Diagnostic Accuracy of a MR Protocol Acquired with and without Endorectal Coil for Detection of Prostate Cancer: A Multicenter Study

Picture Perfect: The Status of Image Quality in Prostate MRI

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Prostate MRI: Is Endorectal Coil Necessary?—A Review

To assess the necessity of endorectal coil use in 3 Tesla (T) prostate magnetic resonance imaging (MRI), a literature review comparing the image quality and diagnostic performance with an endorectal coil (ERC) and a without endorectal coil (NERC), with a phased array coil or a wearable perineal coil (WPC), was performed. A PubMed search of 3T prostate MRI using an endorectal coil for studies published until 31 July 2021 was performed. A total of 14 studies comparing 3T prostate MRI with and without endorectal coil use were identified. The quality scores and diagnostic performances were recorded for each study. In total, five studies compared image quality; five studies compared quality and performance; and four studies compared performance of detection, size of detected lesions, accuracy of cancer localization, and aggressiveness/staging. The use of an endorectal coil improved image quality with a higher overall signal to noise ratio, posterior and peripheral zone signal to noise ratio, high b-value attenuation diffusion coefficient (ADC) signal to noise ratio, and contrast to noise ratio. Endorectal coil use improved subjective image quality for anatomic detail on T2 weighted images (T2WI) and diffusion weighted images (DWI). Endorectal coil use had less motion artifact on DWI than non-endorectal coil use, but produced a higher occurrence of other artifacts on DWI. Endorectal coils had higher sensitivity, specificity, and positive predictive value (PPV) in the detection of overall and index lesions, as well as smaller and less aggressive lesions, missing fewer and smaller lesions than non-endorectal coils. Endorectal coils had higher sensitivity than non-endorectal coils in localizing and staging lesions. Endorectal coils improved quantitative and qualitative image quality and diagnostic performance in the detection of smaller and less aggressive cancers in 3T prostate MRI.

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.

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Simple Summary

Prostate Cancer is one of the main threats to men’s health. Its accurate diagnosis is crucial to properly treat patients depending on the cancer’s level of aggressiveness. Tumor risk-stratification is still a challenging task due to the difficulties met during the reading of multi-parametric Magnetic Resonance Images. Artificial Intelligence models may help radiologists in staging the aggressiveness of the equivocal lesions, reducing inter-observer variability and evaluation time. However, these algorithms need many high-quality images to work efficiently, bringing up overfitting and lack of standardization and reproducibility as emerging issues to be addressed. This study attempts to illustrate the state of the art of current research of Artificial Intelligence methods to stratify prostate cancer for its clinical significance suggesting how widespread use of public databases could be a possible solution to these issues.

Abstract

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging

Introduction

Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, “DLR” in improving image quality and mitigating artifacts, which is now commercially available as AIR^TM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts.

Methods

This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERC_DLR, ERC_Conv, Non-ERC_DLR, and Non-ERC_Conv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor.

Results

The Non-ERC_DLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor.

Conclusion

Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.

Radiomics and Prostate MRI: Current Role and Future Applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.

Multicenter transperineal MRI-TRUS fusion guided outpatient clinic prostate biopsies under local anesthesia

INTRODUCTION

Transperineal Prostate biopsies (TPBx) are usually performed under general anesthesia without image fusion. This study aimed to evaluate prostate cancer (Pca) detection rates (CDR), pain, and adverse events using a novel, free-hand TPBx technique, based on elastic fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) under local anesthesia.

MATERIALS AND METHODS

This multicenter retrospective study included all consecutive patients scheduled for a TPBx. All had clinical suspicion of Pca, active surveillance scheduled for a re-biopsy, or suspicion of local recurrence after previous treatment. Bi-parametric or multiparametric MRI was performed in all patients and classified as positive in the case of Prostate Imaging-Reporting and Data System (PIRADS) suspicion ≥3. At least 1 targeted TPBx was realized from each PIRADS ≥3 index lesion. Six to 12 systematic random TPBx were done in patients with negative MRI. All biopsies were performed under local anesthesia in an outpatient clinic with MRI-TRUS fusion and the 3D navigation system Trinity Perine (Koelis, France). Any- and clinically significant Pca (csPca) (ISUP gr. ≥2) was recorded. Biopsy-related pain and adverse events were reported according to a visual analogue score of 0-10.

RESULTS

In total, 377 patients were included for analyses. The mean age was 67 years (95% Confidence Interval: 66-68) and the median prostate-specific antigen was 7.2 ng/ml (interquartile range [IQR] 4.8-11.0). MRI was negative in 6% and positive in 94%. The median MRI prostate volume was 43 ml (IQR 31-60) and the median MRI index tumor volume was 0.9 ml (IQR 0.5-2.1). The median number of TPBx was 4 (IQR 3-4). The overall detection of any- and csPca was 64% and 52%, respectively. The overall CDR according to PIRADS 3, 4, and 5 was 30%, 70%, and 94%, respectively. In patients with negative MRI, any- and csPca was detected in 23% and 9%, respectively. The median visual analogue score score was 2 (IQR 1-3, range 0-7). Two patients (0.5%) developed postbiopsy infection, of which one developed urosepsis. Treatment requiring haematuria or urinary retention did not occur.

CONCLUSION

Free-hand MRI/TRUS fusion-guided and systematic random TPBx in LA is a feasible, safe, and well-tolerated technique for diagnosing Pca.

Quality Comparison of 3 Tesla multiparametric MRI of the prostate using a flexible surface receiver coil versus conventional surface coil plus endorectal coil setup

PURPOSE

To subjectively and quantitatively compare the quality of 3 Tesla magnetic resonance imaging of the prostate acquired with a novel flexible surface coil (FSC) and with a conventional endorectal coil (ERC).

METHODS

Six radiologists independently reviewed 200 pairs of axial, high-resolution T2-weighted and diffusion-weighted image data sets, each containing one examination acquired with the FSC and one with the ERC, respectively. Readers selected their preferred examination from each pair and assessed every single examination using six quality criteria on 4-point scales. Signal-to-noise ratios were measured and compared.

RESULTS

Two readers preferred FSC acquisition (36.5-45%) over ERC acquisition (13.5-15%) for both sequences combined, and four readers preferred ERC acquisition (41-46%). Analysis of pooled responses for both sequences from all readers shows no significant preference for FSC or ERC. Analysis of the individual sequences revealed a pooled preference for the FSC in T2WI (38.7% vs 17.8%) and for the ERC in DWI (50.9% vs 19.6%). Patients' weight was the only weak predictor of a preference for the ERC acquisition (p = 0.04). SNR and CNR were significantly higher in the ERC acquisitions (p<0.001) except CNR differentiating tumor lesions from benign prostate (p=0.1).

CONCLUSION

Although readers have strong individual preferences, comparable subjective image quality can be obtained for prostate MRI with an ERC and the novel FSC. ERC imaging might be particularly valuable for sequences with inherently lower SNR as DWI and larger patients whereas the FSC is generally preferred in T2WI. FSC imaging generates a lower SNR than with an ERC.

Doctor, a patient is on the phone asking about the endorectal coil!

The question referred to in the title of this article is a relatively common situation when performing prostate MRI in some healthcare settings. Moreover, the answer is not always straightforward. The decisions on type of receiver coil for prostate MRI and whether or not an endorectal coil (ERC) should be used is based on several factors. These relate to the patient (e.g., body habitus, presence of metallic devices in the pelvis), the focus of the exam (diagnosis, staging, recurrence), and characteristics of the MRI system (e.g., magnetic field strength and hardware components including coil design and number of elements/channels available in the surface coil). Historically, the combined use of an ERC and a surface coil was the optimal combination for maximizing the signal-to-noise ratio (SNR), particularly for low-strength magnetic fields (1.5T). However, there are several disadvantages associated with the use of an ERC, and several studies have advocated equivalent clinical performance of modern MRI systems for diagnosis and staging of prostate cancer (PCa), either with ERC or surface alone. Accordingly, there is a wide variation in the precise imaging technique across institutions. This article focuses on the most relevant aspects of the decision of whether to use an ERC for PCa MR imaging.

Advances in the diagnostic options for prostate cancer

Over the past decade, despite the controversies surrounding prostate cancer screening, significant refinements have improved its application. PSA screening, although it has been questioned, appears to confer a mortality benefit and remains the most effective way to identify the possible presence of prostate cancer. Methods to improve the specificity of PSA screening and limit overdiagnosis of indolent cancers, including risk-stratified screening regimens, are currently being utilized. Certain imaging modalities, such as multiparametric MRI, have proven to be excellent adjuncts providing improved risk stratification and the ability for targeted biopsies; however, concerns over variability in interpretation and generalizability persist. A number of novel biomarkers have become available with nearly all demonstrating the ability to improve upon the specificity of PSA screening; however, optimal timing, direct comparisons, and usefulness in conjunction with imaging modalities remain to be elucidated. With the improvement in testing options and recognition of the risk/benefit ratio for men undergoing screening for prostate cancer, the increasing role of shared decision making in the process is emphasized.

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

Automatic segmentation of prostatic zones on multiparametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.

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.