathological features.

MATERIALS AND METHODS

With institutional review board approval, 267 consecutive men with 3-T MRI before radical prostatectomy (RP) between 2012 and 2018 were evaluated by two blinded radiologists who assigned PI-RADS v2.1 scores. Discrepancies were resolved by consensus. A third blinded radiologist measured observation size and ADC (ADC.mean, ADC.min [lowest ADC within an observation], ADC.ratio [ADC.mean/ADC.peripheral zone {PZ}]). Size and ADC were compared to pathological stage and Gleason score (GS) using t tests, ANOVA, Pearson correlation, and receiver operating characteristic (ROC) analysis.

RESULTS

Consensus review identified 267 true positive category 4 and 5 observations representing 83.1% (222/267) PZ and 16.9% (45/267) transition zone (TZ) tumors. Inter-observer agreement for PI-RADS v2.1 scoring was moderate (K = 0.45). Size was associated with extra-prostatic extension (EPE) (19 ± 8 versus 14 ± 6 mm, p < 0.001) and seminal vesicle invasion (SVI) (24 ± 9 versus 16 ± 7 mm, p < 0.001). Size ≥ 15 mm optimized the accuracy for EPE with area under the ROC curve (AUC) and sensitivity/specificity of 0.68 (CI 0.62-0.75) and 63.2%/65.6%. Size ≥ 19 mm optimized the accuracy for SVI with AUC/sensitivity/specificity of 0.75 (CI 0.66-0.83)/69.4%/70.6%. ADC metrics were not associated with pathological stage. Larger observation size (p = 0.032), lower ADC.min (p = 0.010), and lower ADC.ratio (p = 0.010) were associated with higher GS. Size correlated better to higher Gleason scores (p = 0.002) compared to ADC metrics (p = 0.09-0.11).

CONCLUSION

Among PI-RADS v2.1 category 4 and 5 observations, size was associated with higher pathological stage whereas ADC metrics were not. Size, ADC.minimum, and ADC.ratio differed in tumors stratified by Gleason score.

KEY POINTS

• Among PI-RADS category 4 and 5 observations, size but not ADC can differentiate between tumors by pathological stage. • An observation size threshold of 15 mm and 19 mm optimized the accuracy for diagnosis of extra-prostatic extension and seminal vesicle invasion. • Among PI-RADS category 4 and 5 observations, size, ADC.minimum, and ADC.ratio differed comparing tumors by Gleason score.

MRI of the Prostate With and Without Endorectal Coil at 3 T: Correlation With Whole-Mount Histopathologic Gleason Score

OBJECTIVE. The purpose of this article is to prospectively compare image quality and diagnostic accuracy of clinically significant prostate cancer with and without endorectal coil (ERC) at 3 T using a combination of T2-weighted and diffusion-weighted MRI. SUBJECTS AND METHODS. Twenty-three patients with biopsy-proven prostate cancer underwent MRI with and without ERC at the same visit. Patients subsequently underwent radical prostatectomy. Specimens were assessed by whole-mount histopathologic examination. Two radiologists reviewed MR images for image quality (5-point scale) and disease using Prostate Imaging Reporting and Data Systems version 2 (PI-RADSv2). Sensitivity, specificity, and area under the ROC curve (AUC) were calculated with and without ERC. Additionally, apparent diffusion coefficient (ADC) was correlated with Gleason score and ADC values of each lesion were compared with and without ERC. RESULTS. Image quality was comparable with and without ERC (3.8 vs 3.5). Twenty-nine cancer foci larger than 0.5 cm in diameter were found in 23 patients on histopathologic examination; 18 tumors had a Gleason score of 7 or greater. Two radiologists recorded AUC for tumors with a Gleason score of 7 or greater as 0.96 and 0.96 with ERC and 0.88 and 0.91 without ERC. All 13 tumors with a Gleason score of 3 + 4 were detected with ERC, but only 9 were detected without ERC. One of five tumors with Gleason scores less than 3 + 4 was missed with and without ERC. ADC significantly correlated with Gleason score. There was no significant difference in the ADC of a lesion on MRI with and without an ERC. CONCLUSION. MRI with and without ERC was equally accurate at showing prostate cancers with Gleason scores of 4 + 3 or greater. However, MRI with ERC was superior at showing cancer with a Gleason score of 3 + 4. There was no significant difference in ADC values between scores acquired with or without an ERC.

Comparison of double inversion recovery magnetic resonance imaging (DIR-MRI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in detection of prostate cancer: A pilot study

INTRODUCTION

DCE-MRI is established for detecting prostate cancer (PCa). However, it requires a gadolinium contrast agent, with potential risks for patients. The application of DIR-MRI is simple and may allow cancer detection without the use of an intravenous contrast agent by differentially nullifying signal from normal and abnormal prostate tissue, creating contrast between the cancer and background normal prostate. In this pilot study we gathered data from DIR-MRI and DCE-MRI of the prostate for an equivalence trial. We also looked at how the DIR-MRI appearance varies with the aggressiveness of PCa.

METHOD

DIR-MRI and DCE-MRI were acquired. The images were assessed by an experienced Consultant Radiologist and a novice reporter (Radiographer). The potential PCa lesions were quantified using a lesion to normal ratio (LNR). Radiological pathological correlation was made to identify the MRI lesions that represented significant PCa. A Wilcoxon sign rank was used to compare DCE-LNR and DIR-LNR for PCa containing lesions. Pearson's correlation was used to look at the relationship between DIR-LNR and PCa grade group (aggressiveness).

RESULTS

DCE-LNR and DIR-LNR were found to be significantly different (Z = -5.910, p < 0.001). However, a significant correlation was found between PCa grade group and DIR-LNR.

CONCLUSION

DIR and DCE sequences are not equivalent and significant cancer is more conspicuous on the DCE sequence. However, DIR-LNR does correlate with PCa aggressiveness.

IMPLICATIONS FOR PRACTICE

With the correlation of PCa grade group with DIR-LNR this may be a useful sequence in evaluation of the prostate; stratifying the risk of there being clinically significant PCa before biopsy is performed. Furthermore, given that DIR-LNR appears to predict PCa aggressiveness DIR might be used as part of a multiparametric MRI protocol designed to avoid biopsy.

Synthetic Apparent Diffusion Coefficient for High b-Value Diffusion-Weighted MRI in Prostate

Purpose

It has been reported that diffusion-weighted imaging (DWI) with ultrahigh b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher Materials and Methods. Fifteen patients (7 malignant and 8 benign) were included in this study retrospectively with the institutional ethical committee approval. All images were acquired at a 3T MR scanner. The ADC values were calculated using a monoexponential model. Synthetic ADC (sADC) for higher b-value increases the diagnostic power of prostate cancer. DWI with higher

Results

No significant difference was observed between actual ADC and sADC for b-value increases the diagnostic power of prostate cancer. DWI with higher p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (Discussion/

Conclusion

Our initial investigation suggests that the ADC values corresponding to higher b-value can be computed using log-linear relationship derived from lower b-values (b ≤ 1000). Our method might help clinicians to decide the optimal b-value for prostate lesion identification.b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher

Diagnosis and Grading of Prostate Cancer by Relaxation Maps From Synthetic MRI

BACKGROUND

The interpretation system for prostate MRI is largely based on qualitative image contrast of different tissue types. Therefore, a fast, standardized, and robust quantitative technique is necessary. Synthetic MRI is capable of quantifying multiple relaxation parameters, which might have potential applications in prostate cancer (PCa).

PURPOSE

To investigate the use of quantitative relaxation maps derived from synthetic MRI for the diagnosis and grading of PCa.

STUDY TYPE

Prospective.

SUBJECTS

In all, 94 men with pathologically confirmed PCa or benign pathological changes.

FIELD STRENGTH/SEQUENCE

T₁ -weighted imaging, T₂ -weighted imaging, diffusion-weighted imaging, and synthetic MRI at 3.0T.

ASSESSMENT

Four kinds of tissue types were identified on pathology, including PCa, stromal hyperplasia (SH), glandular hyperplasia (GH), and noncancerous peripheral zone (PZ). PCa foci were grouped as low-grade (LG, Gleason score ≤6) and intermediate/high-grade (HG, Gleason score ≥7). Regions of interest were manually drawn by two radiologists in consensus on parametric maps according to the pathological results.

STATISTICAL TESTS

Independent sample t-test, Mann-Whitney U-test, and receiver operating characteristic curve analysis.

RESULTS

T₁ and T₂ values of PCa were significantly lower than SH (P = 0.015 and 0.002). The differences of T₁ and T₂ values between PCa and noncancerous PZ were also significant (P ≤ 0.006). The area under the curve (AUC) of the apparent diffusion coefficient (ADC) value was significantly higher than T₁ , T₂ , and proton density (PD) values in discriminating PCa from SH and noncancerous PZ (P ≤ 0.025). T₂ , PD, and ADC values demonstrated similar diagnostic performance in discriminating LG from HG PCa (AUC = 0.806 [0.640-0.918], 0.717 [0.542-0.854], and 0.817 [0.652-0.925], respectively; P ≥ 0.535).

DATA CONCLUSION

Relaxation maps derived from synthetic MRI were helpful for discriminating PCa from other benign pathologies. But the overall diagnostic performance was inferior to the ADC values. T₂ , PD, and ADC values performed similarly in discriminating LG from HG PCa lesions.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:552-564.