The expanding role of MRI in prostate cancer. AJR Am J Roentgenol. 2013;201:1229-1238. [PMID: 24261361 DOI: 10.2214/ajr.12.10178]

Evaluation of Machine Learning Classification Models for False-Positive Reduction in Prostate Cancer Detection Using MRI Data

In this work, several machine learning (ML) algorithms, both classical ML and modern deep learning, were investigated for their ability to improve the performance of a pipeline for the segmentation and classification of prostate lesions using MRI data. The algorithms were used to perform a binary classification of benign and malignant tissue visible in MRI sequences. The model choices include support vector machines (SVMs), random decision forests (RDFs), and multi-layer perceptrons (MLPs), along with radiomic features that are reduced by applying PCA or mRMR feature selection. Modern CNN-based architectures, such as ConvNeXt, ConvNet, and ResNet, were also evaluated in various setups, including transfer learning. To optimize the performance, different approaches were compared and applied to whole images, as well as gland, peripheral zone (PZ), and lesion segmentations. The contribution of this study is an investigation of several ML approaches regarding their performance in prostate cancer (PCa) diagnosis algorithms. This work delivers insights into the applicability of different approaches for this context based on an exhaustive examination. The outcome is a recommendation or preference for which machine learning model or family of models is best suited to optimize an existing pipeline when the model is applied as an upstream filter.

Prostate Cancer Local Staging with Magnetic Resonance Imaging

Accurate determination of the local stage of prostate cancer is crucial for treatment planning and prognosis. The primary objective of local staging is to distinguish between organ-confined and locally advanced disease, with the latter carrying a worse clinical prognosis. The presence of locally advanced disease features of prostate cancer, such as extra-prostatic extension, seminal vesicle invasion, and positive surgical margin, can impact the choice of treatment. Over the past decade, multiparametric MRI (mpMRI) has become the preferred imaging modality for the local staging of prostate cancer and has been shown to provide accurate information on the location and extent of disease. It has demonstrated superior performance compared to staging based on traditional clinical nomograms. Despite being a relatively new technique, mpMRI has garnered considerable attention and ongoing investigations. Therefore, in this review, we will discuss the current use of mpMRI on prostate cancer local staging.

Impact of forced diuresis at two different time points on pelvic imaging in prostatic carcinoma with 68 Ga-PSMA PET/CT

OBJECTIVE

We compared diagnostic quality of 68 Ga-PSMA PET/CT imaging focused on the pelvic structures using two furosemide protocols in two different groups of patients.

MATERIAL AND METHODS

A total of 55 patients with prostate cancer were retrospectively enrolled in the study. Out of 55, 31 patients were in group 1 (median age: 66 years, Range 44-78 years) in which furosemide injection was given after completion of whole-body 68 Ga-PSMA PET/CT scan and 24 patients were in group 2 (median age: 63.5 years, range: 50-82 years) in which it was given along with the 68 Ga-PSMA injection. In both groups, an initial time point scan (T0 scan) and a delayed time point scan (T1scan) were done. The images were analyzed qualitatively as well as quantitatively.

RESULTS

Quantitatively there was no statistically significant difference between the SUVmax and T:B of prostatic lesion and seminal vesicle invasion (SVI) in both the groups at two time points ( P  > 0.05). Early furosemide injection caused a washout of the urinary bladder radiotracer concentration in significantly higher number of patients in group 2 (62.5% vs. 6.45% patients, P  < 0.001). There was significant clearance of radiotracer activity from the ureters in group 2 (SUVmax: 9.28 vs. 3.09, P  = 0.002).

CONCLUSION

The simultaneous furosemide and 68 Ga-PSMA injection can reduce the urinary excretion of the tracer and improve the diagnostic confidence of prostatic lesion, SVI and lymph nodal metastasis, along with reducing the scanning time and radiation burden, making this protocol an effective alternative to the present protocol of delayed furosemide injection.

Comparison of Diagnostic Value between 99mTechnetium-Methylene Diphosphate Bone Scan and 99mTechnetium-Prostate-specific Membrane Antigen Scan in Patients with Prostate Cancer with Osseous Metastases

Background

Prostate cancer (PCa) ranks as the second most prevalent cancer among men globally. The utilization of efficient and cost-effective diagnostic and therapeutic approaches holds paramount importance in the diagnosis and treatment of these patients, significantly impacting treatment outcomes. This study focuses on the investigation and comparison of two commonly employed scans within the treatment process for these patients.

Methods

In this prospective study, which spanned over 2 years, 40 patients diagnosed with PCa underwent examination using two scans: 99m Technetium-Prostate-specific Membrane Antigen (99mTC-PSMA) Scan and between Technetium-Methylene Diphosphate (99mTC-MDP) Bone Scan. The findings of these scans were then compared with each other, as well as with the results obtained from magnetic resonance imaging and the prostate-specific antigen level. The analysis of the results was conducted utilizing SPSS 22 software, and descriptive statistical methods were employed to present the findings.

Results

In this prospective study, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the 99mTC-MDP Bone Scan were found to be 88.2%, 83.3%, 96.7%, 55.5%, and 87.5%, respectively. Similarly, for the 99mTC-PSMA Scan, the corresponding values were 94.1%, 83.3%, 96.4%, 83.3%, and 92.5%, respectively.

Conclusions

Based on the findings of this study, it can be concluded that the diagnostic accuracy of the 99mTC-PSMA Scan is marginally higher compared to the 99mTC-MDP Bone Scan. Therefore, for patients who are limited to only one scan, the 99mTC-PSMA Scan appears to be the preferable choice.

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels

BACKGROUND

Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk.

METHODS

Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy.

RESULTS

Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy.

CONCLUSIONS

There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.

Toward mechatronic MRI-guided focal laser ablation of the prostate: Robust registration for improved needle delivery

BACKGROUND

Multiparametric MRI (mpMRI) is an effective tool for detecting and staging prostate cancer (PCa), guiding interventional therapy, and monitoring PCa treatment outcomes. MRI-guided focal laser ablation (FLA) therapy is an alternative, minimally invasive treatment method to conventional therapies, which has been demonstrated to control low-grade, localized PCa while preserving patient quality of life. The therapeutic success of FLA depends on the accurate placement of needles for adequate delivery of ablative energy to the target lesion. We previously developed an MR-compatible mechatronic system for prostate FLA needle guidance and validated its performance in open-air and clinical 3T in-bore experiments using virtual targets.

PURPOSE

To develop a robust MRI-to-mechatronic system registration method and evaluate its in-bore MR-guided needle delivery accuracy in tissue-mimicking prostate phantoms.

METHODS

The improved registration multifiducial assembly houses thirty-six aqueous gadolinium-filled spheres distributed over a 7.3 × 7.3 × 5.2 cm volume. MRI-guided needle guidance accuracy was quantified in agar-based tissue-mimicking prostate phantoms on trajectories (N = 44) to virtual targets covering the mechatronic system's range of motion. 3T gradient-echo recalled (GRE) MRI images were acquired after needle insertions to each target, and the air-filled needle tracks were segmented. Needle guidance error was measured as the shortest Euclidean distance between the target point and the segmented needle trajectory, and angular error was measured as the angle between the targeted trajectory and the segmented needle trajectory. These measurements were made using both the previously designed four-sphere registration fiducial assembly on trajectories (N = 7) and compared with the improved multifiducial assembly using a Mann-Whitney U test.

RESULTS

The median needle guidance error of the system using the improved registration fiducial assembly at a depth of 10 cm was 1.02 mm with an interquartile range (IQR) of 0.42-2.94 mm. The upper limit of the one-sided 95% prediction interval of needle guidance error was 4.13 mm. The median (IQR) angular error was 0.0097 rad (0.0057-0.015 rad) with a one-sided 95% prediction interval upper limit of 0.022 rad. The median (IQR) positioning error using the previous four-sphere registration fiducial assembly was 1.87 mm (1.77-2.14 mm). This was found to be significantly different (p = 0.0012) from the median (IQR) positioning error of 0.28 mm (0.14-0.95 mm) using the new registration fiducial assembly on the same trajectories. No significant difference was detected between the medians of the angular errors (p = 0.26).

CONCLUSION

This is the first study presenting an improved registration method and validation in tissue-mimicking phantoms of our remotely actuated MR-compatible mechatronic system for delivery of prostate FLA needles. Accounting for the effects of needle deflection, the system was demonstrated to be capable of needle delivery with an error of 4.13 mm or less in 95% of cases under ideal conditions, which is a statistically significant improvement over the previous method. The system will next be validated in a clinical setting.

Feasibility and preliminary clinical tolerability of low-field MRI-guided prostate biopsy

OBJECTIVE

We evaluate the clinical feasibility of a portable, low-field magnetic resonance imaging (MRI) system for prostate cancer (PCa) biopsy.

METHODS

A retrospective analysis of men who underwent a 12-core systematic transrectal ultrasound-guided prostate biopsy (SB) and a low-field MRI guided transperineal targeted biopsy (MRI-TB). Comparison of the detection of clinically significant PCa (csPCa) (Gleason Grade [GG] ≥ 2) by SB and low field MRI-TB, stratified by Prostate Imaging Reporting & Data System (PI-RADS) score, prostate volume, and prostate serum antigen (PSA) was performed.

RESULTS

A total of 39 men underwent both the MRI-TB and SB biopsy. Median (interquartile range [IQR]) age was 69.0 (61.5-73) years, body mass index (BMI) was 28.9 kg/m² (25.3-34.3), prostate volume was 46.5 cc (32-72.7), and PSA was 9.5 ng/ml (5.5-13.2). The majority (64.4%) of patients had PI-RADS ≥ 4 lesions and 25% of lesions were anterior on pre-biopsy MRII. Cancer detection rate (CDR) was greatest when combining SB and MRI-TB (64.1%). MRI-TB detected 74.3% (29/39) cancers. Of which, 53.8% (21/39) were csPCa while SB detected 42.5% (17/39) csPCa (p = 0.21). In 32.5% (13/39) of cases, MRI-TB upstaged the final diagnosis, compared to 15% (6/39) of cases in which SB upstaged the final diagnosis (p = 0.11).

CONCLUSION

Low-field MRI-TB is clinically feasible. Although future studies on the accuracy of MRI-TB system are needed, the initial CDR is comparable to those seen with fusion-based prostate biopsies. A transperineal and targeted approach may be beneficial in patients with higher BMI and anterior lesions.

Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis

The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular thermometry without altering the biochemical reactions and cellular membrane damage is challenging, requiring appropriately biocompatible, nontoxic, and efficient biosensors. Bright, photostable, and functionalized fluorescent nanodiamonds (FNDs) have emerged as excellent probes for intracellular thermometry and magnetometry with the spatial resolution on a nanometer scale. The temperature and magnetic field-dependent luminescence of naturally occurring defects in diamonds are key to high-sensitivity biosensing applications. Alterations in the surface chemistry of FNDs and conjugation with polymer, metallic, and magnetic nanoparticles have opened vast possibilities for drug delivery, diagnosis, nanomedicine, and magnetic hyperthermia. This study covers some recently reported research focusing on intracellular thermometry, magnetic sensing, and emerging applications of artificial intelligence (AI) in biomedical imaging. We extend the application of FNDs as biosensors toward disease diagnosis by using intracellular, stationary, and time-dependent information. Furthermore, the potential of machine learning (ML) and AI algorithms for developing biosensors can revolutionize any future outbreak.

Magnetic Resonance Imaging Guided Prostate Biopsy in Patients with ≥ One Negative Systematic Transrectal Ultrasound-Guided Biopsy: A Systemic Review and Meta-Analysis

The present study aimed to compare prostate cancer (PCa) and clinically significant PCa (csPCa) detection sensitivity between magnetic resonance imaging guided-biopsy (MRI-GB) and transrectal ultrasound-guided biopsy (TRUS-GB) in patients with ≥ 1 negative TRUS-GB, and to explore the additive value of TRUS-GB to MRI-GB. The meta-analysis of 18 studies demonstrated that MRI-GB had a similar sensitivity for PCa detection but a higher sensitivity for csPCa than TRUS-GB. In conclusion, there was limited value in combining TRUS-GB with MRI-GB compared with MRI-GB alone for csPCa detection in patients with one or more negative TRUS-GBs that were suspicious of having PCa.

Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal

Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can get around this issue. In order to diagnose prostate cancer via PA spectrum assessment, this work describes the machine learning (ML) technique implementation, such as supervised classification and unsupervised hierarchical clustering. The collected PA signals were preprocessed using Pwelch method, and the features are extracted using two methods such as hierarchical cluster and correlation assessment. The extracted features are classified using four ML-methods, namely, Support Vector Machine (SVM), Naïve Bayes (NB), decision tree C4.5, and Linear Discriminant Analysis (LDA). Furthermore, as these components alter throughout the progression of prostate cancer, this study focuses on the composition and distribution of collagen, lipids, and haemoglobin. In diseased tissues compared to normal tissues, there is a stronger correlation between the various chemical components ultrasonic power spectra, suggesting that the microstructural dispersion in tumour tissues has been more uniform. The accuracy of several classifiers used in cancer tissue diagnosis was greater than 94% for all four methods, which is effective than that of benchmark medical methods. Thus, the method shows significant promise for the noninvasive, early detection of severe prostate cancer.

Multimodal Contrastive Supervised Learning to Classify Clinical Significance MRI Regions on Prostate Cancer

Clinically significant regions (CSR), captured over multi-parametric MRI (mp-MRI) images, have emerged as a potential screening test for early prostate cancer detection and characterization. These sequences are able to quantify morphology, micro-circulation, and cellular density patterns that might be related to cancer disease. Nonetheless, this evaluation is mainly carried out by expert radiologists, introducing inter-reader variability in the diagnosis. Therefore, different deep learning models were proposed to support the diagnosis, but a proper representation of prostate lesions remains limited due to the non-alignment among sequences and the dependency of considerable amounts of labeled data for learning. The main limitation of such representation lies in the cross-entropy minimization that only exploits inter-class variation, being insufficient data augmentation and transfer learning strategies. This work introduces a Supervised Contrastive Learning (SCL) strategy that fully exploits the inter and intra-class variability of prostate lesions to robustly represent MRI regions. This strategy extracts lesion sample tuples, with positive and negative labels, regarding a query lesion. Such tuples are involved into an easy-positive, and semi-hard negative mining to project samples that better update the deep representation. The proposed learning strategy achieved an average ROC-AVC of 0.82, to characterize prostate cancer in MRI, using only the 60% of the available annotated data. Clinical relevance - A robust learning scheme that properly finds representations in limited data scenarios to classify clinically significant MRI regions on prostate cancer.

Proclarix, A New Biomarker for the Diagnosis of Clinically Significant Prostate Cancer: A Systematic Review

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) has improved the early detection of clinically significant prostate cancer (csPCa). However, an appropriate selection of men for mpMRI or prostate biopsy is still challenging, which is why new biomarkers or predictive models are recommended to determine those patients who will benefit from prostate biopsy. Proclarix is a new test that provides the risk of csPCa based on thrombospondin-1 (THBS1), cathepsin D (CTSD), prostate-specific antigen (PSA), and percentage of free PSA (%fPSA), as well as age. This systematic review analyzes the current clinical status of Proclarix and future development.

EVIDENCE ACQUISITION

A systematic review of the literature was carried out by two independent reviewers. The Medical Subject Heading (MeSH) terms 'prostate', 'thrombospondin-1', 'cathepsin-D' and 'Proclarix' were used. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison and Outcomes (PICO) selection criteria were followed. Finally, four articles analyzed the clinical usefulness of Proclarix.

EVIDENCE SYNTHESIS

Proclarix has been developed in men with PSA levels between 2 and 10 ng/mL, normal digital rectal examination (DRE), and prostate volume (PV)​ ≥ 35 cm³. Proclarix is associated with the PCa grade group and is more effective than %fPSA in detecting csPCa. Two studies analyzed the efficacy of Proclarix in men undergoing guided and systematic biopsies, obtaining similar results to PSA density.

CONCLUSION

Initial studies have shown the potential benefit of Proclarix in patients with specific characteristics. Future studies are needed to verify the clinical usefulness of Proclarix in men with suspected PCa before and after mpMRI.

Comparison of Prostate Imaging and Reporting Data System V2.0 and V2.1 for Evaluation of Transition Zone Lesions: A 5-Reader 202-Patient Analysis

OBJECTIVE

The aim of the study was to compare the distribution of Prostate Imaging and Reporting Data System (PI-RADS) scores, interreader agreement, and diagnostic performance of PI-RADS v2.0 and v2.1 for transition zone (TZ) lesions.

METHODS

The study included 202 lesions in 202 patients who underwent 3T prostate magnetic resonance imaging showing a TZ lesion that was later biopsied with magnetic resonance imaging/ultrasound fusion. Five abdominal imaging faculty reviewed T2-weighted imaging and high b value/apparent diffusion coefficient images in 2 sessions. Cases were randomized using a crossover design whereby half in the first session were reviewed using v2.0 and the other half using v2.1, and vice versa for the 2nd session. Readers provided T2-weighted imaging and DWI scores, from which PI-RADS scores were derived.

RESULTS

Interreader agreement for all PI-RADS scores had κ of 0.37 (v2.0) and 0.26 (v2.1). For 4 readers, the percentage of lesions retrospectively scored PI-RADS 1 increased greater than 5% and PI-RADS 2 score decreased greater than 5% from v2.0 to v2.1. For 2 readers, the percentage scored PI-RADS 3 decreased greater than 5% and, for 2 readers, increased greater than 5%. The percentage of PI-RADS 4 and 5 lesions changed less than 5% for all readers. For the 4 readers with increased frequency of PI-RADS 1 using v2.1, 4% to 16% were Gleason score ≥3 + 4 tumor. Frequency of Gleason score ≥3 + 4 in PI-RADS 3 lesions increased for 2 readers and decreased for 1 reader. Sensitivity of PI-RADS of 3 or greater for Gleason score ≥3 + 4 ranged 76% to 90% (v2.0) and 69% to 96% (v2.1). Specificity ranged 32% to 64% (v2.0) and 25% to 72% (v2.1). Positive predictive value ranged 43% to 55% (v2.0) and 41% to 58% (v2.1). Negative predictive value ranged 82% to 87% (v2.0) and 81% to 91% (v2.1).

CONCLUSIONS

Poor interreader agreement and lack of improvement in diagnostic performance indicate an ongoing need to refine evaluation of TZ lesions.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Comparison and prediction of artefact severity due to total hip replacement in 1.5 T versus 3 T MRI of the prostate

PURPOSE

To evaluate image quality and diagnostic value of multiparametric prostate MRI (mpMRI) in patients with total hip replacement (THR) at 1.5 and 3 Tesla.

METHODS

In this retrospective multicenter cohort study patients with uni- or bilateral THR and 1.5 T or 3 T mpMRI were included. Seventy consecutive, standard-of-care examinations per field strength were evaluated regarding their diagnostic value. The overall diagnostic value and prostate imaging quality score (PI-QUAL) were assessed. Artifact severity in the localizer and mpMRI sequences (T2w, DWI, DCE) was scored on a 3-point scale. Correlation between diagnostic value and artifacts was analysed. Moreover, a subgroup analysis focussed on image quality at different 3 T scanner generations.

RESULTS

140 consecutive patients (mean age 72, median PSA value 8.3 ng/ml) were included. When comparing 1.5 T to 3 T examinations, no significant differences were observed regarding the artifact severity of DWI and the localizer and the overall diagnostic value of the images. There was a strong correlation between the diagnostic value, PI-QUAL score, and artifact severity in the localizer and DWI. T2w and DCE sequences showed overall low artifacts. Significant improvement in image quality for 3 T at the latest scanner generation was observed, especially for DWI (p < 0.03).

CONCLUSIONS

MpMRI of patients with THR can be conducted at both field strengths without significant differences in artifacts. The localizer might be useful as an early forecasting feature for diagnostic value and particularly for contrast medium application decision. Patients with THR could benefit from technically advanced scanner generation and rs/ptx-EPI DWI sequences.

Prostate cancer identification via photoacoustic spectroscopy and machine learning

Photoacoustic spectroscopy can generate abundant chemical and physical information about biological tissues. However, this abundance of information makes it difficult to compare these tissues directly. Data mining methods can circumvent this problem. We describe the application of machine-learning methods (including unsupervised hierarchical clustering and supervised classification) to the diagnosis of prostate cancer by photoacoustic spectrum analysis. We focus on the content and distribution of hemoglobin, collagen, and lipids, because these molecules change during the development of prostate cancer. A higher correlation among the ultrasonic power spectra of these chemical components is observed in cancerous than in normal tissues, indicating that the microstructural distributions in cancerous tissues are more consistent. Different classifiers applied in cancer-tissue diagnoses achieved an accuracy of 82 % (better than that of standard clinical methods). The technique thus exhibits great potential for painless early diagnosis of aggressive prostate cancer.

Diagnostic Yield of Incremental Biopsy Cores and Second Lesion Sampling for In-Gantry MRI-Guided Prostate Biopsy

BACKGROUND. In-gantry MRI-guided biopsy (MRGB) of the prostate has been shown to be more accurate than other targeted prostate biopsy methods. However, the optimal number of cores to obtain during in-gantry MRGB remains undetermined. OBJECTIVE. The purpose of this study was to assess the diagnostic yield of obtaining an incremental number of cores from the primary lesion and of second lesion sampling during in-gantry MRGB of the prostate. METHODS. This retrospective study included 128 men with 163 prostate lesions who underwent in-gantry MRGB between 2016 and 2019. The men had a total of 163 lesions sampled with two or more cores, 121 lesions sampled with three or more cores, and 52 lesions sampled with four or more cores. A total of 40 men underwent sampling of a second lesion. Upgrade on a given core was defined as a greater International Society of Urological Pathology (ISUP) grade group (GG) relative to the previously obtained cores. Clinically significant prostate cancer (csPCa) was defined as ISUP GG 2 or greater. RESULTS. The frequency of any upgrade was 12.9% (21/163) on core 2 versus 10.7% (13/121) on core 3 (p = .29 relative to core 2) and 1.9% (1/52) on core 4 (p = .03 relative to core 3). The frequency of upgrade to csPCa was 7.4% (12/163) on core 2 versus 4.1% (5/121) on core 3 (p = .13 relative to core 2) and 0% (0/52) on core 4 (p = .07 relative to core 3). The frequency of upgrade on core 2 was higher for anterior lesions (p < .001) and lesions with a higher PI-RADS score (p = .007); the frequency of upgrade on core 3 was higher for apical lesions (p = .01) and lesions with a higher PI-RADS score (p = .01). Sampling of a second lesion resulted in an upgrade in a single patient (2.5%; 1/40); both lesions were PI-RADS category 4 and showed csPCa. CONCLUSION. When performing in-gantry MRGB of the prostate, obtaining three cores from the primary lesion is warranted to optimize csPCa diagnosis. Obtaining a fourth core from the primary lesion or sampling a second lesion has very low yield in upgrading cancer diagnoses. CLINICAL IMPACT. To reduce patient discomfort and procedure times, operators may refrain from obtaining more than three cores or second lesion sampling.

Feasibility of diffusion weighting with a local inside-out nonlinear gradient coil for prostate MRI

PURPOSE

Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI quality image is directly related to low gradient amplitudes, since weak gradients must be compensated with long echo times.

METHODS

We propose a new type of MRI accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients.

RESULTS

Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC.

CONCLUSIONS

Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

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.

Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective

Purpose

This study aimed to develop a model for predicting pathologic extracapsular extension (ECE) and seminal vesicle invasion (SVI) while integrating magnetic resonance imaging-based T-staging (cTMRI, cT1c-cT3b).

Materials and Methods

A total of 1,915 who underwent radical prostatectomy between 2006-2016 met the inclusion/exclusion criteria. We performed a multivariate logistic regression analysis as well as Bayesian network (BN) modeling based on possible confounding factors. The BN model was internally validated using 5-fold validation.

Results

According to the multivariate logistic regression analysis, initial prostate-specific antigen (iPSA) (β=0.050, p<0.001), percentage of positive biopsy cores (PPC) (β=0.033, p<0.001), both lobe involvement on biopsy (β=0.359, p=0.009), Gleason score (β=0.358, p<0.001), and cTMRI (β=0.259, p<0.001) were significant factors for ECE. For SVI, iPSA (β=0.037, p<0.001), PPC (β=0.024, p<0.001), GS (β=0.753, p<0.001), and cTMRI (β=0.507, p<0.001) showed statistical significance. BN models to predict ECE and SVI were also successfully established. The overall AUC/accuracy of the BN models were 0.76/73.0% and 0.88/89.6% for ECE and SVI, respectively. According to internal comparison between the BN model and Roach formula, BN model had improved AUC values for predicting ECE (0.76 vs. 0.74; p=0.060) and SVI (0.88 vs. 0.84, p<0.001).

Conclusion

wo models to predict pathologic ECE and SVI integrating cTMRI were established and installed on a separate website for public access to guide radiation oncologists.

The diagnostic accuracy of multiparametric MRI for detection and localization of prostate cancer depends on the affected region

Objectives

To determine the diagnostic accuracy of 3T multiparametric magnetic resonance imaging (mpMRI) for detecting and locating prostate cancer (PCa) on Dickinson's 27-sector map, using histopathology specimens from radical prostatectomy (RP) as the reference standard.

Patients and methods

The authors studied a continuous series of 140 patients who underwent RP over three consecutive years. Prior to RP, all patients had mpMRI for detection and localization of PCa and further assessment by biopsy. To minimize the potential of disease progression, 25 patients were excluded because the interval between mpMRI and RP exceeded 6 months, which left 115 patients eligible for analysis. The mpMRI findings were reported using the Prostate Imaging-Reporting and Data System (PI-RADS) v2, considering PI-RADS ≥ 3 to indicate PCa. The histopathology findings from RP specimens were graded using the Gleason scoring system, considering Gleason ≥ 6 to indicate PCa. The location of the tumors was mapped on Dickinson's 27-sector map for both mpMRI and histopathology and compared by rigid sector-by-sector matching.

Results

The cohort of 115 patients eligible for analysis was aged 66.5 ± 6.0 years at RP. Of the 3105 sectors analyzed, there were 412 true positives (13%), 28 false positives (1%), 68 false negatives (2%), and 2597 true negatives (84%). Across the 27 sectors of the prostate, mpMRI sensitivity ranged from 50% to 100% and specificity from 96% to 100%, while PPV ranged from 50% to 100%, and NPV from 91% to 100%. For the anterior prostate, mpMRI had a sensitivity of 80% (CI, 71%-86%), specificity of 99% (CI, 99%-100%), PPV of 91% (CI, 83%-95%), and NPV of 99% (CI, 98%-99%). For the posterior prostate, mpMRI had a sensitivity of 88% (CI, 84%-91%), specificity of 98% (CI, 97%-99%), PPV of 94% (CI, 92%-96%), and NPV of 96% (CI, 94%-97%). Overall, mpMRI had a sensitivity of 86%, specificity of 99%, PPV of 94%, and NPV of 97%.

Conclusions

The accuracy of mpMRI in detecting and locating prostate tumors depends on the affected region, but its high NPV across all sectors suggests that negative findings may not need corroboration by other techniques.

3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

PI-RADS: what is new and how to use it

The prostate imaging reporting and data system (PI-RADS) has revolutionized the use of magnetic resonance imaging (MRI) for the management of prostate cancer (PCa). The most recent version 2.1, PI-RADS v2.1, provides specific refinements in the performance, relaxing some recommendations which were not found to be helpful, while reinforcing and clarifying others. The interpretation of T2-weighted imaging (T2WI) in the transition zone (TZ), and the overall assessment of TZ nodules, now allows for a clearer distinction between those which are clearly benign and those which might warrant tissue sampling. Additional changes also resolve discrepancies in T2WI and diffusion-weighted imaging (DWI) of the peripheral zone (PZ). PI-RADS v2.1 is a simpler, more straightforward, and more reproducible method to better communicate between physicians regarding findings on prostate MRI.

Malignancy Detection in Prostate Multi-Parametric MR Images Using U-net with Attention

Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.

A review of the role of MRI in diagnosis and treatment of early stage lung cancer

Despite magnetic resonance imaging (MRI) being a mainstay in the oncologic care for many disease sites, it has not routinely been used in early lung cancer diagnosis, staging, and treatment. While MRI provides improved soft tissue contrast compared to computed tomography (CT), an advantage in multiple organs, the physical properties of the lungs and mediastinum create unique challenges for lung MRI. Although multi-detector CT remains the gold standard for lung imaging, advances in MRI technology have led to its increased clinical relevance in evaluating early stage lung cancer. Even though positron emission tomography is used more frequently in this context, functional MR imaging, including diffusion-weighted MRI and dynamic contrast-enhanced MRI, are emerging as useful modalities for both diagnosis and evaluation of treatment response for lung cancer. In parallel with these advances, the development of combined MRI and linear accelerator devices (MR-linacs), has spurred the integration of MRI into radiation treatment delivery in the form of MR-guided radiotherapy (MRgRT). Despite challenges for MRgRT in early stage lung cancer radiotherapy, early data utilizing MR-linacs shows potential for the treatment of early lung cancer. In both diagnosis and treatment, MRI is a promising modality for imaging early lung cancer.

Influence of the Location and Zone of Tumor in Prostate Cancer Detection and Localization on 3-T Multiparametric MRI Based on PI-RADS Version 2

OBJECTIVE. The objective of our study was to determine the performance of 3-T multiparametric MRI (mpMRI) for prostate cancer (PCa) detection and localization, stratified by anatomic zone and level, using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and whole-mount histopathology (WMHP) as reference. MATERIALS AND METHODS. Multiparametric MRI examinations of 415 consecutive men were compared with thin-section WMHP results. A genitourinary radiologist and pathologist collectively determined concordance. Two radiologists assigned PI-RADSv2 scores and sector location to all detected foci by consensus. Tumor detection rates were calculated for clinical and pathologic (tumor location and zone) variables. Both rigid and adjusted sector-matching models were used to account for fixation-related issues. RESULTS. Of 863 PCa foci in 16,185 prostate sectors, the detection of overall and index PCa lesions in the midgland, base, and apex was 54.9% and 83.1%, 42.1% and 64.0% (p = 0.04, p = 0.02), and 41.9% and 71.4% (p = 0.001, p = 0.006), respectively. Tumor localization sensitivity was highest in the midgland compared with the base and apex using an adjusted match compared with a rigid match (index lesions, 71.3% vs 43.7%; all lesions, 70.8% vs 36.0%) and was greater in the peripheral zone (PZ) than in the transition zone. Three-Tesla mpMRI had similarly high specificity (range, 93.8-98.3%) for overall and index tumor localization when using both rigid and adjusted sector-matching approaches. CONCLUSION. For 3-T mpMRI, the highest sensitivity (83.1%) for detection of index PCa lesions was in the midgland, with 98.3% specificity. Multiparametric MRI performance for sectoral localization of PCa within the prostate was moderate and was best for index lesions in the PZ using an adjusted model.

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

INTRODUCTION

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

METHOD

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

RESULTS

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

CONCLUSION

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

IMPLICATIONS FOR PRACTICE

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

Accuracy of multiparametric magnetic resonance imaging for diagnosing prostate Cancer: a systematic review and meta-analysis

BACKGROUND

The application of multiparametric magnetic resonance imaging (mpMRI) for diagnosis of prostate cancer has been recommended by the European Association of Urology (EAU), National Comprehensive Cancer Network (NCCN), and European Society of Urogenital Radiology (ESUR) guidelines. The purpose of this study is to systematically review the literature on assessing the accuracy of mpMRI in patients with suspicion of prostate cancer.

METHOD

We searched Embase, Pubmed and Cochrane online databases from January 12,000 to October 272,018 to extract articles exploring the possibilities that the pre-biopsy mpMRI can enhance the diagnosis accuracy of prostate cancer. The numbers of true- and false-negative results and true- and false-positive ones were extracted to calculate the corresponding sensitivity and specificity of mpMRI. Study quality was assessed using QUADAS-2 tool. Random effects meta-analysis and a hierarchical summary receiver operating characteristic (HSROC) plot were performed for further study.

RESULTS

After searching, we acquired 3741 articles for reference, of which 29 studies with 8503 participants were eligible for inclusion. MpMRI maintained impressive diagnostic value, the area under the HSROC curve was 0.87 (95%CI,0.84-0.90). The sensitivity and specificity for mpMRI were 0.87 [95%CI, 0.81-0.91] and 0.68 [95%CI,0.56-0.79] respectively. The positive likelihood ratio was 2.73 [95%CI 1.90-3.90]; negative likelihood ratio was 0.19 [95% CI 0.14,-0.27]. The risk of publication bias was negligible with P = 0.96.

CONCLUSION

Results of the meta-analysis suggest that mpMRI is a sensitive tool to diagnose prostate cancer. However, because of the high heterogeneity existing among the included studies, further studies are needed to apply the results of this meta-analysis in clinic.

Performance of Diffusion Kurtosis Imaging Versus Diffusion Tensor Imaging in Discriminating Between Benign Tissue, Low and High Gleason Grade Prostate Cancer

RATIONALE AND OBJECTIVES

To investigate the performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in discriminating benign tissue, low- and high-grade prostate adenocarcinoma (PCa).

MATERIALS AND METHODS

Forty-eight patients with biopsy-proven PCa of different Gleason grade (GG), who provided written informed consent, were enrolled. All subjects underwent 3T DWI examinations by using b values 0, 500, 1000, 1500, 2000, and 2500 s/mm² and six gradient directions. Mean diffusivity, fractional anisotropy (FA), apparent kurtosis (K), apparent kurtosis-derived diffusivity (D), and proxy fractional kurtosis anisotropy (KFA) maps were obtained. Regions of interest were selected in PCa, in the contralateral benign zone, and in the peritumoral area. Histogram analysis was performed by measuring mean, 10th, 25th, and 90th (p90) percentile of the whole-lesion volume. Kruskal-Wallis test with Bonferroni correction was used to assess significant differences between different regions of interest. The correlation between diffusion metrics and GG and between DKI and DTI parameters was evaluated with Pearson's test. ROC curve analysis was carried out to analyze the ability of histogram variables to differentiate low- and high-GG PCa.

RESULTS

All metrics significantly discriminated PCa from benign and from peritumoral tissue (except for K, KFA_p90, and FA). K_p90 showed the highest correlation with GG and the best diagnostic ability (area under the curve = 0.84) in discriminating low- from high-risk PCa.

CONCLUSION

Compared to DTI, DKI provides complementary and additional information about prostate cancer tissue, resulting more sensitive to PCa-derived modifications and more accurate in discriminating low- and high-risk PCa.

Detection and Localization of Prostate Cancer at 3-T Multiparametric MRI Using PI-RADS Segmentation

OBJECTIVE. The purpose of this study is to determine the overall and sector-based performance of 3-T multiparametric MRI for prostate cancer (PCa) detection and localization by using Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) scoring and segmentation compared with whole-mount histopathologic analysis. MATERIALS AND METHODS. Multiparametric 3-T MRI examinations of 415 consecutive men were compared with thin-section whole-mount histopathologic analysis. A genitourinary radiologist and pathologist collectively determined concordance. Two radiologists assigned PI-RADSv2 categories and sectoral location to all detected foci by consensus. Tumor detection rates were calculated for clinical and pathologic (Gleason score) variables. Both rigid and adjusted sector-matching models were used to account for fixation-related issues. RESULTS. The 415 patients had 863 PCa foci (52.7% had a Gleason score ≥ 7, 61.9% were ≥ 1 cm, and 90.4% (375/415) of index lesions were ≥ 1 cm) and 16,185 prostate sectors. Multiparametric MRI enabled greater detection of PCa lesions 1 cm or larger (all lesions vs index lesions, 61.6% vs 81.6%), lesions with Gleason score greater than or equal to 7 (all lesions vs index lesions, 71.4% vs 80.9%), and index lesions with both Gleason score greater than or equal to 7 and size 1 cm or larger (83.3%). Higher sensitivity was obtained for adjusted versus rigid tumor localization for all lesions (56.0% vs 28.5%), index lesions (55.4% vs 34.3%), lesions with Gleason score greater than or equal to 7 (55.7% vs 36.0%), and index lesions 1 cm or larger (56.1% vs 35.0%). Multiparametric 3-T MRI had similarly high specificity (96.0-97.9%) for overall and index tumor localization with adjusted and rigid sector-matching approaches. CONCLUSION. Using 3-T multiparametric MRI and PI-RADSv2, we achieved the highest sensitivity (83.3%) for the detection of lesions 1 cm or larger with Gleason score greater than or equal to 7. Sectoral localization of PCa within the prostate was moderate and was better with an adjusted model than with a rigid model.

Radiomics and radiogenomics of prostate cancer

Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.

Intra- and interreader reproducibility of PI-RADSv2: A multireader study

BACKGROUND

The Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) has been in use since 2015; while interreader reproducibility has been studied, there has been a paucity of studies investigating the intrareader reproducibility of PI-RADSv2.

PURPOSE

To evaluate both intra- and interreader reproducibility of PI-RADSv2 in the assessment of intraprostatic lesions using multiparametric magnetic resonance imaging (mpMRI).

STUDY TYPE

Retrospective.

POPULATION/SUBJECTS

In all, 102 consecutive biopsy-naïve patients who underwent prostate MRI and subsequent MR/transrectal ultrasonography (MR/TRUS)-guided biopsy.

FIELD STRENGTH/SEQUENCES

Prostate mpMRI at 3T using endorectal with phased array surface coils (TW MRI, DW MRI with ADC maps and b2000 DW MRI, DCE MRI).

ASSESSMENT

Previously detected and biopsied lesions were scored by four readers from four different institutions using PI-RADSv2. Readers scored lesions during two readout rounds with a 4-week washout period.

STATISTICAL TESTS

Kappa (κ) statistics and specific agreement (Po ) were calculated to quantify intra- and interreader reproducibility of PI-RADSv2 scoring. Lesion measurement agreement was calculated using the intraclass correlation coefficient (ICC).

RESULTS

Overall intrareader reproducibility was moderate to substantial (κ = 0.43-0.67, Po  = 0.60-0.77), while overall interreader reproducibility was poor to moderate (κ = 0.24, Po  = 46). Readers with more experience showed greater interreader reproducibility than readers with intermediate experience in the whole prostate (P = 0.026) and peripheral zone (P = 0.002). Sequence-specific interreader agreement for all readers was similar to the overall PI-RADSv2 score, with κ = 0.24, 0.24, and 0.23 and Po  = 0.47, 0.44, and 0.54 in T2 -weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE), respectively. Overall intrareader and interreader ICC for lesion measurement was 0.82 and 0.71, respectively.

DATA CONCLUSION

PI-RADSv2 provides moderate intrareader reproducibility, poor interreader reproducibility, and moderate interreader lesion measurement reproducibility. These findings suggest a need for more standardized reader training in prostate MRI.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2.

Comparison of PET/MRI with multiparametric MRI in diagnosis of primary prostate cancer: A meta-analysis

OBJECTIVE

This meta-analysis aimed to compare the diagnostic performance of positron emission tomography (PET)/MRI using various radiotracers with multiparametric (mp) MRI for detection of primary prostate cancer (PCa).

METHODS

A systematic literature search up to January 2019 was performed to identify studies that evaluated the diagnostic value of PET/MRI and mpMRI for detection of PCa in the same patient cohorts and had sufficient data to construct 2 × 2 contingency tables for true-positive (TP), false-positive (FP), false-negative (FN), and true-negative (TN) results. The quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool, and pooled sensitivity (SEN) and specificity (SPE) were calculated. Summary receiver operating characteristic (ROC) curves and area under the curves (AUCs) were used to compare the performances of PET/MRI and mpMRI.

RESULTS

We identified 9 eligible studies that included a total of 353 patients. PET/MRI had a SEN of 0.783 (95% CI, 0.758-0.807) and a SPE of 0.899 (95% CI, 0.879-0.917), and mpMRI had a SEN of 0.603 (95% CI, 0.574-0.631) and a SPE of 0.887 (95% CI, 0.866-0.906). PET/MRI had a higher AUC than mpMRI (0.9311, 95% CI, 0.8990-0.9632 vs. 0.8403, 95% CI, 0.7864-0.8942; P =  0.0036). There was no notable publication bias, but there was medium heterogeneity in outcomes. The meta-regression analysis showed the major potential cause of heterogeneity was the use of region-based rather than lesion-based analysis.

CONCLUSION

PET/MRI has very good diagnostic performance and outperforms mpMRI for the diagnosis of primary PCa.

Model-based reconstruction framework for correction of signal pile-up and geometric distortions in prostate diffusion MRI

PURPOSE

Prostate diffusion-weighted MRI scans can suffer from geometric distortions, signal pileup, and signal dropout attributed to differences in tissue susceptibility values at the interface between the prostate and rectal air. The aim of this work is to present and validate a novel model based reconstruction method that can correct for these distortions.

METHODS

In regions of severe signal pileup, standard techniques for distortion correction have difficulty recovering the underlying true signal. Furthermore, because of drifts and inaccuracies in the determination of center frequency, echo planar imaging (EPI) scans can be shifted in the phase-encoding direction. In this work, using a B0 field map and a set of EPI data acquired with blip-up and blip-down phase encoding gradients, we model the distortion correction problem linking the distortion-free image to the acquired raw corrupted k-space data and solve it in a manner analogous to the sensitivity encoding method. Both a quantitative and qualitative assessment of the proposed method is performed in vivo in 10 patients.

RESULTS

Without distortion correction, mean Dice similarity scores between a reference T2W and the uncorrected EPI images were 0.64 and 0.60 for b-values of 0 and 500 s/mm2 , respectively. Compared to the Topup (distortion correction method commonly used for neuro imaging), the proposed method achieved Dice scores (0.87 and 0.85 versus 0.82 and 0.80) and better qualitative results in patients where signal pileup was present because of high rectal gas residue.

CONCLUSION

Model-based reconstruction can be used for distortion correction in prostate diffusion MRI.

Validation of PI-RADS Version 2 in Transition Zone Lesions for the Detection of Prostate Cancer

Purpose To determine the association between Prostate Imaging Reporting and Data System (PI-RADS) version 2 scores and prostate cancer (PCa) in a cohort of patients undergoing biopsy of transition zone (TZ) lesions. Materials and Methods A total of 634 TZ lesions in 457 patients were identified from a prospectively maintained database of consecutive patients undergoing prostate magnetic resonance imaging. Prostate lesions were retrospectively categorized with the PI-RADS version 2 system by two readers in consensus who were blinded to histopathologic findings. The proportion of cancer detection for all PCa and for clinically important PCa (Gleason score ≥3+4) for each PI-RADS version 2 category was determined. The performance of PI-RADS version 2 in cancer detection was evaluated. Results For PI-RADS category 2 lesions, the overall proportion of cancers was 4% (one of 25), without any clinically important cancer. For PI-RADS category 3, 4, and 5 lesions, the overall proportion of cancers was 22.2% (78 of 352), 39.1% (43 of 110), and 87.8% (129 of 147), respectively, and the proportion of clinically important cancers was 11.1% (39 of 352), 29.1% (32 of 110), and 77.6% (114 of 147), respectively. Higher PI-RADS version 2 scores were associated with increasing likelihood of the presence of clinically important PCa (P < .001). Differences were found in the percentage of cancers in the PI-RADS category between PI-RADS 3 and those upgraded to PI-RADS 4 based on diffusion-weighted imaging for clinically important cancers (proportion for clinically important cancers for PI-RADS 3 and PI-RADS 3+1 were 11.1% [39 of 352] and 30.8% [28 of 91], respectively; P < .001). Conclusion Higher PI-RADS version 2 scores are associated with a higher proportion of clinically important cancers in the TZ. PI-RADS category 2 lesions rarely yield PCa, and their presence does not justify targeted biopsy.

3D MRI Modeling of Thin and Spatially Complex Soft Tissue Structures without Shrinkage: Lamprey Myosepta as an Example

3D imaging techniques enable the nondestructive analysis and modeling of complex structures. Among these, MRI exhibits good soft tissue contrast, but is currently less commonly used for nonclinical research than X-ray CT, even though the latter requires contrast-staining that shrinks and distorts soft tissues. When the objective is the creation of a realistic and complete 3D model of soft tissue structures, MRI data are more demanding to acquire and visualize and require extensive post-processing because they comprise noncubic voxels with dimensions that represent a trade-off between tissue contrast and image resolution. Therefore, thin soft tissue structures with complex spatial configurations are not always visible in a single MRI dataset, so that standard segmentation techniques are not sufficient for their complete visualization. By using the example of the thin and spatially complex connective tissue myosepta in lampreys, we developed a workflow protocol for the selection of the appropriate parameters for the acquisition of MRI data and for the visualization and 3D modeling of soft tissue structures. This protocol includes a novel recursive segmentation technique for supplementing missing data in one dataset with data from another dataset to produce realistic and complete 3D models. Such 3D models are needed for the modeling of dynamic processes, such as the biomechanics of fish locomotion. However, our methodology is applicable to the visualization of any thin soft tissue structures with complex spatial configurations, such as fasciae, aponeuroses, and small blood vessels and nerves, for clinical research and the further exploration of tensegrity. Anat Rec, 301:1745-1763, 2018. © 2018 Wiley Periodicals, Inc.

PI-RADS v2: Current standing and future outlook

The Prostate Imaging-Reporting and Data System (PI-RADS) was created in 2012 to establish standardization in prostate multiparametric magnetic resonance imaging (mpMRI) acquisition, interpretation, and reporting. In hopes of improving upon some of the PI-RADS v1 shortcomings, the PI-RADS Steering Committee released PI-RADS v2 in 2015. This paper reviews the accuracy, interobserver agreement, and clinical outcomes of PI-RADS v2 and comments on the limitations of the current literature. Overall, PI-RADS v2 shows improved sensitivity and similar specificity compared to PI-RADS v1. However, concerns exist regarding interobserver agreement and the heterogeneity of the study methodology.

Positron Emission Tomography Imaging of Prostate Cancer with Ga-68-Labeled Gastrin-Releasing Peptide Receptor Agonist BBN7-14 and Antagonist RM26

Radiolabeled bombesin (BBN) analogs have long been used for developing gastrin-releasing peptide receptor (GRPR) targeted imaging probes, and tracers with excellent in vivo performance including high tumor uptake, high contrast, and favorable pharmacokinetics are highly desired. In this study, we compared the ⁶⁸Ga-labeled GRPR agonist (Gln–Trp–Ala–Val–Gly–His–Leu–Met–NH₂, BBN_7–14) and antagonist (d-Phe–Gln–Trp–Ala–Val–Gly–His–Sta–Leu–NH₂, RM26) for the positron emission tomography (PET) imaging of prostate cancer. The in vitro stabilities, receptor binding, cell uptake, internalization, and efflux properties of the probes ⁶⁸Ga–1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA)–Aca–BBN_7–14 and ⁶⁸Ga–NOTA–poly(ethylene glycol)₃ (PEG₃)–RM26 were studied in PC-3 cells, and the in vivo GRPR targeting abilities and kinetics were investigated using PC-3 tumor xenografted mice. BBN_7–14, PEG₃-RM26, NOTA–Aca–BBN_7–14, and NOTA–PEG₃–RM26 showed similar binding affinity to GRPR. In PC-3 tumor-bearing mice, the tumor uptake of ⁶⁸Ga–NOTA–PEG₃–RM26 remained at around 3.00 percentage of injected dose per gram of tissue within 1 h after injection, in contrast with ⁶⁸Ga–NOTA–Aca–BBN_7–14, which demonstrated rapid elimination and high background signal. Additionally, the majority of the ⁶⁸Ga–NOTA–PEG₃–RM26 remained intact in mouse serum at 5 min after injection, while almost all of the ⁶⁸Ga–NOTA–Aca–BBN_7–14 was degraded under the same conditions, demonstrating more-favorable in vivo pharmacokinetic properties and metabolic stabilities of the antagonist probe relative to its agonist counterpart. Overall, the antagonistic GRPR targeted probe ⁶⁸Ga–NOTA–PEG₃–RM26 is a more-promising candidate than the agonist ⁶⁸Ga–NOTA–Aca–BBN_7–14 for the PET imaging of prostate cancer patients.

Initial risk stratification and staging in prostate cancer with prostatic-specific membrane antigen positron emission tomography/computed tomography: A first-stop-shop

Current imaging for prostate cancer (PCa) had limitations for risk stratification and staging. Magnetic resonance imaging frequently underestimated lymph node metastasis while bone scintigraphy often had diagnostic dilemmas. Prostatic-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET/CT) has been remarkable in PCa recurrence. Ninety-seven PSMA PET-CT scans were reanalyzed for tumor node metastases staging and risk stratification of lymph node and distant metastasis proportion. Histopathology of 23/97 patients was available as gold standard. Chi-square test was used for proportion comparison. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), overestimation, underestimation, and correct estimation of T and N stages were calculated. Kappa coefficient (κ) was derived for inter-rater agreement. Lymph node or distant metastasis detection on PSMA PET/CT increased significantly with increase in risk category. PSMA PET/CT sensitivity, specificity, PPV, and NPV for extraprostatic extension, seminal vesicle invasion, and lymph node metastases were 63.16%, 100%, 100%, 36.36%; 55%, 100%, 100%, 25%; and 65.62%, 99.31%, 87.50%, and 97.53%, respectively. Kappa coefficient showed substantial agreement between PSMA PET/CT and histopathological lymph node metastases (κ = 0.734); however, it was just in fair agreement (κ = 0.277) with T stage. PSMA PET/CT overestimated, underestimated, and correct estimated T and N stages in 8.71%, 39.13%, 52.17% and 8.71%, 4.35%, and 86.96% cases, respectively. PSMA PET/CT has potential for initial risk stratification with reasonable correct N stage estimation, however underestimates T stage. Hence, we concluded that PSMA PET/CT should be used as “ first-stop-shop” for staging and initial risk stratification of PCa with regional magnetic resonance imaging in surgically resectable cases.

MRI-directed cognitive fusion-guided biopsy of the anterior prostate tumors

PURPOSE

We aimed to evaluate the efficacy of magnetic resonance imaging (MRI)-directed cognitive fusion transrectal ultrasonography (TRUS)-guided anterior prostate biopsy for diagnosis of anterior prostate tumors and to illustrate this technique.

METHODS

A total of 39 patients with previous negative TRUS biopsy, but high clinical suspicion of occult prostate cancer, prospectively underwent prostate MRI including diffusion-weighted imaging (DWI). Patients with a suspicious anterior lesion on MRI underwent targeted anterior gland TRUS-guided biopsy with cognitive fusion technique using sagittal probe orientation. PIRADS version 1 scores (T2, DWI, and overall), lesion size, prostate-specific antigen (PSA), PSA density, and prostate gland volume were compared between positive and negative biopsy groups and between clinically significant cancer and remaining cases. Logistic regression analysis of imaging parameters and prostate cancer diagnosis was performed.

RESULTS

Anterior gland prostate adenocarcinoma was diagnosed in 18 patients (46.2%) on targeted anterior gland TRUS-guided biopsy. Clinically significant prostate cancer was diagnosed in 13 patients (33.3%). MRI lesion size, T2, DWI, and overall PIRADS scores were significantly higher in patients with positive targeted biopsies and those with clinically significant cancer (P < 0.05). Biopsies were positive in 90%, 33%, and 29% of patients with overall PIRADS scores of 5, 4, and 3 respectively. Overall PIRADS score was an independent predictor of all prostate cancer diagnosis and of clinically significant prostate cancer diagnosis.

CONCLUSION

Targeted anterior gland TRUS-guided biopsy with MRI-directed cognitive fusion enables accurate sampling and may improve tumor detection yield of anterior prostate cancer.