Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.

Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy

OBJECTIVE

Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach.

METHODS

We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE).

RESULTS

Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm.

CONCLUSION

SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.

Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancer

OBJECTIVE

To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer.

METHODS

Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9-10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer.

RESULTS

Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9-10 (P < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9-10). The entropy and energy values correlate with GS (r = 0.767, r = -0.692, P < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8.

CONCLUSION

Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency.

Carbon Flux as a Measure of Prostate Cancer Aggressiveness: [11C]-Acetate PET/CT

Purpose: Dynamic [¹¹C]-acetate positron emission tomography (PET) can be used to study tissue perfusion and carbon flux simultaneously. In this study, the feasibility of the quantification of prostate cancer aggressiveness using parametric methods assessing [¹¹C]-acetate kinetics was investigated in prostate cancer subjects. The underlying uptake mechanism correlated with [¹¹C]-acetate influx and efflux measured in real-time in vitro in cell culture. Methods: Twenty-one patients with newly diagnosed low-to-moderate risk prostate cancer underwent magnetic resonance imaging (MRI) and dynamic [¹¹C]-acetate PET/CT examinations of the pelvis. Parametric images of K₁ (extraction × perfusion), k₂ (oxidative metabolism) and V_T (=K₁/k₂, anabolic metabolism defined as carbon retention) were constructed using a one-tissue compartment model with an arterial input function derived from pelvic arteries. Regions of interest (ROIs) of the largest cancer lesion in each patient and normal prostate tissue were drawn using information from MRI (T2 and DWI images), biopsy results, and post-surgical histopathology of whole prostate sections (n=7). In vitro kinetics of [¹¹C]-acetate were studied on DU145 and PC3 cell lines using LigandTracer^® White equipment for the measurement of the radioactivity uptake in real-time at 37°C. Results: Mean prostate specific antigen (PSA) was 8.33±3.92 ng/mL and median Gleason Sum 6 (range 5-7). K₁, V_T and standardized uptake values (SUVs) were significantly higher in cancerous prostate tissues compared to normal ones for all patients (p<0.001), while k₂ was not (p=0.26). PSA values correlated to early SUVs (r=0.50, p=0.02) and K₁ (r=0.48, p=0.03). Early and late SUVs correlated to V_T (r>0.76, p<0.001) and K₁ (r>0.64, p<0.005). In vitro studies demonstrated higher extraction and retention (p<0.01) of [¹¹C]-acetate in the more aggressive PC3 cells. Conclusion: Parametric images could be used to visualize the [¹¹C]-acetate kinetics of the prostate cancer exhibiting elevated extraction associated with the cancer aggressiveness. The influx rate of [¹¹C]-acetate studied in cell culture also showed dependence on the cancer aggressiveness associated with elevated lipogenesis. Dynamic [¹¹C]-acetate/PET demonstrated potential for prostate cancer aggressiveness estimation using parametric-based K₁ and V_T values.

Accelerated Segmented Diffusion-Weighted Prostate Imaging for Higher Resolution, Higher Geometric Fidelity, and Multi-b Perfusion Estimation

PURPOSE

The aim of this study was to improve the geometric fidelity and spatial resolution of multi-b diffusion-weighted magnetic resonance imaging of the prostate.

MATERIALS AND METHODS

An accelerated segmented diffusion imaging sequence was developed and evaluated in 25 patients undergoing multiparametric magnetic resonance imaging examinations of the prostate. A reduced field of view was acquired using an endorectal coil. The number of sampled diffusion weightings, or b-factors, was increased to allow estimation of tissue perfusion based on the intravoxel incoherent motion (IVIM) model. Apparent diffusion coefficients measured with the proposed segmented method were compared with those obtained with conventional single-shot echo-planar imaging (EPI).

RESULTS

Compared with single-shot EPI, the segmented method resulted in faster acquisition with 2-fold improvement in spatial resolution and a greater than 3-fold improvement in geometric fidelity. Apparent diffusion coefficient values measured with the novel sequence demonstrated excellent agreement with those obtained from the conventional scan (R = 0.91 for bmax = 500 s/mm and R = 0.89 for bmax = 1400 s/mm). The IVIM perfusion fraction was 4.0% ± 2.7% for normal peripheral zone, 6.6% ± 3.6% for normal transition zone, and 4.4% ± 2.9% for suspected tumor lesions.

CONCLUSIONS

The proposed accelerated segmented prostate diffusion imaging sequence achieved improvements in both spatial resolution and geometric fidelity, along with concurrent quantification of IVIM perfusion.

Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I2 statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

Head-to-head comparison of prostate MRI using an endorectal coil versus a non-endorectal coil: meta-analysis of diagnostic performance in staging T3 prostate cancer

AIM

To compare the diagnostic performance of prostate magnetic resonance imaging (MRI) with an endorectal coil (ERC) to performance without an ERC using either body-array (BAC) or pelvic phased-array coil (PAC) in staging T3 prostate cancer.

MATERIALS AND METHODS

An electronic search of the PUBMED and EMBASE databases was performed until 10 October 2018 to identify studies performing a head-to-head comparison of prostate MRI using a 1.5 or 3 T magnet with an ERC and with a BAC/PAC for staging T3 prostate cancer. Pooled sensitivity and specificity of all studies were plotted in a hierarchical summary receiver operating characteristic plot. The diagnostic performance of the two techniques in staging T3 disease was evaluated using bivariate random-effects meta-analysis.

RESULTS

Eight studies comparing head-to-head prostate MRI with an ERC and with a BAC/PAC were identified of which six studies compared the diagnostic performance. The pooled sensitivity and specificity of MRI with an ERC for detecting T3a, T3b and T3a+b was 53% and 95%; 52% and 92%; 72% and 65% respectively. For MRI with a BAC/PAC these were 34%, and 95%; 45% and 94%; 70% and 66%. There was no statistical difference between an ERC and a BAC/PAC in terms of sensitivity (p=0.41) and specificity (p=0.63) for T3a. The area under the receiver operating characteristic (AUROC) curve for T3a, T3b and T3a+b was 0.830, 0.901, 0.741 for an ERC and 0.790, 0.645, 0.711 for BAC, respectively.

CONCLUSION

There is no significant difference in the diagnostic performance of MRI of prostate with an ERC and with a BAC/PAC in staging T3 prostate cancer.

Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images

PURPOSE

To explore the clinical feasibility of predicting the efficacy of neoadjuvant chemoradiotherapy (nCRT) for rectal cancer on the basis of texture analysis (TA) of T2-weighted imaging (T2WI).

METHODS

The cohort for this prospective study comprised 136 patients with rectal cancer to be treated with nCRT, all of whom underwent three MR scans (pre-, early, and post-nCRT). Treatment efficacy was assessed on the basis of the outcomes of pathologic complete response (pCR) and non-pCR as determined by postoperative pathological examination. Extraction and analysis of texture features in T2WI of defined tumor regions were performed by AK software. Pre- and early-nCRT texture features were selected as potential predictors of outcomes by logistic regression analysis, and a prediction model for pCR was developed. A receiver operating characteristic (ROC) curve was used to assess the predictive power of texture features in pre- and early-nCRT images.

RESULTS

Univariate logistic regression analysis demonstrated that the pre-nCRT features of energy, entropy, and skewness, and early-nCRT features of variance, kurtosis, energy, and entropy were independent predictors of pCR. A prediction model incorporating these predictors was constructed by multivariate logistic regression, The AUCs of pre-nCRT, early, and combined models were 0.751, 0.831, and 0.873, respectively; the sensitivities 66, 71, and 75%, respectively; and the specificities 87.22, 86.11, and 91.67%, respectively.

CONCLUSIONS

TA of T2WI images can predict the efficacy of nCRT for rectal cancer, possibly providing a new marker of tumor biological response in clinical practice.

Modern imaging and image-guided treatments of the prostate gland: MR and ablation for cancer and prostatic artery embolization for benign prostatic hyperplasia

Multiparametric MRI (mpMRI) has proven to be an essential tool for diagnosis, post-treatment follow-up, aggressiveness assessment, and active surveillance of prostate cancer. Currently, this imaging technique is part of the daily practice in many oncological centres. This manuscript aims to review the use of mpMRI in the set of prostatic diseases, either malignant or benign: mpMRI to detect and stage prostate cancer is discussed, as well as its use for active surveillance. Image-guided ablation techniques for prostate cancer are also reviewed. The need to establish minimum acceptable technical parameters for prostate mpMRI, standardize reports, uniform terminology for describing imaging findings, and develop assessment categories that differentiate levels of suspicion for clinically significant prostate cancer led to the development of the Prostate Imaging Reporting and Data System that is reviewed. Special focus will also be given on the most up-to-date evidence of prostatic artery embolization (PAE) for symptomatic benign prostatic hyperplasia (BPH). Management of patients with BPH, technical aspects of PAE, expected outcomes and level of evidence are reviewed with the most recent literature. PAE is a challenging technique that requires dedicated anatomical knowledge and comprehensive embolization skills. PAE has been shown to be an effective minimally-invasive treatment option for symptomatic BPH patients, that can be viewed between medical therapy and surgery. PAE may be a good option for symptomatic BPH patients that do not want to be operated and can obviate the need for prostatic surgery in up to 80% of treated patients.

Accelerated 3D T2 w-imaging of the prostate with 1-millimeter isotropic resolution in less than 3 minutes

PURPOSE

To achieve 3D T2 w imaging of the prostate with 1-mm isotropic resolution in less than 3 min.

METHODS

We devised and implemented a 3D T2 -prepared multishot balanced steady state free precession (T2 prep-bSSFP) acquisition sequence with a variable density undersampled trajectory combined with a total variation regularized iterative SENSE (TV-SENSE) reconstruction. Prospectively undersampled images of the prostate (acceleration factor R = 3) were acquired in 11 healthy subjects in an institutional review board-approved study. Image quality metrics (subjective signal-to-noise ratio, contrast, sharpness, and overall prostate image quality) were evaluated by 2 radiologists. Scores of the proposed accelerated sequence were compared using the Wilcoxon signed-rank and Kruskal-Wallis non-parametric tests to prostate images acquired using a fully sampled 3D T2 prep-bSSFP acquisition, and with clinical standard 2D and 3D turbo spin echo (TSE) T2 w acquisitions. A P-value < 0.05 was considered significant.

RESULTS

The 3× accelerated 3D T2 prep-bSSFP images required a scan time (min:s) of 2:45, while the fully sampled 3D T2 prep-bSSFP and clinical standard 3D TSE images were acquired in 8:23 and 7:29, respectively. Image quality scores (contrast, sharpness, and overall prostate image quality) of the accelerated 3D T2 prep-bSSFP, fully sampled T2 prep-bSSFP, and clinical standard 3D TSE acquisitions along all 3 spatial dimensions were not significantly different (P > 0.05).

CONCLUSION

3D T2 w images of the prostate with 1-mm isotropic resolution can be acquired in less than 3 min, with image quality that is comparable to a clinical standard 3D TSE sequence but only takes a third of the acquisition time.

MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases

Synchronous liver metastasis (SLM) remains a major challenge for rectal cancer. Early detection of SLM is a key factor to improve the survival rate of rectal cancer. In this radiomics study, we predicted the SLM based on the radiomics of primary rectal cancer. A total of 328 radiomics features were extracted from the T2WI images of 194 patients. The least absolute shrinkage and selection operator (LASSO) regression was used to reduce the feature dimension and to construct the radiomics signature. after LASSO, principal component analysis (PCA) was used to sort the features of the surplus characteristics, and selected the features of the total contribution of 85%. Then the prediction model was built by linear regression, and the decision curve analysis was used to judge the net benefit of LASSO and PCA. In addition, we used two independent cohorts for training (n = 135) and validation (n = 159). We found that the model based on LASSO dimensionality construction had the maximum net benefit (in the training set (AUC [95% confidence interval], 0.857 [0.787–0.912]) and in the validation set (0.834 [0.714–0.918]). The radiomics nomogram combined with clinical risk factors and LASSO features showed a good predictive performance in the training set (0.921 [0.862–0.961]) and validation set (0.912 [0.809–0.97]). Our study indicated that radiomics based on primary rectal cancer could provide a non-invasive way to predict the risk of SLM in clinical practice.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate K^trans (volume transfer rate constant), v_e (extravascular, extracellular volume fraction), k_ep (efflux rate constant), and τ_i (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T₁ values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for K^trans, v_e, k_ep, and τ_i, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in K^trans and v_e (wCV = 0.50 and 0.10, respectively), but had smaller effects on k_ep and τ_i (wCV = 0.39 and 0.22, respectively). k_ep is less sensitive to AIF variation than K^trans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τ_i parameter may have advantages over the conventional PK parameters in a longitudinal study.

Accelerated 3D T2 mapping with dictionary-based matching for prostate imaging

PURPOSE

To develop a fast and accurate method for 3D T2 mapping of prostate cancer using undersampled acquisition and dictionary-based fitting.

METHODS

3D high-resolution T2 -weighted images (0.9 × 0.9 × 3 mm3 ) were obtained with a multishot T2 -prepared balanced steady-state free precession (T2 -prep-bSSFP) acquisition sequence using a 3D variable density undersampled Cartesian trajectory. Each T2 -weighted image was reconstructed using total variation regularized sensitivity encoding. A flexible simulation framework based on extended phase graphs generated a dictionary of magnetization signals, which was customized to the proposed sequence. The dictionary was matched to the acquired T2 -weighted images to retrieve quantitative T2 values, which were then compared to gold-standard spin echo acquisition values using monoexponential fitting. The proposed approach was validated in simulations and a T1 /T2 phantom, and feasibility was tested in 8 healthy subjects.

RESULTS

The simulation analysis showed that the proposed T2 mapping approach is robust to noise and typically observed T1 variations. T2 values obtained in the phantom with T2 prep-bSSFP and the acquisition-specific, dictionary-based matching were highly correlated with the gold-standard spin echo method (r = 0.99). Furthermore, no differences were observed with the accelerated acquisition compared to the fully sampled acquisition (r = 0.99). T2 values obtained in prostate peripheral zone, central gland, and muscle in healthy subjects (age, 26 ± 6 years) were 97 ± 14, 76 ± 7, and 36 ± 3 ms, respectively.

CONCLUSION

3D quantitative T2 mapping of the whole prostate can be achieved in 3 minutes.

Metabolomics Biomarkers of Prostate Cancer: A Systematic Review

Prostate cancer (PCa) diagnosis with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment, highlighting the need for novel biomarkers. The aim of this review is to provide a summary of available metabolomics PCa biomarkers, particularly for clinically significant disease. A systematic search was conducted on PubMed for publications from July 2008 to July 2018 in accordance with PRISMA guidelines to report biomarkers with respect to their application in PCa diagnosis, progression, aggressiveness, recurrence, and treatment response. The vast majority of studies report biomarkers with the ability to distinguish malignant from benign prostate tissue with a few studies investigating biomarkers associated with disease progression, treatment response or tumour recurrence. In general, these studies report high dimensional datasets and the number of analysed metabolites often significantly exceeded the number of available samples. Hence, observed multivariate differences between case and control samples in the datasets might potentially also be associated with pre-analytical, technical, statistical and confounding factors. Giving the technical and methodological hurdles, there are nevertheless a number of metabolites and pathways repeatedly reported across various technical approaches, cohorts and sample types that appear to play a predominant role in PCa tumour biology, progression and recurrence.

Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

PURPOSE

To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard.

METHODS

We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3.

RESULTS

119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively.

CONCLUSIONS

ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

Adding multiparametric MRI to the MSKCC and Partin nomograms for primary prostate cancer: Improving local tumor staging?

INTRODUCTION AND OBJECTIVES

As a single diagnostic modality, multiparametric MRI (mpMRI) has imperfect accuracy to detect locally advanced prostate cancer (T-stages 3-4). In this study we evaluate if combining mpMRI with preoperative nomograms (Memorial Sloan Kettering Cancer Center [MSKCC] and Partin) improves the prediction of locally advanced tumors.

MATERIALS AND METHODS

Preoperative mpMRI results of 430 robot-assisted radical prostatectomy patients were analyzed. MSKCC and Partin nomogram scores predicting extraprostatic growth were calculated. Logistic regression analysis was performed, combining the nomogram prediction scores with mpMRI results. The diagnostic value of the combined models was evaluated by creating receiver operator characteristics curves and comparing the area under the curve (AUC).

RESULTS

mpMRI was a significant predictor of locally advanced disease in addition to both the MSKCC and Partin nomogram, despite its low sensitivity (45.3%). However, overall predictive accuracy increased by only 1% when mpMRI was added to the MSKCC nomogram (AUC MSKCC 0.73 vs MSKCC + mpMRI 0.74). Predictive accuracy for the Partin Tables increased 4% (AUC Partin 0.62 vs Partin + mpMRI 0.66).

CONCLUSION

The addition of mpMRI to the preoperative MSKCC and Partin nomograms did not increase diagnostic accuracy for the prediction of locally advanced prostate cancer.

Magnetic Resonance Imaging only Workflow for Radiotherapy Simulation and Planning in Prostate Cancer

Magnetic resonance imaging (MRI) is often combined with computed tomography (CT) in prostate radiotherapy to optimise delineation of the target and organs-at-risk (OAR) while maintaining accurate dose calculation. Such a dual-modality workflow requires two separate imaging sessions, and it has some fundamental and logistical drawbacks. Due to the availability of new MRI hardware and software solutions, CT examinations can be omitted for prostate radiotherapy simulations. All information for treatment planning, including electron density maps and bony anatomy, can nowadays be obtained with MRI. Such an MRI-only simulation workflow reduces delineation ambiguities, eases planning logistics, and improves patient comfort; however, careful validation of the complete MRI-only workflow is warranted. The first institutes are now adopting this MRI-only workflow for prostate radiotherapy. In this article, we will review technology and workflow requirements for an MRI-only prostate simulation workflow.

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2

Background

Multiparametric MRI (mp‐MRI) combined with machine‐aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics‐based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI‐RADS v2) scores.

Purpose

To develop and validate a radiomics‐based model for differentiating PCa and assessing its aggressiveness compared with PI‐RADS v2 scores.

Study Type

Retrospective.

Population

In all, 182 patients with biopsy‐proven PCa and 199 patients with a biopsy‐proven absence of cancer were enrolled in our study.

Field Strength/Sequence

Conventional and diffusion‐weighted MR images (b values = 0, 1000 sec/mm²) were acquired on a 3.0T MR scanner.

Assessment

A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T₂WI, respectively. A predictive model was constructed for differentiating PCa from non‐PCa and high‐grade from low‐grade PCa. The diagnostic performance of each radiomics‐based model was compared with that of the PI‐RADS v2 scores.

Statistical Tests

A radiomics‐based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.

Results

For PCa versus non‐PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T₂WI, ADC, and T₂WI&ADC features, respectively. For low‐grade versus high‐grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T₂WI, ADC, and T₂WI&ADC features, respectively. PI‐RADS v2 had an AUC of 0.867 in differentiating PCa from non‐PCa and an AUC of 0.763 in differentiating high‐grade from low‐grade PCa.

Data Conclusion

Both the T₂WI‐ and ADC‐based radiomics models showed high diagnostic efficacy and outperformed the PI‐RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high‐grade vs. low‐grade PCa.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:875–884.

Prostate imaging features that indicate benign or malignant pathology on biopsy

Accurate diagnosis of clinically significant prostate cancer is essential in identifying patients who should be offered treatment with curative intent. Modifications to the Gleason grading system in recent years show that accurate grading and reporting at needle biopsy can improve identification of clinically significant prostate cancers. Extracapsular extension of prostate cancer has been demonstrated to be an adverse prognostic factor with greater risk of metastatic spread than organ-confined disease. Tumor volume may be an independent prognostic factor and should be considered in conjunction with other factors. Multi-parametric magnetic resonance imaging (MP-MRI) has become an increasingly important tool in the diagnosis and characterization of prostate cancer. MP-MRI allows T2-weighted (T2W) anatomical imaging to be combined with functional and physiological assessment. Diffusion-weighted imaging (DWI) has shown greater sensitivity, specificity and negative predictive value compared to prostate specific antigen (PSA) testing and T2W imaging alone and has a more positive correlation with Gleason score and tumour volume. Dynamic gadolinium contrast-enhanced (DCE) imaging can exhibit difficulties in distinguishing prostatitis from malignancy in the peripheral zone, and between benign prostatic hyperplasia (BPH) and malignancies in the transition zone (TZ). Computer aided diagnosis utilizes software to aid radiologists in detecting and diagnosing abnormalities from diagnostic imaging. New techniques of quantitative MRI, such as VERDICT MRI use tissue-specific factors to delineate different cellular and microstructural phenotypes, characterizing tissue properties with greater detail. Proton MR spectroscopic imaging (MRSI) is a more technically challenging imaging modality than DCE and DWI MRI. Over the last decade, choline and prostate-specific membrane antigen (PSMA) positron emission tomography (PET) have developed as better tools for staging than conventional imaging. While hyperpolarized MRI shows promise in improving the imaging and differentiation of benign and malignant lesions there is further work required. Accurate reading and interpretation of diagnostic investigations is key to accurate identification of abnormal areas requiring biopsy, sparing those in whom benign or indolent disease can be managed by non-invasive means. Embracing and advancing existing technologies is essential in furthering this process.

Characterization of prostate cancer with MR spectroscopic imaging and diffusion-weighted imaging at 3 Tesla

PURPOSE

To retrospectively measure metabolic ratios and apparent diffusion coefficient (ADC) values from 3-Tesla MR spectroscopic imaging (MRSI) and diffusion-weighted imaging (DWI) in benign and malignant peripheral zone (PZ) prostate tissue, assess the parameters' associations with malignancy, and develop and test rules for classifying benign and malignant PZ tissue using whole-mount step-section pathology as the reference standard.

METHODS

This HIPAA-compliant, IRB-approved study included 67 men (median age, 61 years; range, 41-74 years) with biopsy-proven prostate cancer who underwent preoperative 3 T endorectal multiparametric MRI and had ≥1 PZ lesion >0.1 cm³ at whole-mount histopathology. In benign and malignant PZ regions identified from pathology, voxel-based choline/citrate, polyamines/choline, polyamines/creatine, and (choline + polyamines + creatine)/citrate ratios were averaged, as were ADC values. Patients were randomly split into training and test sets; rules for separating benign from malignant regions were generated with classification and regression tree (CART) analysis and assessed on the test set for sensitivity and specificity. Odds ratios (OR) were evaluated using generalized estimating equations.

RESULTS

CART analysis of all parameters identified only ADC and (choline + polyamines + creatine)/citrate as significant predictors of cancer. Sensitivity and specificity, respectively, were 0.81 and 0.82 with MRSI-derived, 0.98 and 0.51 with DWI-derived, and 0.79 and 0.90 with MRSI + DWI-derived classification rules. Areas under the curves (AUC) in the test set were 0.93 (0.87-0.97) with ADC, 0.82 (0.72-0.91) with MRSI, and 0.96 (0.92-0.99) with MRSI + ADC.

CONCLUSION

We developed statistically-based rules for identifying PZ cancer using 3-Tesla MRSI, DWI, and MRSI + DWI and demonstrated the potential value of MRSI + DWI.

Towards a universal MRI atlas of the prostate and prostate zones : Comparison of MRI vendor and image acquisition parameters

BACKGROUND AND PURPOSE

The aim of this study was to evaluate an automatic multi-atlas-based segmentation method for generating prostate, peripheral (PZ), and transition zone (TZ) contours on MRIs with and without fat saturation (±FS), and compare MRIs from different vendor MRI systems.

METHODS

T2-weighted (T2) and fat-saturated (T2FS) MRIs were acquired on 3T GE (GE, Waukesha, WI, USA) and Siemens (Erlangen, Germany) systems. Manual prostate and PZ contours were used to create atlas libraries. As a test MRI is entered, the procedure for atlas segmentation automatically identifies the atlas subjects that best match the test subject, followed by a normalized intensity-based free-form deformable registration. The contours are transformed to the test subject, and Dice similarity coefficients (DSC) and Hausdorff distances between atlas-generated and manual contours were used to assess performance.

RESULTS

Three atlases were generated based on GE_T2 (n = 30), GE_T2FS (n = 30), and Siem_T2FS (n = 31). When test images matched the contrast and vendor of the atlas, DSCs of 0.81 and 0.83 for T2 ± FS were obtained (baseline performance). Atlases performed with higher accuracy when segmenting (i) T2FS vs. T2 images, likely due to a superior contrast between prostate vs. surrounding tissue; (ii) prostate vs. zonal anatomy; (iii) in the mid-gland vs. base and apex. Atlases performance declined when tested with images with differing contrast and MRI vendor. Conversely, combined atlases showed similar performance to baseline.

CONCLUSION

The MRI atlas-based segmentation method achieved good results for prostate, PZ, and TZ compared to expert contoured volumes. Combined atlases performed similarly to matching atlas and scan type. The technique is fast, fully automatic, and implemented on commercially available clinical platform.

68Ga-PSMA-PET: added value and future applications in comparison to the current use of choline-PET and mpMRI in the workup of prostate cancer

Positron emission tomography (PET) has been commonly and successfully used, in combination with computed tomography (CT) and more recently magnetic resonance (MRI), in the workup of intermediate or high-risk prostate cancer (PCa). Nowadays, new specific receptor targeted PET tracers in prostate cancer imaging have been introduced; one of the most used is ⁶⁸Ga-PSMA, that evaluates the expression of prostate-specific membrane antigen (PSMA). This tracer has been rapidly taken into account for its better sensitivity and specificity compared to lipid metabolism tracers, such as ¹¹C/¹⁸F labelled fluorocholine. Besides, in the era of theranostics, this tracer is having a useful application not only for imaging but also for therapeutic purposes. The aim of this review article is, in the first part, to give an overview of the main indications and future development of ⁶⁸Ga-PSMA imaging, using PET/CT or PET/MRI, according to the clinical course of the disease and in view of the current use of multiparametric MRI (mpMRI) and choline PET in the management of PCa. In the second part, a brief overview of the promising ¹⁸F-labelled PSMA tracers and the current use of PSMA radionuclide therapy will be provided.

A system using patient-specific 3D-printed molds to spatially align in vivo MRI with ex vivo MRI and whole-mount histopathology for prostate cancer research

BACKGROUND

Patient-specific 3D-printed molds and ex vivo MRI of the resected prostate have been two important strategies to align MRI with whole-mount histopathology (WMHP) for prostate cancer (PCa) research, but the combination of these two strategies has not been systematically evaluated.

PURPOSE

To develop and evaluate a system that combines patient-specific 3D-printed molds with ex vivo MRI (ExV) to spatially align in vivo MRI (InV), ExV, and WMHP in PCa patients.

STUDY TYPE

Prospective cohort study.

POPULATION

Seventeen PCa patients who underwent 3T MRI and robotic-assisted laparoscopic radical prostatectomy (RALP).

FIELD STRENGTH/SEQUENCES

T₂ -weighted turbo spin-echo sequences at 3T.

ASSESSMENT

Immediately after RALP, the fresh whole prostate specimens were imaged in patient-specific 3D-printed molds by 3T MRI and then sectioned to create WMHP slides. The time required for ExV was measured to assess impact on workflow. InV, ExV, and WMHP images were registered. Spatial alignment was evaluated using: slide offset (mm) between ExV slice locations and WMHP slides; overlap of the 3D prostate contour on InV versus ExV using Dice's coefficient (0 to 1); and 2D target registration error (TRE, mm) between corresponding landmarks on InV, ExV, and WMHP. Data are reported as mean ± standard deviation (SD).

STATISTICAL TESTING

Differences in 2D TRE before versus after registration were compared using the Wilcoxon signed-rank test (P < 0.05 considered significant).

RESULTS

ExV (duration 115 ± 15 min) was successfully incorporated into the workflow for all cases. Absolute slide offset was 1.58 ± 1.57 mm. Dice's coefficient was 0.865 ± 0.035. 2D TRE was significantly reduced after registration (P < 0.01) with mean (±SD of per patient means) of 1.9 ± 0.6 mm for InV versus ExV, 1.4 ± 0.5 mm for WMHP versus ExV, and 2.0 ± 0.5 mm for WMHP versus InV.

DATA CONCLUSION

The proposed system combines patient-specific 3D-printed molds with ExV to achieve spatial alignment between InV, ExV, and WMHP with mean 2D TRE of 1-2 mm.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:270-279.

Long-term outcomes of partial prostate treatment with magnetic resonance imaging-guided brachytherapy for patients with favorable-risk prostate cancer

BACKGROUND

Partial prostate treatment has emerged as a potential method for treating patients with favorable-risk prostate cancer while minimizing toxicity. The authors previously demonstrated poor rates of biochemical disease control for patients with National Comprehensive Cancer Network (NCCN) intermediate-risk disease using partial gland treatment with brachytherapy. The objective of the current study was to estimate the rates of distant metastasis and prostate cancer-specific mortality (PCSM) for this cohort.

METHODS

Between 1997 and 2007, a total of 354 men with clinical T1c disease, a prostate-specific antigen (PSA) level < 15 ng/mL, and Gleason grade ≤3 + 4 prostate cancer underwent partial prostate treatment with brachytherapy to the peripheral zone under 0.5-Tesla magnetic resonance guidance. The cumulative incidences of metastasis and PCSM for the NCCN very low-risk, low-risk, and intermediate-risk groups were estimated. Fine and Gray competing risk regression was used to evaluate clinical factors associated with time to metastasis.

RESULTS

A total of 22 patients developed metastases at a median of 11.0 years (interquartile range, 6.9-13.9 years). The 12-year metastasis rates for patients with very low-risk, low-risk, and intermediate-risk disease were 0.8% (95% confidence interval [95% CI], 0.1%-4.4%), 8.7% (95% CI, 3.4%-17.2%), and 15.7% (95% CI, 5.7%-30.2%), respectively, and the 12-year PCSM estimates were 1.6% (95% CI, 0.1%-7.6%), 1.4% (95% CI, 0.1%-6.8%), and 8.2% (95% CI, 1.9%-20.7%), respectively. On multivariate analysis, NCCN risk category (low risk: hazard ratio, 6.34 [95% CI, 1.18-34.06; P = .03] and intermediate risk: hazard ratio, 6.98 [95% CI, 1.23-39.73; P = .03]) was found to be significantly associated with the time to metastasis.

CONCLUSIONS

Partial prostate treatment with brachytherapy may be associated with higher rates of distant metastasis and PCSM for patients with intermediate-risk disease after long-term follow-up. Treatment of less than the full gland may not be appropriate for this cohort.

Clinical trials involving positron emission tomography and prostate cancer: an analysis of the ClinicalTrials.gov database

Background

The goal of this study is to evaluate the status and future perspectives of clinical trials on positron emission tomography in prostate cancer for diagnostic or therapeutic as well as for surveillance purposes.

Methods

The www.ClinicalTrials.gov database was searched on the 20th of January 2017 for all trials containing terms describing “prostate cancer” (prostate, prostatic, malignant, malignancy, cancer, tumor) and “positron emission tomography”. In total 167 trials were identified. Trials that included diseases other than PCa were excluded (n = 27; 16%). Furthermore, we excluded trials (n = 4, 2%) withdrawn prior to first patient enrollment. The remaining trials (n = 137, 82%) were selected for further manual classification analysis.

Results

One hundred thirty-seven trials were detected and analyzed. Majority of trials were in “active” recruitment status (n = 46, 34%) followed by trials that had been “completed” - (n = 34, 25%) and trials with “closed recruitment but active follow-up” (n = 23, 17%). Phase 1 and 2 comprised 46% of the complete trial portfolio. Locally confined disease was of major interest (n = 46, 34%), followed by metastatic disease – not otherwise specified (n = 43, 13%). Evaluation of PET was the primary goal of the trial in 114 (83%) cases. Most of the trials evaluated only one agent (n = 122, 89%). Choline and PSMA represented two major groups (total 50%) and they were equally distributed across trial portfolio with 25% (n = 34) each. PSMA trials showed the highest average annual growth rate of 56%. The trials were conducted in 17 countries.

Conclusion

The scientific community is showing a strong and ever-growing interest in the field and we expect that in the coming years, more phase III trials will be initiated ultimately delivering the required Level 1 evidence.

Electronic supplementary material

The online version of this article (10.1186/s13014-018-1057-3) contains supplementary material, which is available to authorized users.

Diagnostic Performance of Monoexponential DWI Versus Diffusion Kurtosis Imaging in Prostate Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

We aimed to compare the diagnostic performance of monoexponential DWI and diffusion kurtosis imaging (DKI) for the detection of prostate cancer (PCa).

MATERIALS AND METHODS

A systematic literature search was conducted for studies evaluating the diagnostic value of monoexponential DWI and DKI for PCa in the same patient cohorts with sufficient data to construct 2 × 2 contingency tables. Qualities of the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Data were extracted to calculate pooled sensitivities and specificities. We constructed summary ROC curves and calculated AUCs to determine the performances of DKI parameters (diffusion coefficient and kurtosis characterizing the deviation from the monoexponential decay) and apparent diffusion coefficient (ADC) values in diagnosing PCa.

RESULTS

Five studies (463 patients) were included, with eight, nine, and 10 subsets of data available for analysis of ADC, diffusion, and kurtosis, respectively. Pooled sensitivities were 89% (95% CI, 80-94%) for ADC, 91% (95% CI, 84-95%) for diffusion, and 87% (95% CI, 83-91%) for kurtosis. Pooled specificities were 86% (95% CI, 80-90%) for ADC, 78% (95% CI, 71-84%) for diffusion, and 85% (95% CI, 81-89%) for kurtosis. According to the summary ROC analyses, the AUC was 0.93 (95% CI, 0.90-0.95) for ADC, 0.89 (95% CI, 0.86-0.92) for diffusion, and 0.93 (95% CI, 0.90-0.95) for kurtosis. There was no notable publication bias, but significant heterogeneity was observed.

CONCLUSION

Monoexponential DWI and DKI showed comparable diagnostic accuracies for PCa. DKI is a potentially helpful method for the diagnosis of PCa. Therefore, on the basis of current evidence, we do not recommend including DKI in routine clinical assessment of PCa for the moment.

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

BACKGROUND

Radiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa.

PURPOSE

To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 120 PCa patients from two institutions, I₁ and I₂ , partitioned into training set D₁ (N = 70) from I₁ and independent validation set D₂ (N = 50) from I₂ . All patients were followed for ≥3 years.

SEQUENCE

3T, T₂ -weighted (T₂ WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences.

ASSESSMENT

PCa regions of interest (ROIs) on T₂ WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T₂ WI and ADC maps) were extracted from the ROIs. A machine-learning classifier (C_BCR ) was trained with the best discriminating set of radiomic features to predict BCR (p_BCR ).

STATISTICAL TESTS

Wilcoxon rank-sum tests with P < 0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using p_BCR was assessed using the Kaplan-Meier method and compared with Gleason Score (GS), PSA, and PIRADS-v2.

RESULTS

Distribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T₂ WI and ADC were associated with BCR (P < 0.05) on D₁ . C_BCR predictions resulted in a mean AUC = 0.84 on D₁ and AUC = 0.73 on D₂ . A significant difference in BCR-free survival between the predicted classes (BCR + and BCR-) was observed (P = 0.02) on D₂ compared to those obtained from GS (P = 0.8), PSA (P = 0.93) and PIRADS-v2 (P = 0.23).

DATA CONCLUSION

Radiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.

Comparison of quantitative apparent diffusion coefficient parameters with prostate imaging reporting and data system V2 assessment for detection of clinically significant peripheral zone prostate cancer

PURPOSE

To compare diagnostic performance of PI-RADSv2 with ADC parameters to identify clinically significant prostate cancer (csPC) and to determine the impact of csPC definitions on diagnostic performance of ADC and PI-RADSv2.

METHODS

We retrospectively identified treatment-naïve pathology-proven peripheral zone PC patients who underwent 3T prostate MRI, using high b-value diffusion-weighted imaging from 2011 to 2015. Using 3D slicer, areas of suspected tumor (T) and normal tissue (N) on ADC (b = 0, 1400) were outlined volumetrically. Mean ADC_T, mean ADC_N, ADC_ratio (ADC_T/ADC_N) were calculated. PI-RADSv2 was assigned. Three csPC definitions were used: (A) Gleason score (GS) ≥ 4 + 3; (B) GS ≥ 3 + 4; (C) MRI-based tumor volume >0.5 cc. Performances of ADC parameters and PI-RADSv2 in identifying csPC were measured using nonparametric comparison of receiver operating characteristic curves using the area under the curve (AUC).

RESULTS

Eighty five cases met eligibility requirements. Diagnostic performances (AUC) in identifying csPC using three definitions were: (A) ADC_T (0.83) was higher than PI-RADSv2 (0.65, p = 0.006); (B) ADC_T (0.86) was higher than ADC_ratio (0.68, p < 0.001), and PI-RADSv2 (0.70, p = 0.04); (C) PI-RADSv2 (0.73) performed better than ADC_ratio (0.56, p = 0.02). ADC_T performance was higher when csPC was defined by A or B versus C (p = 0.038 and p = 0.01, respectively). ADC_ratio performed better when csPC was defined by A versus C (p = 0.01). PI-RADSv2 performance was not affected by csPC definition.

CONCLUSIONS

When csPC was defined by GS, ADC parameters provided better csPC discrimination than PI-RADSv2, with ADC_T providing best result. When csPC was defined by MRI-calculated volume, PI-RADSv2 provided better discrimination than ADC_ratio. csPC definition did not affect PI-RADSv2 diagnostic performance.

Computer-aided diagnosis of prostate cancer using multi-parametric MRI: comparison between PUN and Tofts models

Computer-aided diagnosis (CAD) systems are increasingly being used in clinical settings to report multi-parametric magnetic resonance imaging (mp-MRI) of the prostate. Usually, CAD systems automatically highlight cancer-suspicious regions to the radiologist, reducing reader variability and interpretation errors. Nevertheless, implementing this software requires the selection of which mp-MRI parameters can best discriminate between malignant and non-malignant regions. To exploit functional information, some parameters are derived from dynamic contrast-enhanced (DCE) acquisitions. In particular, much CAD software employs pharmacokinetic features, such as K ^trans and k _ep, derived from the Tofts model, to estimate a likelihood map of malignancy. However, non-pharmacokinetic models can be also used to describe DCE-MRI curves, without any requirement for prior knowledge or measurement of the arterial input function, which could potentially lead to large errors in parameter estimation. In this work, we implemented an empirical function derived from the phenomenological universalities (PUN) class to fit DCE-MRI. The parameters of the PUN model are used in combination with T2-weighted and diffusion-weighted acquisitions to feed a support vector machine classifier to produce a voxel-wise malignancy likelihood map of the prostate. The results were all compared to those for a CAD system based on Tofts pharmacokinetic features to describe DCE-MRI curves, using different quality aspects of image segmentation, while also evaluating the number and size of false positive (FP) candidate regions. This study included 61 patients with 70 biopsy-proven prostate cancers (PCa). The metrics used to evaluate segmentation quality between the two CAD systems were not statistically different, although the PUN-based CAD reported a lower number of FP, with reduced size compared to the Tofts-based CAD. In conclusion, the CAD software based on PUN parameters is a feasible means with which to detect PCa, without affecting segmentation quality, and hence it could be successfully applied in clinical settings, improving the automated diagnosis process and reducing computational complexity.

Local staging with multiparametric MRI in daily clinical practice: diagnostic accuracy and evaluation of a radiologic learning curve

PURPOSE

To estimate the diagnostic accuracy of multiparametric MRI (mpMRI) for the detection of locally advanced prostate cancer (T-stage 3-4) prior to radical prostatectomy, in a multicenter cohort representing daily clinical practice. In addition, the radiologic learning curve for the detection of locally advanced disease is evaluated.

METHODS

Preoperative mpMRI findings of 430 patients (2012-2016) were compared to pathology results following radical prostatectomy. The diagnostic accuracy (sensitivity, specificity, PPV, and NPV) for the detection of locally advanced disease was calculated and compared for all years separately, to evaluate the presence of a radiological learning curve.

RESULTS

Of all 137 patients with locally advanced disease, 62 patients were preoperatively detected with mpMRI [sensitivity 45.3% (95% CI 36.9-53.6%), specificity 75.8% (CI 70.9-80.7%), PPV 46.6% (CI 38.1-55.1%), and NPV 74.7% (CI 69.8-79.7%)]. The diagnostic accuracy did not improve significantly over time (sensitivity p = 0.12; specificity p = 0.57).

CONCLUSIONS

In daily clinical practice, the diagnostic accuracy of mpMRI for the detection of locally advanced prostate cancer remains limited. It, therefore, seems questionable whether mpMRI is adequate to guide preoperative decision-making. No significant radiologic learning curve for the detection of locally advance disease was observed.

Registration Accuracy of Patient-Specific, Three-Dimensional-Printed Prostate Molds for Correlating Pathology With Magnetic Resonance Imaging

OBJECTIVE

This investigation was performed to evaluate the registration accuracy between magnetic resonance imaging (MRI) and pathology using three-dimensional (3-D) printed molds.

METHODS

Tissue-mimicking prostate phantoms were manufactured with embedded fiducials. The fiducials were used to measure and compare target registration error (TRE) between phantoms that were sliced by hand versus phantoms that were sliced within 3-D-printed molds. Subsequently, ten radical prostatectomy specimens were placed inside molds, scanned with MRI, and then sliced. The ex vivo scan was used to assess the true location of whole mount (WM) slides relative to in vivo MRI. The TRE between WM and in vivo MRI was measured using anatomic landmarks.

RESULTS

Manually sliced phantoms had a 4.1-mm mean TRE, whereas mold-sliced phantoms had a 1.9-mm mean TRE. Similarly, mold-assisted slicing reduced mean angular misalignment around the left-right (LR) anatomic axis from 10.7° to 4.5°. However, ex vivo MRI revealed that excised prostates were misaligned within molds, including a mean 14° rotation about the LR axis. The mean in-plane TRE was 3.3 mm using molds alone and 2.2 mm after registration was corrected with ex vivo MRI.

CONCLUSION

Patient-specific molds improved accuracy relative to manual slicing techniques in a phantom model. However, the registration accuracy of surgically resected specimens was limited by their imperfect fit within molds. This limitation can be overcome with the addition of ex vivo imaging.

SIGNIFICANCE

The accuracy of 3-D-printed molds was characterized, quantifying their utility for facilitating MRI-pathology registration.

Surface impact on nanoparticle-based magnetic resonance imaging contrast agents

Magnetic resonance imaging (MRI) is one of the most widely used diagnostic tools in the clinic. To improve imaging quality, MRI contrast agents, which can modulate local T1 and T2 relaxation times, are often injected prior to or during MRI scans. However, clinically used contrast agents, including Gd3+-based chelates and iron oxide nanoparticles (IONPs), afford mediocre contrast abilities. To address this issue, there has been extensive research on developing alternative MRI contrast agents with superior r1 and r2 relaxivities. These efforts are facilitated by the fast progress in nanotechnology, which allows for preparation of magnetic nanoparticles (NPs) with varied size, shape, crystallinity, and composition. Studies suggest that surface coatings can also largely affect T1 and T2 relaxations and can be tailored in favor of a high r1 or r2. However, the surface impact of NPs has been less emphasized. Herein, we review recent progress on developing NP-based T1 and T2 contrast agents, with a focus on the surface impact.

Multiparametric (mp) MRI of prostate cancer

Prostate cancer (PCa) is one of the most prevalent cancers in men. A large number of men are detected with PCa; however, the clinical behavior ranges from low-grade indolent tumors that never develop into a clinically significant disease to aggressive, invasive tumors that may rapidly progress to metastatic disease. The challenges in clinical management of PCa are at levels of screening, diagnosis, treatment, and follow-up after treatment. Magnetic resonance imaging (MRI) methods have shown a potential role in detection, localization, staging, assessment of aggressiveness, targeting biopsies, etc. in PCa patients. Multiparametric MRI (mpMRI) is emerging as a better option compared to the individual imaging methods used in the evaluation of PCa. There are attempts to improve the reproducibility and reliability of mpMRI by using an objective scoring system proposed in the prostate imaging reporting and data system (PIRADS) for standardized reporting. Prebiopsy mpMRI may be used to detect PCa in men with elevated prostate-specific antigen or abnormal digital rectal examination and to enable targeted biopsies. mpMRI can also be used to decide on clinical management of patients, for example active surveillance, and may help in detecting only the pathology that requires detection. It can potentially not only guide patient selection for initial and repeat biopsy but also reduce false-negative biopsies. This review presents a description of the MR methods most commonly applied for investigations of prostate. The anatomical, functional and metabolic parameters obtained from these MR methods are discussed with regard to their physical basis and their contribution to mpMRI investigations of PCa.

Quantitative parameters in dynamic contrast-enhanced magnetic resonance imaging for the detection and characterization of prostate cancer

Objectives

to assess the diagnostic accuracy of quantitative parameters of DCE-MRI in multi-parametric MRI (mpMRI) in comparison to the histopathology (including Gleason grade) of prostate cancer.

Patients and methods

150 men with suspected prostate cancer (abnormal digital rectum examination and or elevated prostate-specific antigen) received pre-biopsy 3T mpMRI and were recruited into peer-reviewed, protocol-based prospective study. The DCE-MRI quantitative parameters (K^trans (influx transfer constant) and k_ep (efflux rate constant)) of the cancerous and normal areas were recorded using four different kinetic models employing Olea Sphere (Olea Medical, La Ciotat, France). The correlation between these parameters and the histopathology of the lesions (biopsy and in a sub-cohort 41 radical prostatectomy specimen) was assessed.

Results

The quantitative parameters showed a significant difference between non-cancerous (benign) and cancerous lesions (Gleason score≥3+3) in the prostate gland. The cut-off values for prostate cancer differentiation were: K^trans (0.205 min⁻¹) and k_ep (0.665 min⁻¹) in the extended Tofts model (ET) and K^trans(0.205 min⁻¹ and k_ep (0.63 min⁻¹) in the Lawrence and Lee delay (LD) models respectively. The mean K^trans value also showed a difference between low-grade cancer (Gleason score=3+3) and high-grade cancer (Gleason score ≥ 3+4). With the addition of DCE-MRI quantitative parameters, the sensitivity of the PIRAD scoring system was increased from 56.6% to 92.1% (K^trans_ET), 93.1% (k_ep_ET), 91.0%, (K^trans_LD) and 89.4% (k_ep_LD).

Conclusion

Quantitative DCE-MRI parameters improved the diagnostic performance of conventional MRI in distinguishing normal and prostate cancers, including characterization of grade of cancers. The ET and LD models in post-image processing analysis provided better cut-off values for prostate cancer differentiation than the other quantitative DCE-MRI parameters.

Multiparametric Magnetic Resonance Imaging of the Prostate: Repeatability of Volume and Apparent Diffusion Coefficient Quantification

OBJECTIVES

The aim of this study was to evaluate the repeatability of a region of interest (ROI) volume and mean apparent diffusion coefficient (ADC) in standard-of-care 3 T multiparametric magnetic resonance imaging (mpMRI) of the prostate obtained with the use of endorectal coil.

MATERIALS AND METHODS

This prospective study was Health Insurance Portability and Accountability Act compliant, with institutional review board approval and written informed consent. Men with confirmed or suspected treatment-naive prostate cancer scheduled for mpMRI were offered a repeat mpMRI within 2 weeks. Regions of interest corresponding to the whole prostate gland, the entire peripheral zone (PZ), normal PZ, and suspected tumor ROI (tROI) on axial T2-weighted, dynamic contrast-enhanced subtract, and ADC images were annotated and assessed using Prostate Imaging Reporting and Data System (PI-RADS) v2. Repeatability of the ROI volume for each of the analyzed image types and mean ROI ADC was summarized with repeatability coefficient (RC) and RC%.

RESULTS

A total of 189 subjects were approached to participate in the study. Of 40 patients that gave initial agreement, 15 men underwent 2 mpMRI examinations and completed the study. Peripheral zone tROIs were identified in 11 subjects. Tumor ROI volume was less than 0.5 mL in 8 of 11 subjects. PI-RADS categories were identical between baseline-repeat studies in 11/15 subjects and differed by 1 point in 4/15. Peripheral zone tROI volume RC (RC%) was 233 mm (71%) on axial T2-weighted, 422 mm (112%) on ADC, and 488 mm (119%) on dynamic contrast-enhanced subtract. Apparent diffusion coefficient ROI mean RC (RC%) were 447 × 10 mm/s (42%) in PZ tROI and 471 × 10 mm/s (30%) in normal PZ. Significant difference in repeatability of the tROI volume across series was observed (P < 0.005). The mean ADC RC% was lower than volume RC% for tROI ADC (P < 0.05).

CONCLUSIONS

PI-RADS v2 overall assessment was highly repeatable. Multiparametric magnetic resonance imaging sequences differ in volume measurement repeatability. The mean tROI ADC is more repeatable compared with tROI volume in ADC. Repeatability of prostate ADC is comparable with that in other abdominal organs.

Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer

BACKGROUND

Gleason Score (GS) is a validated predictor of prostate cancer (PCa) disease progression and outcomes. GS from invasive needle biopsies suffers from significant inter-observer variability and possible sampling error, leading to underestimating disease severity ("underscoring") and can result in possible complications. A robust non-invasive image-based approach is, therefore, needed.

PURPOSE

Use spatially registered multi-parametric MRI (MP-MRI), signatures, and supervised target detection algorithms (STDA) to non-invasively GS PCa at the voxel level.

METHODS AND MATERIALS

This study retrospectively analyzed 26 MP-MRI from The Cancer Imaging Archive. The MP-MRI (T2, Diffusion Weighted, Dynamic Contrast Enhanced) were spatially registered to each other, combined into stacks, and stitched together to form hypercubes. Multi-parametric (or multi-spectral) signatures derived from a training set of registered MP-MRI were transformed using statistics-based Whitening-Dewhitening (WD). Transformed signatures were inserted into STDA (having conical decision surfaces) applied to registered MP-MRI determined the tumor GS. The MRI-derived GS was quantitatively compared to the pathologist's assessment of the histology of sectioned whole mount prostates from patients who underwent radical prostatectomy. In addition, a meta-analysis of 17 studies of needle biopsy determined GS with confusion matrices and was compared to the MRI-determined GS.

RESULTS

STDA and histology determined GS are highly correlated (R = 0.86, p < 0.02). STDA more accurately determined GS and reduced GS underscoring of PCa relative to needle biopsy as summarized by meta-analysis (p < 0.05).

CONCLUSION

This pilot study found registered MP-MRI, STDA, and WD transforms of signatures shows promise in non-invasively GS PCa and reducing underscoring with high spatial resolution.

Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies

Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas ('habitats') were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.

Validation of MRI to TRUS registration for high-dose-rate prostate brachytherapy

PURPOSE

The objective of this study was to develop and validate an open-source module for MRI to transrectal ultrasound (TRUS) registration to support tumor-targeted prostate brachytherapy.

METHODS AND MATERIALS

In this study, 15 patients with prostate cancer lesions visible on multiparametric MRI were selected for the validation. T2-weighted images with 1-mm isotropic voxel size and diffusion weighted images were acquired on a 1.5T Siemens imager. Three-dimensional (3D) TRUS images with 0.5-mm slice thickness were acquired. The investigated registration module was incorporated in the open-source 3D Slicer platform, which can compute rigid and deformable transformations. An extension of 3D Slicer, SlicerRT, allows import of and export to DICOM-RT formats. For validation, similarity indices, prostate volumes, and centroid positions were determined in addition to registration errors for common 3D points identified by an experienced radiation oncologist.

RESULTS

The average time to compute the registration was 35 ± 3 s. For the rigid and deformable registration, respectively, Dice similarity coefficients were 0.87 ± 0.05 and 0.93 ± 0.01 while the 95% Hausdorff distances were 4.2 ± 1.0 and 2.2 ± 0.3 mm. MRI volumes obtained after the rigid and deformable registration were not statistically different (p > 0.05) from reference TRUS volumes. For the rigid and deformable registration, respectively, 3D distance errors between reference and registered centroid positions were 2.1 ± 1.0 and 0.4 ± 0.1 mm while registration errors between common points were 3.5 ± 3.2 and 2.3 ± 1.1 mm. Deformable registration was found significantly better (p < 0.05) than rigid registration for all parameters.

CONCLUSIONS

An open-source MRI to TRUS registration platform was validated for integration in the brachytherapy workflow.