Prostate MRI based on PI-RADS version 2: how we review and report

Sialic acid blockade inhibits the metastatic spread of prostate cancer to bone

BACKGROUND

Bone metastasis is a common consequence of advanced prostate cancer. Bisphosphonates can be used to manage symptoms, but there are currently no curative treatments available. Altered tumour cell glycosylation is a hallmark of cancer and is an important driver of a malignant phenotype. In prostate cancer, the sialyltransferase ST6GAL1 is upregulated, and studies show ST6GAL1-mediated aberrant sialylation of N-glycans promotes prostate tumour growth and disease progression.

METHODS

Here, we monitor ST6GAL1 in tumour and serum samples from men with aggressive prostate cancer and using in vitro and in vivo models we investigate the role of ST6GAL1 in prostate cancer bone metastasis.

FINDINGS

ST6GAL1 is upregulated in patients with prostate cancer with tumours that have spread to the bone and can promote prostate cancer bone metastasis in vivo. The mechanisms involved are multi-faceted and involve modification of the pre-metastatic niche towards bone resorption to promote the vicious cycle, promoting the development of M2 like macrophages, and the regulation of immunosuppressive sialoglycans. Furthermore, using syngeneic mouse models, we show that inhibiting sialylation can block the spread of prostate tumours to bone.

INTERPRETATION

Our study identifies an important role for ST6GAL1 and α2-6 sialylated N-glycans in prostate cancer bone metastasis, provides proof-of-concept data to show that inhibiting sialylation can suppress the spread of prostate tumours to bone, and highlights sialic acid blockade as an exciting new strategy to develop new therapies for patients with advanced prostate cancer.

FUNDING

Prostate Cancer Research and the Mark Foundation For Cancer Research, the Medical Research Council and Prostate Cancer UK.

Toward confident prostate cancer detection using ultrasound: a multi-center study

PURPOSE

Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue.

METHODS

Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods.

RESULTS

PCa detection models achieve performance scores up to 76 % average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as 2 % , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue.

CONCLUSION

Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.

Unveiling the Intricacies: A Comprehensive Review of Magnetic Resonance Imaging (MRI) Assessment of T2-Weighted Hyperintensities in the Neuroimaging Landscape

T2-weighted hyperintensities in neuroimaging represent areas of heightened signal intensity on magnetic resonance imaging (MRI) scans, holding crucial importance in neuroimaging. This comprehensive review explores the T2-weighted hyperintensities, providing insights into their definition, characteristics, clinical relevance, and underlying causes. It highlights the significance of these hyperintensities as sensitive markers for neurological disorders, including multiple sclerosis, vascular dementia, and brain tumors. The review also delves into advanced neuroimaging techniques, such as susceptibility-weighted and diffusion tensor imaging, and the application of artificial intelligence and machine learning in hyperintensities analysis. Furthermore, it outlines the challenges and pitfalls associated with their assessment and emphasizes the importance of standardized protocols and a multidisciplinary approach. The review discusses future directions for research and clinical practice, including the development of biomarkers, personalized medicine, and enhanced imaging techniques. Ultimately, the review underscores the profound impact of T2-weighted hyperintensities in shaping the landscape of neurological diagnosis, prognosis, and treatment, contributing to a deeper understanding of complex neurological conditions and guiding more informed and effective patient care.

Proton versus photon therapy for high-risk prostate cancer with dose escalation of dominant intraprostatic lesions: a preliminary planning study

Background and purpose

This study aimed to investigate the feasibility of safe-dose escalation to dominant intraprostatic lesions (DILs) and assess the clinical impact using dose-volume (DV) and biological metrics in photon and proton therapy. Biological parameters defined as late grade ≥ 2 gastrointestinal (GI) and genitourinary (GU) derived from planned (D P) and accumulated dose (D A) were utilized.

Materials and methods

In total, 10 patients with high-risk prostate cancer with multiparametric MRI-defined DILs were investigated. Each patient had two plans with a focal boost to the DILs using intensity-modulated proton therapy (IMPT) and volumetric-modulated arc therapy (VMAT). Plans were optimized to obtain DIL coverage while respecting the mandatory organ-at-risk constraints. For the planning evaluation, DV metrics, tumor control probability (TCP) for the DILs and whole prostate excluding the DILs (prostate-DILs), and normal tissue complication probability (NTCP) for the rectum and bladder were calculated. Wilcoxon signed-rank test was used for analyzing TCP and NTCP data.

Results

IMPT achieved a higher Dmean for the DILs compared to VMAT (IMPT: 68.1 GyRBE vs. VMAT: 66.6 Gy, p < 0.05). Intermediate-high rectal and bladder doses were lower for IMPT (p < 0.05), while the high-dose region (V60 Gy) remained comparable. IMPT-TCP for prostate-DIL were higher compared to VMAT (IMPT: 86%; α/β = 3, 94.3%; α/β = 1.5 vs. VMAT: 84.7%; α/β = 3, 93.9%; α/β = 1.5, p < 0.05). Likewise, IMPT obtained a moderately higher DIL TCP (IMPT: 97%; α/β = 3, 99.3%; α/β = 1.5 vs. VMAT: 95.9%; α/β = 3, 98.9%; α/β = 1.5, p < 0.05). Rectal D A-NTCP displayed the highest GI toxicity risk at 5.6%, and IMPT has a lower GI toxicity risk compared to VMAT-predicted Quantec-NTCP (p < 0.05). Bladder D P-NTCP projected a higher GU toxicity than D A-NTCP, with VMAT having the highest risk (p < 0.05).

Conclusion

Dose escalation using IMPT is able to achieve a high TCP for the DILs, with the lowest rectal and bladder DV doses at the intermediate-high-dose range. The reduction in physical dose was translated into a lower NTCP (p < 0.05) for the bladder, although rectal toxicity remained equivalent.

Theranostic Robot-Assisted Radical Prostatectomy: Things Understood and Not Understood

OBJECTIVE

This study aimed to explore the benefits of theranostic robot-assisted radical prostatectomy (T-RARP) for clinically highly suspicious prostate cancer (PCa) without proven biopsies.

MATERIAL AND METHODS

Between February 2016 and December 2020, we included men with clinically highly suspicious PCa in this study. They were assessed to have possible localized PCa without any initial treatments, and were categorized into previous benign biopsies or without biopsies. Furthermore, another group of malignant biopsies with RARP in the same time frame was adopted as the control group. The endpoints were to compare the oncological outcome and functional outcome between malignant biopsies with RARP and T-RARP. p < 0.05 was considered to be significant.

RESULTS

We included 164 men with proven malignant biopsies treated with RARP as the control group. For T-RARP, we included 192 men. Among them, 129 were preoperatively benign biopsies, and 63 had no biopsies before T-RARP. Approximately 75% of men in the T-RARP group had malignant pathology in their final reports, and the other 25% had benign pathology. T-RARP provides several oncological advantages, such as a higher initial pathological T stage, lower Gleason grade, and lower odds of positive surgical margins. However, the biochemical recurrence rates were not significantly decreased. From our cohort, T-RARP (odds ratio with 95% confidence interval; erectile recovery: 3.19 (1.84-5.52), p < 0.001; continence recovery: 2.25 (1.46-3.48), p < 0.001) could result in better recovery of functional outcomes than malignant biopsies with RARP.

CONCLUSIONS

For clinically highly suspicious PCa, T-RARP was able to detect around 75% of PCa cases and preserved their functional outcomes maximally. However, in 25% of men with benign pathology, approximately 6% would have incontinence and 10% would have erectile impairment. This part should be sufficiently informed of the potential groups considering T-RARP.

Acquisition Parameters Influence Diffusion Metrics Effectiveness in Probing Prostate Tumor and Age-Related Microstructure

This study aimed to investigate the Diffusion-Tensor-Imaging (DTI) potential in the detection of microstructural changes in prostate cancer (PCa) in relation to the diffusion weight (b-value) and the associated diffusion length l_D. Thirty-two patients (age range = 50-87 years) with biopsy-proven PCa underwent Diffusion-Weighted-Imaging (DWI) at 3T, using single non-zero b-value or groups of b-values up to b = 2500 s/mm². The DTI maps (mean-diffusivity, MD; fractional-anisotropy, FA; axial and radial diffusivity, D// and D┴), visual quality, and the association between DTI-metrics and Gleason Score (GS) and DTI-metrics and age were discussed in relation to diffusion compartments probed by water molecules at different b-values. DTI-metrics differentiated benign from PCa tissue (p ≤ 0.0005), with the best discriminative power versus GS at b-values ≥ 1500 s/mm², and for b-values range 0-2000 s/mm², when the l_D is comparable to the size of the epithelial compartment. The strongest linear correlations between MD, D//, D┴, and GS were found at b = 2000 s/mm² and for the range 0-2000 s/mm². A positive correlation between DTI parameters and age was found in benign tissue. In conclusion, the use of the b-value range 0-2000 s/mm² and b-value = 2000 s/mm² improves the contrast and discriminative power of DTI with respect to PCa. The sensitivity of DTI parameters to age-related microstructural changes is worth consideration.

The Detection Performance of 18 F-Prostate-Specific Membrane Antigen-1007 PET/CT in Primary Prostate Cancer : A Systemic Review and Meta-analysis

BACKGROUND

Multiple tools are now available to determine the requirement for a biopsy to diagnose prostate cancer, and PET/CT with radiolabeled prostate-specific membrane antigen (PSMA)-targeting radiotracers has been recommended for detecting primary prostate cancer. Particularly, the radiotracer 18 F-PSMA-1007 was found to be more favorable for primary tumors compared with other PSMA-targeting radiotracers because of its low clearance via the urinary tract and better image resolution. Thus, we performed a systematic review and meta-analysis to more accurately evaluate the detection performance of 18 F-PSMA-1007 PET/CT in primary prostate cancer patients.

METHODS

An update on the databases of PubMed/MEDLINE, EMBASE, and Cochrane Library for comprehensive literature search was performed on September 30, 2021. The pooling detection rate was calculated on a per-patient basis. The pooling median of the SUV max was analyzed from the included studies. Furthermore, the positive predictive value of 18 F-PSMA-1007 PET/CT with pathologic lesions was analyzed using the criterion standard.

RESULTS

Twelve studies (540 patients total) were included in the meta-analysis. The overall pooling detection rate of 18 F-PSMA-1007 per patient was 94%, and the pooling median of SUV max located at the intraprostate tumor was 16 (range, 3.7-77.7). The positive predictive value of 18 F-PSMA-1007 per lesion with histopathological validation was 0.90, detecting regional lymph node metastasis was 0.94, and detecting localized prostatic tumors was 0.84.

CONCLUSIONS

In the current meta-analysis, we revealed the excellent performance of 18 F-PSMA-1007 to detect localized prostatic tumor lesions and regional lymph node metastasis. Moreover, the uptake of localized tumors in primary prostate cancer was nearly liver uptake and may be considered a suspicious malignancy if it was equal to or greater than the liver uptake.

Directional and inter-acquisition variability in diffusion-weighted imaging and editing for restricted diffusion

Purpose

To evaluate and quantify inter‐directional and inter‐acquisition variation in diffusion‐weighted imaging (DWI) and emphasize signals that report restricted diffusion to enhance cancer conspicuity, while reducing the effects of local microscopic motion and magnetic field fluctuations.

Methods

Ten patients with biopsy‐proven prostate cancer were studied under an Institutional Review Board‐approved protocol. Individual acquisitions of DWI signal intensities were reconstructed to calculate inter‐acquisition distributions and their statistics, which were compared for healthy versus cancer tissue. A method was proposed to detect and filter the acquisitions affected by motion‐induced signal loss. First, signals that reflect restricted diffusion were separated from the acquisitions that suffer from signal loss, likely due to microscopic motion, by imposing a cutoff value. Furthermore, corrected apparent diffusion coefficient maps were calculated by employing a weighted sum of the multiple acquisitions, instead of conventional averaging. These weights were calculated by applying a soft‐max function to the set of acquisitions per‐voxel, making the analysis immune to acquisitions with significant signal loss, even if the number of such acquisitions is high.

Results

Inter‐acquisition variation is much larger than the Rician noise variance, local spatial variations, and the estimates of diffusion anisotropy based on the current data, as well as the published values of anisotropy. The proposed method increases the contrast for cancers and yields a sensitivity of 98.8% with a false positive rate of 3.9%.

Conclusion

Motion‐induced signal loss makes conventional signal‐averaging suboptimal and can obscure signals from areas with restricted diffusion. Filtering or weighting individual acquisitions prior to image analysis can overcome this problem.

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Improvement of diffusion weighted MRI by practical B0 homogenization for head & neck cancer patients undergoing radiation therapy

BACKGROUND

MRI is a frequently used tool in radiation therapy planning. For MR-based tumor segmentation, diffusion weighted imaging plays a major role, which can fail due to excessive image artifacts for head and neck cancer imaging. Here, an easy-to-use setup is presented for imaging of head and neck cancer patients in a radiotherapy thermoplastic fixation mask.

METHODS

In a prospective head and neck cancer study, MRI data of 29 patients has been acquired at 3 different time points during radiation treatment. The data was analyzed with respect to Nyquist ghosting artifacts in the diffusion images in conventional single shot and readout segmented EPI sequences. For 9 patients, an improved setup with water bags for B₀ homogenization was used, and the impact on artifact frequency was analyzed. Additionally, volunteer measurements with B₀ fieldmaps are presented.

RESULTS

The placement of water bags to the sides of the head during MRI measurements significantly reduces artefacts in diffusion MRI. The number of artifact-free images in readout segmented EPI increased from 74% to 95% of the cases. Volunteer measurements showed a significant increase in B₀ homogeneity across slices (head foot direction) as well as within each slice.

CONCLUSIONS

The placement of water bags for B₀ homogenization is easy to implement, cost-efficient and does not impact patient comfort. Therefore, if very sophisticated soft- or hardware solutions are not present at a given site, or cannot be implemented due to restrictions from the thermoplastic mask, this is an excellent alternative to reduce artifacts in diffusion weighted imaging.

ENETS standardized (synoptic) reporting for molecular imaging studies in neuroendocrine tumours

The European Neuroendocrine Tumor Society (ENETS) promotes practices and procedures that aim to improve the standard of care delivered to patients diagnosed with or suspected of having neuroendocrine neoplasia (NEN). At its annual Scientific Advisory Board Meeting in 2018, experts in imaging, pathology and clinical care of patients with NEN drafted guidance for the standardised reporting of diagnostic studies critical to the diagnosis, grading, staging and treatment of NEN. These included pathology, radiology, endoscopy and molecular imaging procedures. In an iterative process, a synoptic reporting template for molecular imaging procedures was developed to guide personalised therapies. Following pilot implementation and refinement within the ENETS Center of Excellence network, harmonisation with specialist imaging societies including the Society of Nuclear Medicine, European Association of Nuclear Medicine and the International Cancer Imaging Society will be pursued.

Synthetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer

Prostate cancer (PCa) is the second most common cancer in men worldwide and the most frequently diagnosed cancer among men in more developed countries. The prognosis of PCa is excellent if detected at an early stage, making early screening crucial for detection and treatment. In recent years, a new form of diffusion magnetic resonance imaging called correlated diffusion imaging (CDI) was introduced, and preliminary results show promise as a screening tool for PCa. In the largest study of its kind, we investigate the relationship between PCa presence and a new variant of CDI we term synthetic correlated diffusion imaging (CDI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^s$$\end{document}s), as well as its performance for PCa delineation compared to current standard MRI techniques [T2-weighted (T2w) imaging, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging] across a cohort of 200 patient cases. Statistical analyses reveal that hyperintensity in CDI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^s$$\end{document}s is a strong indicator of PCa presence and achieves strong delineation of clinically significant cancerous tissue compared to T2w, DWI, and DCE. These results suggest that CDI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^s$$\end{document}s hyperintensity may be a powerful biomarker for the presence of PCa, and may have a clinical impact as a diagnostic aid for improving PCa screening.

Interobserver Agreement and Accuracy in Interpreting mpMRI of the Prostate: a Systematic Review

PURPOSE OF REVIEW

To present the latest evidence related to interobserver agreement and accuracy; evaluate the strengths, weaknesses, and implications of use; and outline opportunities for improvement and future development of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for detection of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI).

RECENT FINDINGS

Our review of currently available evidence suggests that recent improvements to the PI-RADS system with PI-RADS v2.1 slightly improved interobserver agreement, with generally high sensitivity and moderate specificity for the detection of clinically significant PCa. Recent evidence additionally demonstrates substantial improvement in diagnostic specificity with PI-RADS v2.1 compared with PI-RADS v2. However, results of studies examining the comparative performance of v2.1 are limited by small sample sizes and retrospective cohorts, potentially introducing selection bias. Some studies suggest a substantial improvement between v2.1 and v2, while others report no statistically significant difference. Additionally, in PI-RADS v2.1, the interpretation and reporting of certain findings remain subjective, particularly for category 2 lesions, and reader experience continues to vary significantly. These factors further contribute to a remaining degree of interobserver variability and findings of improved performance among more experienced readers. PI-RADS v2.1 appears to show at least minimal improvement in interobserver agreement, diagnostic performance, and both sensitivity and specificity, with greater improvements seen among more experienced readers. However, given the decrescent nature of these improvements and the limited power of all studies examined, the clinical impact of this progress may be marginal. Despite improvements in PI-RADS v2.1, practitioner experience in interpreting mpMRI of the prostate remains the most important factor in prostate cancer detection.

Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.

A single centre service evaluation of the pre-biopsy mpMRI pathway for prostate cancer diagnosis

Aim:

To study the efficacy and impact of the local pre-biopsy multiparametric magnetic resonance imaging (mpMRI) pathway for prostate cancer diagnosis.

Methods:

In this tertiary centre, 570 patients had prostate mpMRI across a 6-month period in 2019. A total of 511 patients met inclusion criteria for retrospective analysis. MRI reporting used the Prostate Imaging-Reporting and Data System (PI-RADS) v2.1. These were assessed alongside histological outcomes and diagnostic times. PI-RADS ⩾ 3 were recommended for biopsy consideration. Gleason scoring ⩾ 3 + 4 and 3 + 3 were used to define clinically and non-clinically significant prostate cancer (csPCa and nsPCa), respectively.

Results:

Overall prostate cancer prevalence was 40% (204/511, csPCa in 31.1%) with an overall biopsy avoidance of 32.1% (164/511). Around 69.7% (356/511) scored PI-RADS ⩾ 3 and 30.3% (155/511) scored PI-RADS 1–2. About 22.6% (35/155) of PI-RADS 1–2 patients proceeded to biopsy, demonstrating a negative predictive value of 91.43% for csPCa. For PI-RADS ⩾ 3 patients, 63.4% (197/312) of those biopsied had cancer (Gleason ⩾ 3 + 3), with 50% (156/312) demonstrating csPCa. Around 76.7% (102/133) of PI-RADS 5, 35.3% (48/136) of PI-RADS 4, 14.0% (6/43) of PI-RADS 3 and 8.6% (3/35) of PI-RADS 1–2 scores demonstrated csPCa. Overall median prostate-specific antigen (PSA) density was 0.15 ng/mL2 (IQR: 0.10–0.27/mL2). PSA density were significantly different across PI-RADS cohorts ( H = 118.8, p < 0.0001) and across all three biopsy outcomes ( H = 99.72, p < 0.0001). Only 34.3% (119/347) of biopsied patients met the NHS 28-day standard. MRI acquisition and reporting met the 14-day local standard in 96.1% (491/511). The biopsy was the most delayed component with a median of 20 days (IQR: 8–43).

Conclusion:

Pre-biopsy mpMRI with PI-RADS scoring safely avoided biopsy in almost one-third (32.1%) of patients. The use of PSA-density in risk stratifying PI-RADS 3 lesions has informed local practice in the period 2020–2021, with implementation of a PSA-density threshold of 0.12 ng/mL2. Biopsy scheduling issues and anaesthetic requirements need to be overcome to improve diagnostic waiting times.

Level of evidence:

2

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

Purpose

To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction.

Methods and materials

In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets.

Results

The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively.

Conclusions

The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-021-00414-6.

PI-RADS v2.1: What has changed and how to report

Multiparametric magnetic resonance imaging (MRI) of the prostate has become a vital imaging tool in daily radiological practice for the stratification of the risk of prostate cancer. There has been a recent update to the Prostate Imaging-Reporting and Data System (PI-RADS). The updated changes in PI-RADS, which is version 2.1, have been described with information pertaining to the recommended imaging protocols, the techniques on how to perform prostate MRI and a simplified approach to interpreting and reporting MRI of the prostate. Explanatory tables, schematic diagrams and key representative images have been used to provide the reader with a useful approach to interpreting and then stratifying lesions in the four anatomical zones of the prostate gland. The intention of this article is to address challenges of interpretation and reporting of prostate lesions in daily practice.

A nomogram based on PI-RADS v2.1 and clinical indicators for predicting clinically significant prostate cancer in the transition zone

Background

This study attempted to develop a nomogram for predicting clinically significant prostate cancer (cs-PCa) in the transition zone (TZ) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators.

Methods

We retrospectively reviewed 383 patients with suspicious prostate lesions in the TZ as a training cohort and 128 patients as the validation cohort from January 2015 to March 2020. Multivariable logistic regression analysis was performed to determine independent predictors for building a nomogram, and the performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve and decision curve.

Results

The PI-RADS v2.1 score and prostate-specific antigen density (PSAD) were independent predictors of TZ cs-PCa. The prediction model had a significantly higher AUC (0.936) than the individual predictors (0.914 for PI-RADS v2.1 score, P=0.045, 0.842 for PSAD, P<0.001). The nomogram showed good discrimination (AUC of 0.936 in the training cohort and 0.963 in the validation cohort) and favorable calibration. When the PI-RADS v2.1 score was combined with PSAD, the diagnostic sensitivity and specificity were 80.7% and 93.8%, respectively, which were better than those of the PI-RADS v2.1 score (sensitivity, 74.2%; specificity, 92.5%) and PSAD (sensitivity, 66.1%; specificity, 88.2%).

Conclusions

The newly constructed nomogram exhibits satisfactory predictive accuracy and consistency for TZ cs-PCa. PI-RADS v2.1 based on bp-MRI is a strong predictor in the detection of TZ cs-PCa. Adding PSAD to PI-RADS v2.1 could improve its diagnostic performance, thereby avoiding unnecessary biopsies.

Interobserver agreement of Prostate Imaging–Reporting and Data System (PI-RADS–v2)

Background

A retrospective study was conducted on 71 consecutive patients with suspected prostate cancer (PCa) with a mean age of 56 years and underwent mp-MRI of the prostate at 3 Tesla MRI. Two readers recognized all prostatic lesions, and each lesion had a score according to Prostate Imaging–Reporting and Data System version 2 (PI-RADS-v2).

Purpose of the study

To evaluate the interobserver agreement of PI-RADS-v2 in characterization of prostatic lesions using multiparametric MRI (mp-MRI) at 3 Tesla MRI.

Results

The overall interobserver agreement of PI-RADS-v2 for both zones was excellent (k = 0.81, percent agreement = 94.9%). In the peripheral zone (PZ) lesions are the interobserver agreement for PI-RADS II (k = 0.78, percent agreement = 83.9%), PI-RADS III (k = 0.66, percent agreement = 91.3 %), PI-RADS IV (k = 0.69, percent agreement = 93.5%), and PI-RADS V (k = 0.91, percent agreement = 95.7 %). In the transitional zone (TZ) lesions are the interobserver agreement for PI-RADS I (k = 0.98, percent of agreement = 96%), PI-RADS II (k = 0.65, percent agreement = 96%), PI-RADS III (k = 0.65, percent agreement = 88%), PI-RADS IV (k = 0.83, percent agreement = 96%), and PI-RADS V (k = 0.82, percent agreement = 92%).

Conclusion

We concluded that PI-RADS-v2 is a reliable and a reproducible imaging modality for the characterization of prostatic lesions and detection of PCa.

Value of clinical parameters and MRI with PI-RADSV2 in predicting seminal vesicle invasion of prostate cancer

OBJECTIVE

To investigate the usefulness of magnetic resonance imaging (MRI) with Prostate Imaging Reporting and Data System version 2 (PI-RADS_V2) and clinical parameters in predicting seminal vesicle invasion (SVI).

MATERIAL AND METHODS

In this retrospective study, we identified 569 prostate cancer patients who underwent radical prostatectomy with MRI before surgery. SVI was interpreted with PI-RADS_V2. Clinical parameters such as the prostate-specific antigen (PSA) and Gleason score (GS) were analyzed for the prediction of SVI. Logistic regression models and receiver operating characteristic (ROC) curves were used to evaluate SVI based on clinical parameters and MRI with PI-RADS_V2.

RESULTS

The median age at presentation was 67 years (43-85 years). The median PSA level was 6.1 ng/mL (2.2-72.8 ng/mL). There were 113 patients with a biopsy GS of ≥ 8. A total of 34 patients (6.0%) were interpreted to have SVI by MRI of which 20 were true positive, and 52 patients (9.1%) had true SVI in the final pathologic analysis. In multivariable analysis, PSA (HR: 1.03, 95% CI: 1.00-1.07), biopsy GS ≥ 8 (HR: 4.14, 95% CI: 2.12-8.09), and MRI with PI-RADS_V2 (HR: 14.67, 95% CI: 6.34-33.93) were significantly associated with pathologic SVI. The area under the curve of the model based on the clinical parameters PSA and GS plus MRI (0.862) was significantly larger than that of the model based on clinical parameters alone (0.777, p < 0.001).

CONCLUSIONS

MRI with PI-RADS_V2 using the clinical parameters PSA and GS was effective in predicting SVI.

Management of prostate cancer after holmium laser enucleation of the prostate

INTRODUCTION

Holmium laser enucleation of the prostate (HoLEP) is effective in treating lower urinary tract symptoms from prostatic disease. We investigate the role of HoLEP in the management of patients with benign prostatic hypertrophy (BPH) and prostate cancer (CaP).

METHODS

Retrospective review of data regarding all patients undergoing HoLEP at a single institution was performed. Pre- and postoperative PSA, multiparametric MRI, and pathology results were analyzed for those with CaP identified prior to or incidentally at HoLEP.

RESULTS

From February 2016 to February 2020, 201 patients underwent HoLEP. Twelve patients had CaP diagnosed before HoLEP: 6 patients with GG1 are on active surveillance (AS), 3 of 4 intermediate-risk patients are on AS and 1 received treatment for disease progression, and both high-risk CaP patients achieved symptomatic benefit from HoLEP and are receiving systemic therapy for CaP. Twenty-one patients (11.1%) with incidentally detected CaP at HoLEP remain on AS or watchful waiting based on clinical scenario.

CONCLUSION

Screening for CaP in HoLEP candidates with PSA and MRI is recommended given that >10% will have incidental CaP. After HoLEP for BPH/LUTS, patients with CaP can be surveilled with PSA and/or MRI. Further investigation is warranted to determine the durability of success of these approaches.

Mini review: Personalization of the radiation therapy management of prostate cancer using MRI-based radiomics

Decisions on how to treat prostate cancer with radiation therapy are guideline-based but as such guidelines have been developed for populations of patients, this invariably leads to overly aggressive treatment in some patients and insufficient treatment in others. Heterogeneity within prostate tumors and in metastatic sites, even within the same patient, is believed to be a major cause of treatment failure. Radiomics biomarkers, more commonly referred to as radiomics 'features", provide readily available, cost-effective, non-invasive tools for screening, detecting tumors and serial monitoring of patients, including assessments of response to therapy and identification of therapeutic complications. Radiomics offers the potential to analyse whole tumors in 3D, as well as sub-regions or 'habitats' within tumors. Combining quantitative information from imaging with pathology, demographic details and other biomarkers will pave the way for personalised treatment selection and monitoring in prostate cancer. The aim of this review is to consider if MRI-based radiomics can bridge the gap between population-based management and personalised management of prostate cancer.

Prostate MRI: practical guidelines for interpreting and reporting according to PI-RADS version 2.1

The increasing precision of multiparametric magnetic resonance imaging of the prostate, together with greater experience and standardization in its interpretation, has given this technique an important role in the management of prostate cancer, the most prevalent non-cutaneous cancer in men. This article reviews the concepts in PI-RADS version 2.1 for estimating the probability and zonal location of significant tumors of the prostate, using a practical approach that includes current considerations about the prerequisites for carrying out the test and recommendations for interpreting the findings. It emphasizes benign findings that can lead to confusion and the criteria for evaluating the probability of local spread, which must be included in the structured report.

The role of multiparametric magnetic resonance imaging and magnetic resonance-guided biopsy in active surveillance for low-risk prostate cancer: A systematic review

The performance of multiparametric magnetic resonance imaging (mpMRI) and subsequent biopsy in monitoring prostate cancer in men on active surveillance (AS) have not been defined clearly. In this systematic review, we aimed to review current literature about the usage of MRI examination in men with low-risk prostate cancer during active surveillance. For that, we searched seven databases to include all studies reporting magnetic resonance imaging in the AS of low-risk prostate cancer. We finally included 11 studies with 1237 patients included. Our results showed an adequate sensitivity and specificity of both modalities to detect disease progression; including disease upgrading and upstaging. However, the performance in the prediction of unfavorable disease was inferior to the detection of upgrading and upstaging. In terms of MRGB, the previous literature agreed on the superiority of using a combination of different biopsy schemes to get a better progression section. Noteworthy, mp-MRI and MRGB had a good predictive value limited to the first year, with TRUSGB showing a superior role in detecting patients with a GS ≥ 7, after that. In conclusion, both of mpMRI and MRGB have shown an adequate performance on assessing disease progression in the AS of low-risk prostate cancer patients. They can be used for disease staging and grading for successful treatment planning.

•

In comparison to the literature, few papers discuss the benefit of MRI screening in low-risk prostate cancer groups.

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Biopsy is considered more invasive than MRI, thus reducing the burden of such methods on the patients.

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PSA values can be misinterpreted especially that it can rise in other diseases such as Benign Prostatic Hyperplasia.

Quantifying the Effect of Location Matching on Accuracy of Multiparametric Magnetic Resonance Imaging Prior to Prostate Biopsy-A Multicentre Study

Background

Multiparametric magnetic resonance imaging (mpMRI) has shown promise to improve detection of prostate cancer over conventional methods. However, most studies do not describe whether the location of mpMRI lesions match that of cancer found at biopsy, which may lead to an overestimation of accuracy.

Objective

To quantitate the effect of mapping locations of mpMRI lesions to locations of positive biopsy cores on the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mpMRI.

Design setting and participant

We retrospectively identified patients having mpMRI of the prostate preceding prostate biopsy at three centres from 2013 to 2016. Men with targetable lesions on imaging underwent directed biopsy in addition to systematic biopsy. We correlated locations of positive mpMRI lesions with those of positive biopsy cores, defining a match when both were in the same sector of the prostate. We defined positive mpMRI as Prostate Imaging Reporting and Data System (PI-RADS) score ≥4 and significant cancer at biopsy as grade group ≥2.

Outcome measurements and statistical analysis

Sensitivity, specificity, PPV, and NPV were calculated with and without location matching.

Results and limitations

Of 446 patients, 247 (55.4%) had positive mpMRI and 232 (52.0%) had significant cancer at biopsy. Sensitivity and NPV for detecting significant cancer with location matching (both 63.4%) were decreased compared with those without location matching (77.6% and 73.9%, respectively). Of the 85 significant cancers not detected by mpMRI, most were of grade group 2 (64.7%, 55/85).

Conclusions

We report a 10-15% decrease in sensitivity and NPV when location matching was used to detect significant prostate cancer by mpMRI. False negative mpMRI remains an issue, highlighting the continued need for biopsy and for improving the standards around imaging quality and reporting.

Patient summary

The true accuracy of multiparametric magnetic resonance imaging (mpMRI) must be determined to interpret results and better counsel patients. We mapped the location of positive mpMRI lesions to where cancer was found at biopsy and found, when compared with matching to cancer anywhere in the prostate, that the accuracy of mpMRI decreased by 10-15%.

Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis

Background

Automatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer.

Methods

Head&neck cancer patients underwent multi-parametric MRI including T2w, pre- and post-contrast T1w, T2*, perfusion (k_trans, v_e) and diffusion (ADC) measurements at 3 time points before and during radiochemotherapy. The 7 different MRI contrasts (input channels) and manually defined gross tumor volumes (primary tumor and lymph node metastases) were used to train CNNs for lesion segmentation. A reference CNN with all input channels was compared to individually trained CNNs where one of the input channels was left out to identify which MRI contrast contributes the most to the tumor segmentation task. A statistical analysis was employed to account for random fluctuations in the segmentation performance.

Results

The CNN segmentation performance scored up to a Dice similarity coefficient (DSC) of 0.65. The network trained without T2* data generally yielded the worst results, with ΔDSC_GTV-T = 5.7% for primary tumor and ΔDSC_GTV-Ln = 5.8% for lymph node metastases compared to the network containing all input channels. Overall, the ADC input channel showed the least impact on segmentation performance, with ΔDSC_GTV-T = 2.4% for primary tumor and ΔDSC_GTV-Ln = 2.2% respectively.

Conclusions

We developed a method to reduce overall scan times in MRI protocols by prioritizing those sequences that add most unique information for the task of automatic tumor segmentation. The optimized CNNs could be used to aid in the definition of the GTVs in radiotherapy planning, and the faster imaging protocols will reduce patient scan times which can increase patient compliance.

Trial registration

The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under register number DRKS00003830 on August 20th, 2015.

Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction

Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (k_trans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 ± 0.18 for data including distortion-corrected ADC and 0.37 ± 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use.

Current status and future prospective of focal therapy for localized prostate cancer: development of multiparametric MRI, MRI-TRUS fusion image-guided biopsy, and treatment modalities

Multiparametric magnetic resonance imaging (mpMRI) has been increasingly used to diagnose clinically significant prostate cancer (csPC) because of its usefulness in combination with anatomic and functional data. MRI-targeted biopsy, such as MRI-transrectal ultrasound (TRUS) fusion image-guided prostate biopsy, has high accuracy in the detection and localization of csPC. This novel diagnostic technique contributes to the development of tailor-made medicine as focal therapy, which cures the csPC while preserving the anatomical structures related to urinary and sexual function. In the early days of focal therapy, TRUS-guided systematic biopsy was used for patient selection, and treatment was performed for patients with low-risk PC. With the introduction of mpMRI and mapping biopsy, the treatment range is now determined based on individualized cancer localization. In recent prospective studies, 87.4% of treated patients had intermediate- and high-risk PC. However, focal therapy has two main limitations. First, a randomized controlled trial would be difficult to design because of the differences in pathological features between patients undergoing focal therapy and radical treatment. Therefore, pair-matched studies and/or historical controlled studies have been performed to compare focal therapy and radical treatment. Second, no long-term (≥ 10-year) follow-up study has been performed. However, recent prospective studies have encouraged the use of focal therapy as a treatment strategy for localized PC because it contributes to high preservation of continence and erectile function.

Combining Prostate-Specific Antigen Parameters With Prostate Imaging Reporting and Data System Score Version 2.0 to Improve Its Diagnostic Accuracy

Background

Any non-invasive test that can predict the absence of prostate cancer (PCa) or absence of clinically significant PCa (CSPCa) is necessary, as it can reduce the number of unnecessary biopsies in patients with gray zone prostate-specific antigen (PSA, 4 - 10 ng/mL). This study evaluated the diagnostic performance of free PSA% and PSA density (PSAD), and Prostate Imaging Reporting and Data System (PIRADS) score (version 2.0) alone and combined in predicting CSPCa in patients with PSA between 4 and 10 ng/mL.

Methods

This prospective study included a total of 104 consecutive patients with lower urinary tract symptoms (LUTS) and serum PSA between 4 and 10 ng/mL, with or without abnormal digital rectal examination (DRE) findings or any hypoechoic lesion on ultrasound sonography of prostate and without prior transrectal ultrasound (TRUS) biopsy of prostate. PIRADS score was calculated using multi-parametric magnetic resonance imaging (mp-MRI) before TRUS biopsy of prostate. Relationships among PIRADS score, PSAD, free PSA% and presence of CSPCa in TRUS biopsy were statistically analyzed.

Results

In patients with CSPCa, significantly higher median age (P = 0.001), PSA level (P < 0.001), PSAD (P < 0.001) and significantly lower prostate volume (P < 0.001) and free PSA% were observed as compared to patients with non-CSPCa. Significantly higher proportion of patients with CSPCa showed PIRADS positive test compared to those with non-CSPCa (86.4% vs. 53.3%, P < 0.001). Cut-off values for PSAD and free PSA% were 0.12 ng/mL² and 25%, respectively. Age, PSAD and free PSA% were significant predictors of PCa, while age and PSAD were significant predictors of CSPCa. Criteria 2, 3 and 4 demonstrated higher specificity and positive predictive value (PPV) in predicting CSPCa as compared to criterion 1. The overall accuracies of criterion 1, 2, 3 and 4 were 64.42%, 85.58%, 80.77% and 79.81%, respectively. The area under the curve (AUC) values of criterion 2, 3 and 4 were higher (0.827, 0.732 and 0.792) than criterion 1 (0.665).

Conclusion

Using PIRADS score for predicting CSPCa as a screening test, criteria 2, 3 and 4 have much higher diagnostic performance and present accuracy of mp-MRI to predict CSPCa can be increased with addition of PSAD and free PSA%.

Radiological semantics discriminate clinically significant grade prostate cancer

Background

Identification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease. The difficulty in obtaining a consensus by the Prostate Imaging and Data Systems (PI-RADS) scores coupled with disagreements in interpreting multi-parametric Magnetic Resonance Imaging (mpMRI) has resulted in increased variability in reporting findings and evaluating the utility of this imaging modality in detecting clinically significant prostate cancer. This study assess the ability of radiological traits (semantics) observed on multi-parametric Magnetic Resonance images (mpMRI) to discriminate clinically significant prostate cancer.

Methods

We obtained multi-parametric MRI studies from 103 prostate cancer patients with 167 targeted biopsies from a single institution. The study was approved by our Institutional Review Board (IRB) for retrospective analysis. The biopsy location had been identified and marked by a clinical radiologist for targeted biopsy based on initial study interpretation. Using the target locations, two study radiologists independently re-evaluated the scans and scored 16 semantic traits on a point scale (up to 5 levels) based on mpMRI images. The semantic traits describe size, shape, and border characteristics of the prostate lesion, as well as presence of disease around lymph nodes (lymphadenopathy). We built a linear classifier model on these semantic traits and related to pathological outcome to identify clinically significant tumors (Gleason Score ≥ 7). The discriminatory ability of the predictors was tested using cross validation method randomly repeated and ensemble values were reported. We then compared the performance of semantic predictors with the PI-RADS predictors.

Results

We found several semantic features individually discriminated high grade Gleason score (ADC-intensity, Homogeneity, early-enhancement, T2-intensity and extraprostatic extention), these univariate predictors had an average area under the receiver operator characteristics (AUROC) ranging from 0.54 to 0.68. Multivariable semantic predictors with three features (ADC-intensity; T2-intensity, enhancement homogenicity) had an average AUROC of 0.7 [0.43, 0.94]. The PI-RADS based predictor had average AUROC of 0.6 [0.47, 0.75].

Conclusion

We find semantics traits are related to pathological findings with relatively higher reproducibility between radiologists. Multivariable predictors formed on these traits shows higher discriminatory ability compared to PI-RADS scores.

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.

Simplified Luminal Water Imaging for the Detection of Prostate Cancer From Multiecho T2 MR Images

BACKGROUND

Luminal water imaging (LWI) suffers less from imaging artifacts than the diffusion-weighted imaging used in multiparametric MRI of the prostate. LWI obtains multicompartment tissue information from a multiecho T2 dataset.

PURPOSE

To compare a simplified LWI technique with apparent diffusion coefficient (ADC) in classifying lesions based on groupings of PI-RADS v2 scores. Secondary aims were to investigate whether LWI differentiates between histologically confirmed tumor and normal tissue as effectively as ADC, and whether LWI is correlated with the multicompartment parameters of the vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) diffusion model.

STUDY TYPE

A subset of a larger prospective study.

POPULATION

In all, 65 male patients aged 49-79 were scanned.

FIELD STRENGTH/SEQUENCE

A 32-echo T2 and a six b-value diffusion sequence (0, 90, 500, 1500, 2000, 3000 s/mm2 ) at 3T.

ASSESSMENT

Regions of interest were placed by a board-certified radiologist in areas of lesion and benign tissue and given PI-RADS v2 scores.

STATISTICAL TESTS

Receiver operating characteristic and logistic regression analyses were performed.

RESULTS

LWI classifies tissue as PI-RADS 1,2 or PI-RADS 3,4,5 with an area under curve (AUC) value of 0.779, compared with 0.764 for ADC. LWI differentiated histologically confirmed malignant from nonmalignant tissue with AUC, sensitivity, and specificity values of 0.81, 75%, and 87%, compared with 0.75, 83%, and 67% for ADC. The microstructural basis of the LWI technique is further suggested by the correspondence with the VERDICT diffusion-based microstructural imaging technique, with α, A1 , A2 , and LWF showing significant correlations.

DATA CONCLUSION

LWI alone can predict PI-RADS v2 score groupings and detect histologically confirmed tumors with an ability similar to ADC alone without the limitations of diffusion-weighted MRI. This is important, given that ADC has an advantage in these tests as it already informs PI-RADS v2 scoring. LWI also provides multicompartment information that has an explicit biophysical interpretation, unlike ADC.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:910-917.

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.

Magnetic resonance imaging-transrectal ultrasound fusion image-guided prostate biopsy: Current status of the cancer detection and the prospects of tailor-made medicine of the prostate cancer

Multi-parametric magnetic resonance imaging (mpMRI) has been increasingly used to diagnose clinically significant prostate cancer (csPCa) because of its growing availability and its ability to combine anatomical and functional data. Magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) fusion imaging provides MRI information with TRUS images for prostate biopsies. This technique combines the superior sensitivity of MRI for targeting suspicious lesions with the practicality and familiarity of TRUS. MRI-TRUS fusion image-guided prostate biopsy is performed with different types of image registration (rigid vs. elastic) and needle tracking methods (electromagnetic tracking vs. mechanical position encoders vs. image-based software tracking). A systematic review and meta-analysis showed that MRI-targeted biopsy detected csPCa at a significantly higher rate than did TRUS-guided biopsy, while it detected significantly fewer cases of insignificant PCas. In addition to the high accuracy of MRI-targeted biopsy for csPCa, localization of csPCa is accurate. The ability to choose the route of biopsy (transperineal vs. transrectal) is required, depending on the patients' risk and the location and size of suspicious lesions on mpMRI. Fusion image-guided prostate biopsy has the potential to allow precise management of prostate cancer, including active surveillance, radical treatment, and focal therapy.

Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors

Prostate cancer diagnosis and treatment continues to be a major public health challenge. The heterogeneity of the disease is one of the major factors leading to imprecise diagnosis and suboptimal disease management. The improved resolution of functional multi-parametric magnetic resonance imaging (mpMRI) has shown promise to improve detection and characterization of the disease. Regions that subdivide the tumor based on Dynamic Contrast Enhancement (DCE) of mpMRI are referred to as DCE-Habitats in this study. The DCE defined perfusion curve patterns on the identified tumor habitat region are used to assess clinical significance. These perfusion curves were systematically quantified using seven features in association with the patient biopsy outcome and classifier models were built to find the best discriminating characteristics between clinically significant and insignificant prostate lesions defined by Gleason score (GS). Multivariable analysis was performed independently on one institution and validated on the other, using a multi-parametric feature model, based on DCE characteristics and ADC features. The models had an intra institution Area under the Receiver Operating Characteristic (AUC) of 0.82. Trained on Institution I and validated on the cohort from Institution II, the AUC was also 0.82 (sensitivity 0.68, specificity 0.95).

PI-RADS v2 and ADC values: is there room for improvement?

PURPOSE

To determine the diagnostic accuracy of ADC values in combination with PI-RADS v2 for the diagnosis of clinically significant prostate cancer (CS-PCa) compared to PI-RADS v2 alone.

MATERIALS AND METHODS

This retrospective study included 155 men whom underwent 3-Tesla prostate MRI and subsequent MR/US fusion biopsies at a single non-academic center from 11/2014 to 3/2016. All scans were performed with a surface coil and included T2, diffusion-weighted, and dynamic contrast-enhanced sequences. Suspicious findings were classified using Prostate Imaging Reporting and Data System (PI-RADS) v2 and targeted using MR/US fusion biopsies. Mixed-effect logistic regression analyses were used to determine the ability of PIRADS v2 alone and combined with ADC values to predict CS-PCa. As ADC categories are more practical in clinical situations than numeric values, an additional model with ADC categories of ≤ 800 and > 800 was performed.

RESULTS

A total of 243 suspicious lesions were included, 69 of which were CS-PCa, 34 were Gleason score 3+3 PCa, and 140 were negative. The overall PIRADS v2 score, ADC values, and ADC categories are independent statistically significant predictors of CS-PCa (p < 0.001). However, the area under the ROC of PIRADS v2 alone and PIRADS v2 with ADC categories are significantly different in both peripheral and transition zone lesions (p = 0.026 and p = 0.03, respectively) Further analysis of the ROC curves also shows that the main benefit of utilizing ADC values or categories is better discrimination of PI-RADS v2 4 lesions.

CONCLUSION

ADC values and categories help to diagnose CS-PCa when lesions are assigned a PI-RADS v2 score of 4.

Detection of extraprostatic disease and seminal vesicle invasion in patients undergoing magnetic resonance imaging-targeted prostate biopsies

Background

Finding incidental extraprostatic extension (EPE) or seminal vesicle invasion (SVI) by prostate cancer (PCa) is rare on standard prostate biopsy. We evaluated the clinical-pathologic features associated with EPE and SVI on multiparametric magnetic resonance imaging (MRI)/ultrasound (US) fusion-guided targeted biopsy (TB).

Methods

A retrospective review was performed from 2014-2017, selecting patients who had undergone TB. Clinical, pathologic, and radiologic features were evaluated.

Results

Five out of 333 (1.5%) patients who had PCa detected on TB had EPE and/or SVI. The average age and prostate-specific antigen (PSA) was 71 years and 17 ng/mL, respectively. The average number of cores taken on TB was 4.2. Two patients had a prior negative SB and two patients had a prior positive SB, one of which underwent radiation therapy. All patients had a PIRADSv2 suspicion score of 4 or 5. Four out of five (80%) patients underwent both SB and concurrent TB, of which 3/4 (75%) had EPE identified only on TB. One out of four (25%) patients also had both EPE and SVI, identified only on TB. One patient underwent only TB for MRI suspicion of SVI, which was pathologically confirmed on TB. On TB, one patient had Grade Group 3, two patients had Grade Group 4, and two patients had Grade Group 5 PCa. Perineural invasion (PNI) was present in 4/5 (80%) patients on TB.

Conclusions

Based on our small series, we hypothesize that MRI/US fusion TB outperforms SB in the identification of EPE and SVI. However, given the small sample size and the overall rarity of these pathologic findings on prostate biopsy, further validation is needed.

Imaging for the selection and monitoring of men on active surveillance for prostate cancer

Traditional prostate imaging is fairly limited, and only a few imaging modalities have been used for this purpose. Until today, grey scale ultrasound was the most widely used method for the characterization of the prostatic gland, however its limitations for prostate cancer (PCa) detection are well known and hence ultrasound is primarily used to localize the prostate and facilitate template prostate biopsies. In the past decade, multiparametric magnetic resonance imaging (mpMRI) of the prostate has emerged as a promising tool for the detection of PCa. Evidence has shown the value of mpMRI in the active surveillance (AS) population, given its ability to detect more aggressive disease, with data building up and supporting its use for the selection of patients suitable for surveillance. Additionally, mpMRI targeted biopsies have shown an improved detection rate of aggressive PCa when compared to regular transrectal ultrasound (TRUS) guided biopsies. Current data supports the use of mpMRI in patients considered for AS for reclassification purposes; with a negative mpMRI indicating a decreased risk of reclassification. However, a percentage of patients with negative imaging or low suspicion lesions can experience reclassification, highlighting the importance of repeat confirmatory biopsy regardless of mpMRI findings. At present, no robust data is available to recommend the substitution of regular biopsies with mpMRI in the follow-up of patients on AS and efforts are being made to determine the role of integrating genomic markers with imaging with the objective of minimizing the need of biopsies during the follow up period.

Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings

BACKGROUND

Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS).

PURPOSE

To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients.

STUDY TYPE

Retrospective.

SUBJECTS MODEL

MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy.

FIELD STRENGTH/SEQUENCE

3T, T2 -weighted (T2 w) and diffusion-weighted (DW) MRI.

ASSESSMENT

A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2 w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative-biopsy-negative (Group 1, N = 15), MRI-positive-biopsy-positive (Group 2, N = 16), MRI-negative-biopsy-positive (Group 3, N = 10), and MRI-positive-biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2 w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum-redundancy-maximum-relevance and used to construct three machine-learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease.

STATISTICAL TESTS

Wilcoxon rank-sum tests with P < 0.05 considered statistically significant.

RESULTS

Seven T2 w-based (First-order Statistics, Haralick, Laws, and Gabor) and three ADC-based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone.

DATA CONCLUSION

Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Evaluating the performance of PI-RADS v2 in the non-academic setting

PURPOSE

To evaluate the utility of PI-RADS v2 to diagnose clinically significant prostate cancer (CS-PCa) with magnetic resonance ultrasound (MR/US) fusion-guided prostate biopsies in the non-academic setting.

MATERIALS/METHODS

Retrospective analysis of men whom underwent prostate multiparametric MRI and subsequent MR/US fusion biopsies at a single non-academic center from 11/2014 to 3/2016. Prostate MRIs were performed on a 3-Tesla scanner with a surface body coil. The Prostate Imaging Reporting and Data System (PI-RADS) v2 scoring algorithm was utilized and MR/US fusion biopsies were performed in selected cases. Mixed effect logistic regression analyses and receiver-operating characteristic (ROC) curves were performed on PI-RADS v2 alone and combined with PSA density (PSAD) to predict CS-PCa.

RESULTS

170 patients underwent prostate MRI with 282 PI-RADS lesions. MR/US fusion diagnosed 71 CS-PCa, 33 Gleason score 3+3, and 168 negative. PI-RADS v2 score is a statistically significant predictor of CS-PCa (P < 0.001). For each one-point increase in the overall PI-RADS v2 score, the odds of having CS-PCa increases by 4.2 (95% CI 2.2-8.3). The area under the ROC curve for PI-RADS v2 is 0.69 (95% CI 0.63-0.76) and for PI-RADS v2 + PSAD is 0.76 (95% CI 0.69-0.82), statistically higher than PI-RADS v2 alone (P < 0.001). The rate of CS-PCa was about twice higher in men with high PSAD (≥0.15) compared to men with low PSAD (<0.15) when a PI-RADS 4 or 5 lesion was detected (P = 0.005).

CONCLUSION

PI-RADS v2 is a strong predictor of CS-PCa in the non-academic setting and can be further strengthened when utilized with PSA density.

Prospective Evaluation of 68Ga-RM2 PET/MRI in Patients with Biochemical Recurrence of Prostate Cancer and Negative Findings on Conventional Imaging

⁶⁸Ga-labeled DOTA-4-amino-1-carboxymethyl-piperidine-d-Phe-Gln-Trp-Ala-Val-Gly-His-Sta-Leu-NH₂ (⁶⁸Ga-RM2) is a synthetic bombesin receptor antagonist that targets gastrin-releasing peptide receptor (GRPr). GRPr proteins are highly overexpressed in several human tumors, including prostate cancer (PCa). We present data from the use of ⁶⁸Ga-RM2 in patients with biochemical recurrence (BCR) of PCa and negative findings on conventional imaging. Methods: We enrolled 32 men with BCR of PCa, who were 59-83 y old (mean ± SD, 68.7 ± 6.4 y). Imaging started at 40-69 min (mean, 50.5 ± 6.8 min) after injection of 133.2-151.7 MBq (mean, 140.6 ± 7.4 MBq) of ⁶⁸Ga-RM2 using a time-of-flight-enabled simultaneous PET/MRI scanner. T1-weighted, T2-weighted, and diffusion-weighted images were acquired. Results: All patients had a rising level of prostate-specific antigen (PSA) (range, 0.3-119.0 ng/mL; mean, 10.1 ± 21.3 ng/mL) and negative findings on conventional imaging (CT or MRI, and a ^99mTc-methylene diphosphonate bone scan) before enrollment. The observed ⁶⁸Ga-RM2 PET detection rate was 71.8%. ⁶⁸Ga-RM2 PET identified recurrent PCa in 23 of the 32 participants, whereas the simultaneous MRI scan identified findings compatible with recurrent PCa in 11 of the 32 patients. PSA velocity was 0.32 ± 0.59 ng/mL/y (range, 0.04-1.9 ng/mL/y) in patients with negative PET findings and 2.51 ± 2.16 ng/mL/y (range, 0.13-8.68 ng/mL/y) in patients with positive PET findings (P = 0.006). Conclusion:⁶⁸Ga-RM2 PET can be used for assessment of GRPr expression in patients with BCR of PCa. High uptake in multiple areas compatible with cancer lesions suggests that ⁶⁸Ga-RM2 is a promising PET radiopharmaceutical for localization of disease in patients with BCR of PCa and negative findings on conventional imaging.

An update of pitfalls in prostate mpMRI: a practical approach through the lens of PI-RADS v. 2 guidelines

Objectives

The aim of the current report is to provide an update in the imaging interpretation of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), with a special focus on how to discriminate pathological tissue from the most common pitfalls that may be encountered during daily clinical practice using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 guidelines.

Methods

All the cases that are shown in this pictorial review comply with the European Society of Urogenital Radiology (ESUR) guidelines for technical mpMRI requirements.

Results

Despite the standardised manner to report mpMRI (PI-RADS v. 2), some para-physiologic appearances of the prostate can mimic cancer. As such, it is crucial to be aware of these pitfalls, in order to avoid the under/overestimation of prostate cancer.

Conclusions

A detailed knowledge of normal and abnormal findings in mpMRI of the prostate is pivotal for an accurate management of the wide spectrum of clinical scenarios that radiologists may encounter during their daily practice.

Teaching Points

• Some para-physiologic appearances of the prostate may mimic cancer.

• Knowledge of normal and abnormal findings in prostate mpMRI is pivotal.

• Any radiologist involved in prostate mpMRI reporting should be aware of pitfalls.

Multi-institutional nomogram predicting benign prostate pathology on magnetic resonance/ultrasound fusion biopsy in men with a prior negative 12-core systematic biopsy

BACKGROUND

Prostate multiparametric magnetic resonance imaging (mpMRI) may be recommended for patients with a prior negative systematic biopsy (SB). However, a proportion of these patients will continue to have no prostate cancer (PCa) identified on magnetic resonance/ultrasound fusion biopsy (FB) despite abnormal mpMRI findings.

METHODS

In this multi-institutional, retrospective study, clinical and mpMRI parameters were assessed for 285 consecutive patients with at least 1 prior negative biopsy who underwent FB for a Prostate Imaging Reporting and Data System (PI-RADS) score of 3 to 5 at the University of Rochester Medical Center from December 2014 to December 2016, or at the University of Alabama at Birmingham from February 2014 to February 2017. Nomograms were generated for predicting benign prostate pathology on both the targeted biopsy and the concurrent SB.

RESULTS

Benign pathology was found in 132 of 285 patients (46.3%). In a multivariate analysis, the predictors of benign prostate pathology on FB were age, prostate-specific antigen, prostate volume, and PI-RADS score. The predicted probabilities were plotted on a receiver operating characteristic curve, and the area under the curve was 0.825. The nomogram demonstrated excellent calibration and a high net benefit in a decision curve analysis. With a theoretical cutoff probability of ≥0.7 used to recommend deferment of FB, 61 of 285 patients (21.4%) would have avoided an unnecessary biopsy, and only 4 of 285 patients (1.4%) with PCa with a Gleason score ≥ 3 + 4 would have been missed.

CONCLUSIONS

False-positive mpMRI examinations may occur in up to 46.3% of patients with a prior negative biopsy. Thus, a multi-institutional nomogram has been developed and validated for predicting benign pathology after FB in patients with a prior negative biopsy, and this may help to reduce the number of unnecessary biopsies in the setting of abnormal mpMRI findings. Cancer 2018;124:278-85. © 2017 American Cancer Society.

The role of different PI-RADS versions in prostate multiparametric magnetic resonance tomography assessment

Background. Standardised Prostate Imaging Reporting and Data System (PI-RADS) guidelines for the assessment of prostate alterations were designed for the assessment of prostate pathology. Published by the ESUR in 2012, PI-RADS v1 was based on the total score of different MRI sequences with subsequent calculation. PI-RADS v2 was published by the American College of Radiology in 2015 and featured different assessment criteria for prostate peripheral and transitory zones.

Aim. To assess the correlations of PI-RADS v1 and PI-RADS v2 with Gleason score values and to define their predictive values of the diagnosis of prostate cancer.

Materials and methods. A retrospective analysis of 66 patients. Prostate specific antigen (PSA) value and the Gleason score (GS) were assessed. One the most malignant focal lesion was selected in the peripheral zone of each lobe of the prostate (91 in total). Statistical analysis was carried out applying SPSS software, v.23, p < 0.05.

Results. Focal lesions assessed by PI-RADS v1 score: 10% – 1, 12% – 2, 41% – 3, 23% – 4, 14% – 5. Assessment applying PI-RADS v.2: 20% – 1, 7.5% – 2, 26%, 29.5%, and 17% were assessed by 3, 4, and 5 scores. Statistically relevant correlation was found only between GS and PI-RADS (p = 0.033).

The positive predictive value of both versions of PI-RADS – 75%, negative predictive value of PI-RADS v1 – 46%, PI-RADS v2 – 43%.

Conclusions. PI-RADS v1 was more statistically relevant in assessing the grade of tumour. Prediction values were similar in both versions

Assessing the validity of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) scoring system in diagnosis of peripheral zone prostate cancer

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MP-MRI will play a major role in next decades for early detection, characterization, and local and even distant staging of prostate cancer.

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mp-MRI of prostate using PI-RADS v2 scoring system proved high sensitivity and specificity in diagnosis of prostate cancer.

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SO PI-RADS v2 scoring system using mp-MRI recommended as non-invasive diagnostic tool correlated to TRUS guided biopsy pathological results.

The purpose

Assessing the accuracy of multi parametric magnetic resonance (mp-MRI) after application of PI-RADS V2 for diagnosis of prostate cancer as comparison to pathological results of trans rectal ultra-sound (TRUS) guided biopsy.

Patients and methods

138 prostatic lesions in 23 patients were retrospectively assessed and analyzed with Trans rectal ultra-sound (TRUS) guided biopsy results. Those patients underwent multi parametric magnetic resonance (mp-MRI) with application of PI-RADS V2 reporting system. The sensitivity, specificity, validity, negative predictive value and positive predictive value were calculated for PI-RADS V2 reporting system compared to biopsy-proven pathological results.

Results

92 out of 138 lesions were positive for Peripheral zone cancer prostate. PI-RADS V2 reporting system proved 88.04% sensitive & 93.4% specific for diagnosis of prostate cancer with negative predictive value & positive predictive value of 100%.

Conclusion

Our results proved that mp-MRI of prostate using PI-RADS v2 scoring system had high sensitivity and specificity in diagnosis of prostate cancer and PI-RADS V2 scoring system using mp-MRI is recommended as a non-invasive diagnostic tool compared to TRUS guided biopsy.