What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel

Added value of randomised biopsy to multiparametric magnetic resonance imaging-targeted biopsy of the prostate in a contemporary cohort

OBJECTIVE

To assess the added value of concurrent systematic randomised ultrasonography-guided biopsy (SBx) to multiparametric magnetic resonance imaging (mpMRI)-targeted biopsy and the additional rate of overdiagnosis of clinically insignificant prostate cancer (ciPCa) by SBx in a large contemporary, real-world cohort.

PATIENTS AND METHODS

A total of 1552 patients with positive mpMRI and consecutive mpMRI-targeted biopsy and SBx were enrolled. Added value and the rate of overdiagnosis by SBx was evaluated.

PRIMARY OUTCOME

added value of SBx, defined as detection rate of clinically significant PCa (csPCa; International Society of Urological Pathology [ISUP] Grade ≥2) by SBx, while mpMRI-targeted biopsy was negative or showed ciPCa (ISUP Grade 1).

SECONDARY OUTCOME

rate of overdiagnosis by SBx, defined as detection of ciPCa in patients with negative mpMRI-targeted biopsy and PSA level of <10 ng/mL.

RESULTS

Detection rate of csPCa by mpMRI-targeted biopsy and/or SBx was 753/1552 (49%). Added value of SBx was 145/944 (15%). Rate of overdiagnosis by SBx was 146/656 (22%). Added value of SBx did not change when comparing patients with previous prostate biopsy and biopsy naïve patients. In multivariable analysis, a Prostate Imaging-Reporting and Data System (PI-RADS) 4 index lesion (odds ratio [OR] 3.19, 95% confidence interval [CI] 1.66-6.78; P = 0.001), a PI-RADS 5 index lesion (OR 2.89, 95% CI 1.39-6.46; P = 0.006) and age (OR 1.05, 95% CI 1.03-1.08; P < 0.001) were independently associated with added value of SBx.

CONCLUSIONS

In our real-world analysis, we saw a significant impact on added value and added rate of overdiagnosis by SBx. Subgroup analysis showed no significant decrease of added value in any evaluated risk group. Therefore, we do not endorse omitting concurrent SBx to mpMRI-guided biopsy of the prostate.

Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology

BACKGROUND

Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration.

PURPOSE

This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration.

METHODS

In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D2 and D3) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D1. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D2 and D3, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N = 200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM.

RESULTS

Our results suggest that MSERgSDM performed comparably to the ground truth (p > 0.05). Additionally, MSERgSDM (ROI Dice ratio = 0.61, landmark distance = 3.26 mm) exhibited significant improvement over MSERg (ROI Dice ratio = 0.59, landmark distance = 3.69 mm) and ProsRegNet (ROI Dice ratio = 0.56, landmark distance = 4.00 mm) in local alignment.

CONCLUSIONS

This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.

Prediction of clinically significant prostate cancer by [68 Ga]Ga-PSMA-11 PET/CT: a potential tool for selecting patients for active surveillance

PURPOSE

In our study, our aim was to investigate the role of [68 Ga]Ga-PSMA-11 PET /CT imaging in the diagnosis of clinically significant prostate cancer (csPCa) (ISUP GG 2 and higher) in patients initially diagnosed with ISUP GG 1 and 2 after prostate biopsy.

MATERIALS AND METHODS

We retrospectively reviewed 147 patient records in whom [68 Ga]Ga-PSMA-11 PET/CT imaging was performed preoperatively. All patients were initially diagnosed with ISUP GG 1 and 2 PCa by biopsy. Final pathology reports were obtained after radical prostatectomy. The [68 Ga]Ga-PSMA-11 PET/CT images were evaluated to determine the PRIMARY score. Patients' mpMRI-PIRADS scores were also recorded when available and analyzed in correlation with the pathology results.

RESULTS

For the 114 patients scored using PRIMARY, 19 out of 37 patients with scores of 1 and 2 (51%) were diagnosed with csPCa. Of the 77 patients with PRIMARY scores between 3 and 5, 64 (83%) had csPCa. Notably, every patient with a PRIMARY score of 5 had csPCa. PRIMARY scoring had a sensitivity of 77% and specificity of 58%, with a positive predictive value of 83%. A moderate correlation was observed between PRIMARY scores and ISUP GG (Rho = 0.54, p < 0.001). In contrast, the PIRADS score displayed a sensitivity and specificity of 86% and 25% respectively, with a positive predictive value of 68%. No substantial correlation was found between PIRADS and ISUP GG. Statistical analysis revealed a significant correlation between PRIMARY and ISUP GG (p < 0.001), but not between PIRADS and ISUP GG (p = 0.281). Comparatively, PRIMARY scoring was significantly more reliable than PIRADS scoring in identifying csPCa.

CONCLUSION

[68 Ga]Ga-PSMA-11 PET/CT imaging is promising for distinguishing high-risk prostate cancer patients from those apt for active surveillance, potentially aiding in the identification of csPCa.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS  = -0.21, p = 0.001), Decipher (rS  = -0.193, p = 0.003), and three-tier risk (rS  = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

Role of psa levels and pathological stadiation before radiation therapy in predicting mp-MRI results in patients with prostate cancer recurrence after radical prostatectomy

OBJECTIVE

To evaluate PSA value in mp-MRI results prediction, analyzing patients with high (GS≥8, pT≥3, pN1) and low grade (GS<8, pT<3, pN0) Prostate Cancer (PCa).

MATERIALS AND METHODS

One hundred eighty-eight patients underwent 1.5-Tmp-MRI after Radical Prostatectomy (RP) and before Radiotherapy (RT). They were divided into 2 groups: A and B, for patients with biochemical recurrence (BCR) and without BCR but with high local recurrence risk. Considering Gleason Score (GS), pT and pN as independent grouping variables, ROC analyses of PSA levels at primary PCa diagnosis and PSA before RT were performed in order to identify the optimal cut-off to predict mp-MRI result.

RESULTS

Group A and B showed higher AUC for PSA before RT than PSA at PCa diagnosis, in low and high grade tumors. For low grade tumors the best AUC was 0.646 and 0.685 in group A and B; for high grade the best AUC was 0.705 and 1 in group A and B, respectively. For low grade tumors the best PSA cut-off was 0.565-0.58ng/mL in group A (sensitivity, specificity: 70.5%, 66%), and 0.11-0.13ng/mL in B (sensitivity, specificity: 62.5%, 84.6%). For high grade tumors, the best PSA cut-off obtained was 0.265-0.305ng/mL in group A (sensitivity, specificity: 95%, 42.1%), and 0.13-0.15ng/mL in B (sensitivity, specificity: 100%).

CONCLUSION

Mp-MRI should be performed as added diagnostic tool always when a BCR is detected, especially in high grade PCa. In patients without BCR, mp-MRI results, although poorly related to pathological stadiation, still have a good diagnostic performance, mostly when PSA>0.1-0.15ng/mL.

Opportunistic Prostate Cancer Screening with Biparametric Magnetic Resonance Imaging (VISIONING)

BACKGROUND

This study investigates the use of biparametric magnetic resonance imaging (bpMRI) as primary opportunistic screening for prostate cancer (PCa) without using a prostate-specific antigen (PSA) cut-off.

OBJECTIVE

The primary endpoint was to assess the efforts and effectiveness of identifying 20 participants with clinically significant prostate cancer (csPCa) using bpMRI.

DESIGN, SETTING, AND PARTICIPANTS

Biopsy-naïve men aged over 45 yr were included. All participants underwent 3 Tesla bpMRI, PSA, and digital rectal examination (DRE). Targeted-only biopsy was performed in participants with Prostate Imaging Reporting and Data System (PI-RADS) ≥3. Men with negative bpMRI but suspicious DRE or elevated PSA/PSA density had template biopsies. Preintended protocol adjustments were made after an interim analysis for PI-RADS 3 lesions: no biopsy and follow-up MRI after 6 mo and biopsy only if lesions persisted or upgraded.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Biopsy results underwent a comparison using Fisher's exact test and univariable logistic regression to identify prognostic factors for positive biopsy.

RESULTS AND LIMITATIONS

A total of 229 men were enrolled in this study, of whom 79 underwent biopsy. Among these men, 77 displayed suspicious PI-RADS lesions. PCa was detected in 29 participants (12.7%), of whom 21 had csPCa (9.2%). Biparametric MRI detected 21 csPCa cases, while PSA and DRE would have missed 38.1%. Protocol adjustment led to a 54.6% biopsy reduction in PI-RADS 3 lesions. Overall, in this cohort of men with a median PSA value of 1.26 ng/ml, 10.9 bpMRI scans were needed to identify one participant with csPCa. A major limitation of the study is the lack of a control cohort undergoing systematic biopsies.

CONCLUSIONS

Opportunistic screening utilising bpMRI as a primary tool has higher sensitivity in detecting csPCa than classical screening methods.

PATIENT SUMMARY

Screening with biparametric magnetic resonance imaging (bpMRI) and targeted biopsy identified clinically significant prostate cancer in every 11th man, regardless of the prostate-specific antigen (PSA) levels. Preselecting patients based on PSA >1 ng/ml and a positive family history of prostate cancer, as well as other potential blood tests may further improve the effectiveness of bpMRI in this setting.

Clinical significance of prostate cancer identified by transperineal standard template biopsy in men with nonsuspicious multiparametric magnetic resonance imaging

OBJECTIVE

Multiparametric magnetic resonance imaging (mpMRI) of the prostate has excellent sensitivity in detecting clinically significant prostate cancer (csCaP). However, whether a negative mpMRI in patients with a clinical suspicion of CaP can omit a confirmatory biopsy remains less understood and without consensus. Transperineal (TP) standard template biopsy (SBx) provides an effective approach to CaP detection. Our aim is to provide a comprehensive understanding of the CaP characteristics detected through TP SBx that are systematically overlooked by mpMRI.

METHODS

We conducted a retrospective analysis of all men who underwent prebiopsy mpMRI and subsequent a 20-core TP SBx at our hospital from September 2019 to February 2021. Patients with suspicious mpMRI received a combined TP SBx and targeted biopsy (TBx) (suspicious group), while those without suspicious (negative) mpMRI and who proceeded to biopsy, received TP SBx only (nonsuspicious group). A negative mpMRI was defined as the absence of suspicious findings and/or the presence of low-risk areas with a PI-RADS score of ≤2. Subsequently, we compared and evaluated the clinical and biopsy characteristics between these 2 groups.

RESULTS

We identified 301 men in suspicious group and 215 men in nonsuspicious group. The overall CaP detection rate and csCaP detection rate by TP SBx were 74.1%, 38.9% for suspicious group and 43.3%, 14.9% for nonsuspicious group, respectively. csCaP NPV of mpMRI was 85.1% with a csCaP prevalence 28.9%. The greatest percentage of cancer involvement (GPC) in biopsy core from nonsuspicious group was significantly lower than those of suspicious group (40% vs. 50%, p = 0.005), In multivariate logistic analysis, only PSAD > 0.15 ng/ml/cc was identified as an independent and significant predictor of csCaP in nonsuspicious group.

CONCLUSION

Within our cohort, false-negative rates of mpMRI for csCaP are substantial, reaching 15%. Nonsuspicious cases may contain a large volume tumor since the high GPC of SBx. For cases with nonsuspicious imaging and higher PSAD, a confirmatory biopsy may be necessary due to the increased risk of missed csCaP by mpMRI.

Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions

The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.

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.

Prostate Cancers Invisible on Multiparametric MRI: Pathologic Features in Correlation with Whole-Mount Prostatectomy

We investigated why some prostate cancers (PCas) are not identified on multiparametric MRI (mpMRI) by using ground truth reference from whole-mount prostatectomy specimens. A total of 61 patients with biopsy-confirmed PCa underwent 3T mpMRI followed by prostatectomy. Lesions visible on MRI prospectively or retrospectively identified after correlating with histology were considered "identified cancers" (ICs). Lesions that could not be identified on mpMRI were considered "unidentified cancers" (UCs). Pathologists marked the Gleason score, stage, size, and density of the cancer glands and performed quantitative histology to calculate the tissue composition. Out of 115 cancers, 19 were unidentified on MRI. The UCs were significantly smaller and had lower Gleason scores and clinical stage lesions compared with the ICs. The UCs had significantly (p < 0.05) higher ADC (1.34 ± 0.38 vs. 1.02 ± 0.30 μm2/ms) and T2 (117.0 ± 31.1 vs. 97.1 ± 25.1 ms) compared with the ICs. The density of the cancer glands was significantly (p = 0.04) lower in the UCs. The percentage of the Gleason 4 component in Gleason 3 + 4 lesions was nominally (p = 0.15) higher in the ICs (20 ± 12%) compared with the UCs (15 ± 8%). The UCs had a significantly lower epithelium (32.9 ± 21.5 vs. 47.6 ± 13.1%, p = 0.034) and higher lumen volume (20.4 ± 10.0 vs. 13.3 ± 4.1%, p = 0.021) compared with the ICs. Independent from size and Gleason score, the tissue composition differences, specifically, the higher lumen and lower epithelium in UCs, can explain why some of the prostate cancers cannot be identified on mpMRI.

Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images: evaluation in a cohort of patients on active surveillance

PURPOSE

To assess a region-of-interest-based computer-assisted diagnosis system (CAD) in characterizing aggressive prostate cancer on magnetic resonance imaging (MRI) from patients under active surveillance (AS).

METHODS

A prospective biopsy database was retrospectively searched for patients under AS who underwent MRI and subsequent biopsy at our institution. MRI lesions targeted at baseline biopsy were retrospectively delineated to calculate the CAD score that was compared to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score assigned at baseline biopsy.

RESULTS

186 patients were selected. At baseline biopsy, 51 and 15 patients had International Society of Urological Pathology (ISUP) grade ≥ 2 and ≥ 3 cancer respectively. The CAD score had significantly higher specificity for ISUP ≥ 2 cancers (60% [95% confidence interval (CI): 51-68]) than the PI-RADS score (≥ 3 dichotomization: 24% [CI: 17-33], p = 0.0003; ≥ 4 dichotomization: 32% [CI: 24-40], p = 0.0003). It had significantly lower sensitivity than the PI-RADS ≥ 3 dichotomization (85% [CI: 74-92] versus 98% [CI: 91-100], p = 0.015) but not than the PI-RADS ≥ 4 dichotomization (94% [CI:85-98], p = 0.104). Combining CAD findings and PSA density could have avoided 47/184 (26%) baseline biopsies, while missing 3/51 (6%) ISUP 2 and no ISUP ≥ 3 cancers. Patients with baseline negative CAD findings and PSAd < 0.15 ng/mL2 who stayed on AS after baseline biopsy had a 9% (4/44) risk of being diagnosed with ISUP ≥ 2 cancer during a median follow-up of 41 months, as opposed to 24% (18/74) for the others.

CONCLUSION

The CAD could help define AS patients with low risk of aggressive cancer at baseline assessment and during subsequent follow-up.

Patient-related characteristics predict prostate cancers in men with PI-RADS 4-5 to further optimize the diagnostic performance of MRI

PURPOSE

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI.

MATERIALS AND METHODS

The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated.

RESULTS

A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%.

CONCLUSION

The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.

Clinical Application of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation Score for Reporting Magnetic Resonance Imaging in Men on Active Surveillance for Prostate Cancer

Background

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) score has been developed to standardise prostate magnetic resonance imaging (MRI) reporting in men on active surveillance (AS) for prostate cancer (PCa).

Objective

To evaluate the feasibility of PRECISE scoring and assess its diagnostic accuracy.

Design setting and participants

All PCa patients on AS with a baseline MRI and at least one follow-up MRI scan between January 2008 and September 2022 at a single tertiary referral centre were included in a database. The follow-up protocol of the Prostate Cancer International Active Surveillance (PRIAS) study was used. All scans were retrospectively re-reported by a dedicated uroradiologist and appointed a Prostate Imaging Reporting and Data System (version 2.1) and PRECISE score.

Outcome measurements and statistical analysis

Clinically significant progression was defined by histopathological upgrading (on biopsy or radical prostatectomy) to grade group ≥3 and/or evolution to T3 stage. A survival analysis was performed to assess differential progression-free survival (PFS) according to the PRECISE score.

Results and limitations

A total of 188 patients were included for an analysis with a total of 358 repeat MRI scans and 144 repeat biopsies. The median follow-up was 46 mo (interquartile range 21-74). Radiological progression (PRECISE 4-5) had sensitivity, specificity, negative predictive value, and positive predictive value of, respectively, 78%, 70%, 90%, and 49% for clinically significant progression. Four-year PFS was 91% for PRECISE 1-3 versus 66% for PRECISE 4-5 (p < 0.001). In total, 137 patients underwent a confirmation MRI scan within 18 mo after diagnosis. Four-year PFS in this group was 81% for PRECISE 1-3 versus 43% for PRECISE 4-5 (p < 0.001). Limitations include retrospective design and no strict adherence to AS protocol.

Conclusions

Implementation of PRECISE scoring for PCa patients on AS is feasible and offers a prognostic value. Patients with PRECISE score 4-5 on confirmation MRI within 18 mo after diagnosis have a three-fold higher risk of clinically significant progression after 4 yr.

Patient summary

Patients with low-risk prostate cancer can be followed up carefully. In this study, we evaluate the standardised reporting of repeat magnetic resonance imaging scans (using the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation [PRECISE] recommendations). PRECISE scoring is feasible and helps identify patients in need of further treatment.

A multi-institutional study of 1,111 men with 4K score, multiparametric magnetic resonance imaging, and prostate biopsy

OBJECTIVE

Prostate magnetic resonance imaging (MRI) and biomarkers are often used in conjunction to enhance the selection process for prostate biopsy. However, the optimal sequence of ordering these tests has not been established. A comprehensive evaluation was conducted on a large multi-institutional cohort of patients who underwent MRI, 4K score, and biopsy of the prostate to examine the impact of utilizing both tests vs. either test alone and to determine if the order in which these tests are administered affects the ability to detect clinically significant prostate cancer (csCaP).

METHODS AND MATERIALS

We evaluated men from 8 different institutions who were referred for prostate cancer evaluation and underwent MRI, 4K score test, and prostate biopsy. The primary outcome was the presence of csCaP, defined as grade group 2 or higher cancer on a biopsy of the prostate. We used logistic regression, calibration plots, and decision curve analysis to evaluate using a 4K score or MRI alone vs. both tests together for detecting csCaP. In addition, we evaluated several strategies using one or both tests for selecting men for biopsy and compared them based on the proportion of biopsies avoided and the csCaP's missed.

RESULTS

Among the 1,111 men who formed the final cohort, 553 (49.8%) had prostate cancer, and 353 (31.8%) had csCaP. We found that using MRI and 4K score together had better discrimination, calibration, and a higher clinical utility on decision curve analysis compared to using either test individually. Using both tests together resulted in fewer biopsies avoided and missed cancers compared to using either test alone. Strategies that sequence MRI and 4K score tests resulted in the largest biopsy reduction, with no appreciable difference between starting with an MRI vs. a biomarker.

CONCLUSIONS

We found that using both an MRI and 4K score together was superior to using either test alone but found no appreciable difference between starting with an MRI vs. starting with a 4K score. Prospective studies are needed to identify the best strategy to sequence MRI and biomarkers in the evaluation of csCaP.

Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI

BACKGROUND AND OBJECTIVE

Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging.

METHODS

This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism.

RESULTS

The results of our proposed network's prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively.

CONCLUSIONS

Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance.

Prostate MRI and PSMA-PET in the Primary Diagnosis of Prostate Cancer

Over the last years, prostate magnetic resonance imaging (MRI) has gained a key role in the primary diagnosis of clinically significant prostate cancer (csPCa). While a negative MRI can avoid unnecessary prostate biopsies and the overdiagnosis of indolent cancers, a positive examination triggers biopsy samples targeted to suspicious imaging findings, thus increasing the diagnosis of csPCa with a sensitivity and negative predictive value of around 90%. The limitations of MRI, including suboptimal positive predictive values, are fueling debate on how to stratify biopsy decisions and management based on patient risk and how to correctly estimate it with clinical and/or imaging findings. In this setting, "next-generation imaging" imaging based on radiolabeled Prostate-Specific Membrane Antigen (PSMA)-Positron Emission Tomography (PET) is expanding its indications both in the setting of primary staging (intermediate-to-high risk patients) and primary diagnosis (e.g., increasing the sensitivity of MRI or acting as a problem-solving tool for indeterminate MRI cases). This review summarizes the current main evidence on the role of prostate MRI and PSMA-PET as tools for the primary diagnosis of csPCa, and the different possible interaction pathways in this setting.

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

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

Re-evaluating the diagnostic efficacy of PSA as a referral test to detect clinically significant prostate cancer in contemporary MRI-based image-guided biopsy pathways

Introduction

Modern image-guided biopsy pathways at diagnostic centres have greatly refined the investigations of men referred with suspected prostate cancer. However, the referral criteria from primary care are still based on historical prostate-specific antigen (PSA) cut-offs and age-referenced thresholds. Here, we tested whether better contemporary pathways and biopsy methods had improved the predictive utility value of PSA referral thresholds.

Methods

PSA referral thresholds, age-referenced ranges and PSA density (PSAd) were assessed for positive predictive value (PPV) in detection of clinically significant prostate cancer (csPCa - histological ⩾ Grade Group 2). Data were analysed from men referred to three diagnostics centres who used multi-parametric magnetic resonance imaging (mpMRI)-guided prostate biopsies for disease characterisation. Findings were validated in a separate multicentre cohort. Results: Data from 2767 men were included in this study. The median age, PSA and PSAd were 66.4 years, 7.3 ng/mL and 0.1 ng/mL2, respectively. Biopsy detected csPCa was found in 38.7%. The overall area under the curve (AUC) for PSA was 0.68 which is similar to historical performance. A PSA threshold of ⩾ 3 ng/mL had a PPV of 40.3%, but this was age dependent (PPV: 24.8%, 32.7% and 56.8% in men 50-59 years, 60-69 years and ⩾ 70 years, respectively). Different PSA cut-offs and age-reference ranges failed to demonstrate better performance. PSAd demonstrated improved AUC (0.78 vs 0.68, p < 0.0001) and improved PPV compared to PSA. A PSAd of ⩾ 0.10 had a PPV of 48.2% and similar negative predictive value (NPV) to PSA ⩾ 3 ng/mL and out-performed PSA age-reference ranges. This improved performance was recapitulated in a separate multi-centre cohort (n = 541).

Conclusion

The introduction of MRI-based image-guided biopsy pathways does not appear to have altered PSA diagnostic test characteristics to positively detect csPCa. We find no added value to PSA age-referenced ranges, while PSAd offers better PPV and the potential for a single clinically useful threshold (⩾0.10) for all age groups.

Level of evidence

IV.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

Contemporary Review of Multimodality Imaging of the Prostate Gland

Tissue changes and the enlargement of the prostate, whether benign or malignant, are among the most common groups of diseases that affect men and can have significant impacts on length and quality of life. The prevalence of benign prostatic hyperplasia (BPH) increases significantly with age and affects nearly all men as they grow older. Other than skin cancers, prostate cancer is the most common cancer among men in the United States. Imaging is an essential component in the diagnosis and management of these conditions. Multiple modalities are available for prostate imaging, including several novel imaging modalities that have changed the landscape of prostate imaging in recent years. This review will cover the data relating to commonly used standard-of-care prostate imaging modalities, advances in newer technologies, and newer standards that impact prostate gland imaging.

MRI-derived tumor volume as a predictor of biochemical recurrence and adverse pathology in patients after radical prostatectomy: a propensity score matching study

PURPOSE

To investigate the predictive value of MRI-derived tumor volume (TV) of biochemical recurrence (BCR) and adverse pathology (AP) in patients following radical prostatectomy (RP).

METHODS

The data of 565 patients receiving RP in a single institution between 2010 and 2021 were retrospectively analyzed. All suspicious tumor foci were delineated manually using ITK-SNAP software as the regions of interest (ROIs). The sum of the TV of all lesions was calculated automatically based on the voxel in the ROIs to acquire the final TV parameter. TV was categorized as low-volume (≤ 6.5 cm³) and high-volume (> 6.5 cm³) based on the cut-off value. Univariate and multivariate Cox and logistic regression analyses were performed to identify independent predictors of BCR and AP. The Kaplan-Meier with the log-rank test was conducted to compare the BCR-free survival (BFS) between the low and high-volume groups.

RESULTS

All the included patients were divided into the low-volume group (n = 337) and the high-volume group (n = 228). The TV was an independent predictor of BFS in the multivariate Cox regression analysis (Hazard Ratio (HR) [95% CI]: 1.550 [1.066-2.256], P = 0.022). The Kaplan-Meier analysis demonstrated that low volume was associated with a better BFS than high volume before propensity score matching (PSM) (P < 0.001). One hundred and fifty-eight pairs were obtained by 1:1 PSM to balance the baseline parameters between the two groups. After the PSM, low-volume remained to be associated with a better BFS than high-volume (P = 0.006). TV as a categorical variable was an independent factor of AP in multivariate logistic regression analysis (Odd ratio (OR) [95% CI]: 1.821 [1.064-3.115], P = 0.029). After balancing the potential factors influencing AP by 1:1 PSM, 162 new pairs were identified. The high-volume group had a higher AP rate than the low-volume group after PSM (75.9 vs. 64.8%, P = 0.029).

CONCLUSION

We adopted a novel approach to acquiring the TV on preoperative MRI. TV was significantly associated with BFS and AP of patients undergoing RP, which was further illustrated by PSM analysis. MRI-derived TV may serve as a predictive marker for assessing BFS and AP in further studies, which will facilitate clinical decision-making and patient counseling.

ACR Appropriateness Criteria® Pretreatment Detection, Surveillance, and Staging of Prostate Cancer: 2022 Update

Prostate cancer is second leading cause of death from malignancy after lung cancer in American men. The primary goal during pretreatment evaluation of prostate cancer is disease detection, localization, establishing disease extent (both local and distant), and evaluating aggressiveness, which are the driving factors of patient outcomes such as recurrence and survival. Prostate cancer is typically diagnosed after the recognizing elevated serum prostate-specific antigen level or abnormal digital rectal examination. Tissue diagnosis is obtained by transrectal ultrasound-guided biopsy or MRI-targeted biopsy, commonly with multiparametric MRI without or with intravenous contrast, which has recently been established as standard of care for detecting, localizing, and assessing local extent of prostate cancer. Although bone scintigraphy and CT are still typically used to detect bone and nodal metastases in patients with intermediate- or high-risk prostate cancer, novel advanced imaging modalities including prostatespecific membrane antigen PET/CT and whole-body MRI are being more frequently utilized for this purpose with improved detection rates. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Single-center, prospective phase 2 trial of high-intensity focused ultrasound (HIFU) in patients with unilateral localized prostate cancer: good functional results but oncologically not as safe as expected

PURPOSE

Focal therapy (FT) for localized prostate cancer (PCa) is only recommended within the context of clinical trials by international guidelines. We aimed to investigate oncological follow-up and safety data of focal high-intensity focused ultrasound (HIFU) treatment.

METHODS

We conducted a single-center prospective study of 29 patients with PCa treated with (focal) HIFU between 2016 and 2021. Inclusion criteria were unilateral PCa detected by mpMRI-US-fusion prostate biopsy and maximum prostate specific antigen (PSA) of 15 ng/ml. Follow-up included mpMRI-US fusion-re-biopsies 12 and 24 months after HIFU. No re-treatment of HIFU was allowed. The primary endpoint was failure-free survival (FFS), defined as freedom from intervention due to cancer progression.

RESULTS

Median follow-up of all patients was 23 months, median age was 67 years and median preoperative PSA was 6.8 ng/ml. One year after HIFU treatment PCa was still detected in 13/ 29 patients histologically (44.8%). Two years after HIFU another 7/29 patients (24.1%) were diagnosed with PCa. Until now, PCa recurrence was detected in 11/29 patients (37.93%) which represents an FFS rate of 62%.One patient developed local metastatic disease 2 years after focal HIFU. Adverse events (AE) were low with 70% of patients remaining with sufficient erectile function for intercourse and 97% reporting full maintenance of urinary continence.

CONCLUSION

HIFU treatment in carefully selected patients is feasible. However, HIFU was oncologically not as safe as expected because of progression rates of 37.93% and risk of progression towards metastatic disease. Thus, we stopped usage of HIFU in our department.

Impact of changing PI-RADS cutoff on prostate cancer detection by MRI cognitive fusion biopsy in biopsy-naïve patients

BACKGROUND

Multi-parametric magnetic resonance imaging may improve the detection of prostate cancer. The aim of this work is to compare between PI-RADS 3-5 and PI-RADS 4-5 as a threshold for targeted prostatic biopsy.

METHODS

This is a prospective clinical study that included 40 biopsy-naïve patients referred for prostate biopsy. Patients underwent prebiopsy multi-parametric (mp-MRI), followed by 12-core transrectal ultrasound-guided systematic biopsy and cognitive MRI/TRUS fusion targeted biopsy from each detected lesion. The primary endpoint was to assess the diagnostic accuracy of the PI-RAD 3-4 versus PI-RADS 4-5 lesion by mpMRI for prostate cancer detection in biopsy-naive men.

RESULTS

The overall prostate cancer detection rate and the clinically significant cancer detection rate were 42.5% and 35%, respectively. Targeted biopsies from PI-RADS 3-5 lesions showed a sensitivity of 100%, specificity of 44%, positive predictive value of 51.7%, and negative predictive value of 100%. Restricting targeted biopsies to PI-RADS 4-5 lesions resulted in a decrease in sensitivity and negative predictive value to 73.3% and 86.2%, respectively, while specificity and positive predictive value were increased to 100% for both parameters which was statistically significant (P value < 0.0001 and P value = 0.004, respectively).

CONCLUSIONS

Limiting the TBs to PI-RADS 4-5 lesions improves the performance of mp-MRI in the detection of prostate cancer especially aggressive tumors.

Determination of Whether Apex or Non-Apex Prostate Cancer Is the Best Candidate for the Use of Prostate-Specific Antigen Density to Predict Pathological Grade Group Upgrading and Upstaging after Radical Prostatectomy

Objective: Previous studies have demonstrated that prostate-specific antigen density (PSAD) may aid in predicting Gleason grade group (GG) upgrading and pathological upstaging in patients with prostate cancer (PCa). However, the differences and associations between patients with apex prostate cancer (APCa) and non-apex prostate cancer (NAPCa) have not been described. The aim of this study was to explore the different roles of PSAD in predicting GG upgrading and pathological upstaging between APCa and NAPCa. Patients and Methods: Five hundred and thirty-five patients who underwent prostate biopsy followed by radical prostatectomy (RP) were enrolled. All patients were diagnosed with PCa and classified as either APCa or NAPCa. Clinical and pathological variables were collected. Univariate, multivariate, and receiver operating characteristic (ROC) analyses were performed. Results: Of the entire cohort, 245 patients (45.8%) had GG upgrading. Multivariate analysis revealed that only PSAD (odds ratio [OR]: 4.149, p < 0.001) was an independent, significant predictor of upgrading. A total of 262 patients (49.0%) had pathological upstaging. Both PSAD (OR: 4.750, p < 0.001) and percentage of positive cores (OR: 5.108, p = 0.002) were independently significant predictors of upstaging. Of the 374 patients with NAPCa, 168 (44.9%) displayed GG upgrading. Multivariate analysis also showed PSAD (OR: 8.176, p < 0.001) was an independent predictor of upgrading. Upstaging occurred in 159 (42.5%) patients with NAPCa, and PSAD (OR: 4.973, p < 0.001) and percentage of positive cores (OR: 3.994, p = 0.034) were independently predictive of pathological upstaging. Conversely, of the 161 patients with APCa, 77 (47.8%) were identified with GG upgrading, and 103 (64.0%) patients with pathological upstaging. Multivariate analysis demonstrated that there were no significant predictors, including PSAD, for predicting GG upgrading (p = 0.462) and pathological upstaging (p = 0.100). Conclusions: PSAD may aid in the prediction of GG upgrading and pathological upstaging in patients with PCa. However, this may only be practical in patients with NAPCa but not with APCa. Additional biopsy cores taken from the prostatic apex region may help improve the accuracy of PSAD in predicting GG upgrading and pathological upstaging after RP.

Abbreviated Versus Multiparametric Prostate MRI in Active Surveillance for Prostate-Cancer Patients: Comparison of Accuracy and Clinical Utility as a Decisional Tool

(1) Purpose: To compare the diagnostic accuracy between full multiparametric contrast-enhanced prostate MRI (mpMRI) and abbreviated dual-sequence prostate MRI (dsMRI) in men with clinically significant prostate cancer (csPCa) who were candidates for active surveillance. (2) Materials and Methods: Fifty-four patients with a diagnosis of low-risk PCa in the previous 6 months had a mpMRI scan prior to a saturation biopsy and a subsequent MRI cognitive transperineal targeted biopsy (for PI-RADS ≥ 3 lesions). The dsMRI images were obtained from the mpMRI protocol. The images were selected by a study coordinator and assigned to two readers blinded to the biopsy results (R1 and R2). Inter-reader agreement for clinically significant cancer was evaluated with Cohen's kappa. The dsMRI and mpMRI accuracy was calculated for each reader (R1 and R2). The clinical utility of the dsMRI and mpMRI was investigated with a decision-analysis model. (3) Results: The dsMRI sensitivity and specificity were 83.3%, 31.0%, 75.0%, and 23.8%, respectively, for R1 and R2. The mpMRI sensitivity and specificity were 91.7%, 31.0%, 83.3%, and 23.8%, respectively, for R1 and R2. The inter-reader agreement for the detection of csPCa was moderate (k = 0.53) and good (k = 0.63) for dsMRI and mpMRI, respectively. The AUC values for the dsMRI were 0.77 and 0.62 for the R1 and R2, respectively. The AUC values for the mpMRI were 0.79 and 0.66 for R1 and R2, respectively. No AUC differences were found between the two MRI protocols. At any risk threshold, the mpMRI showed a higher net benefit than the dsMRI for both R1 and R2. (4) Conclusions: The dsMRI and mpMRI showed similar diagnostic accuracy for csPCa in male candidates for active surveillance.

A critical analysis of deficiencies in the quality of information contained in prostate multiparametric MRI requests and reports

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) has been increasingly recognised as an important tool in the diagnosis of prostate cancer. PI-RADSv2 guidelines recommend that important clinical information including prostate-specific antigen (PSA) levels, examination findings, and biopsy information should be included in mpMRI requests. PIRADS score and PSA density (PSAD) are both independent predictors for the presence of a clinically significant prostate cancer.

AIMS

This study aims to evaluate the quality of mpMRI requests and reports at our institution in accordance with these parameters.

METHODS

All prostate mpMRIs performed by radiology services in Galway University Hospital between 1st September 2019 and 1st March 2020 were reviewed. Exclusion criteria were applied. Requests and reports were analysed for the presence of the following parameters: PSA-results, examination findings, biopsy information, PI-RADS score, prostate volume, and PSAD.

RESULTS

A total of 586 mpMRIs were performed, and of these, 546 were included. PSA value was provided in 497 (91%) of requests, exam findings in 355 (65%), and biopsy information in 452 (82%). PIRADS score was included in 224 (41%) of reports, prostate volume in 178 (32.6%), and PSAD in 106 (19%).

CONCLUSIONS

Great variation in the quality of information contained in both requests and reports for prostate mpMRIs exists within our service. We aim to improve this by collaborating with our radiology colleagues to develop a proforma for requesting and reporting of mpMRIs for our radiology systems to ensure important clinical and radiological information is provided in future.

Should men undergo MRI before prostate biopsy - CON

Prostate magnetic resonance imaging (MRI) is increasingly used prior to biopsy in response to the overdiagnosis and overtreatment of prostate cancer (CaP) associated with prostate-specific antigen (PSA) based screening. However, technical limitations in the conventional diffusion-weighted imaging (DWI) sequences as well as the high degree of radiologist-to-radiologist variability in interpreting prostate MRI result in inadequate accuracy. Specifically, the insufficient negative predictive value (NPV) of prostate MRI (76%-87%) does not allow biopsy to be omitted in the negative MRI setting. Additionally, the variable, and relatively low positive predictive value (PPV) of MRI (27%-44%) provides only an incremental improvement in risk prediction compared to readily available clinical tools such as the Prostate Cancer Prevention Trial risk calculator. This small benefit is likely confined to the minority of patients with positive MRI findings in a typically under-sampled region of the prostate (e.g., anterior lesions), which may be obviated by newer biopsy approaches and tools such as transperineal prostate biopsy and micro-ultrasound technology. With these considerations in mind, pre-biopsy prostate MRI in its current form is unlikely to provide a clinically significant benefit, and should not be considered as routine practice until its accuracy is sufficiently improved.

Avoiding Unnecessary Systematic Biopsy in Clinically Significant Prostate Cancer: Comparison Between MRI-Based Radiomics Model and PI-RADS Category

BACKGROUND

MRI-targeted biopsy (MRTB) improves the clinically significant prostate cancer (csPCa) detection rate with fewer biopsy cores in men with suspected PCa. However, whether concurrent systematic biopsy (SB) can be avoided in patients undergoing MRTB remains unclear.

PURPOSE

To evaluate the potential value of MRI-based radiomics models in avoiding unnecessary SB in biopsy-naïve patients.

STUDY TYPE

Retrospective.

POPULATION

A total of 226 patients (mean age 66.6 ± 9.02 years) with suspicion of PCa (PI-RADS score ≥ 3) and received combined cognitive MRTB with SB were retrospectively recruited and randomly divided into training (N = 180) and test (N = 46) cohorts at an 8:2 ratio.

FIELD STRENGTH/SEQUENCE

A 3.0 T, biparametric MRI (bpMRI) including T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map.

ASSESSMENT

The whole prostate gland (PG) and the index lesion (IL) were delineated. Three radiomics models of bpMRI_PG , bpMRI_IL , and bpMRI_PG+IL were constructed, respectively, and the performance of each radiomics model was compared with that of PI-RADS assessment.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. The area under the curve (AUC) and decision curve analysis were used to estimate the models.

RESULTS

The bpMRI_PG+IL radiomics model exhibited good discrimination, calibration, and net benefits, which would reduce the SB biopsy in 71.2% and 71.4% of men with PI-RADS ≥ 5 lesions in the training and test cohorts, respectively.

DATA CONCLUSION

A bpMRI_PG+IL radiomics model may outperform PI-RADS category in help reducing unnecessary SB in biopsy-naïve patients.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 6.

Investigating PSMA-PET/CT to resolve prostate MRI PIRADS4-5 and negative biopsy discordance

BACKGROUND

To determine the utility of diagnostic 18F-DCPyL PSMA-PET/CT to aid management of men with highly suspicious multiparametric MRI prostate (PIRAD 4-5 lesions) and discrepant negative prostate biopsy.

METHODS

A multicentre prospective consecutive case series was conducted (2018-2021), recruiting men with prior mpMRI prostate PIRADS 4-5 lesions and negative prostate biopsy. All men had 18F-DCPyL PSMA-PET/CT with subsequent management based on the concordance between MRI and PET: (1) Concordant lesions were biopsied using in-bore MRI targeting; (2) PSMA-PET/CT avidity without MRI correlate were biopsied using cognitive/software targeting with ultrasound guidance and (3) Patients with negative PET/CT were returned to standard of care follow-up.

RESULTS

29 patients were recruited with 48% (n = 14) having concordant MRI/PET abnormalities. MRI targeted biopsy found prostate cancer in six patients, with grade groups GG3 (n = 1), GG2 (n = 1), GG1 (n = 4) found. Of the 20 men who PSMA-PET/CT avidity and biopsy, analysis showed higher SUVmax (20.1 vs 6.8, p = 0.036) predicted prostate cancer. Of patients who had PSMA-PET avidity without MRI correlate, and those with no PSMA-PET avidity, only one patient was subsequently found to have prostate cancer (GG1). The study is limited by small size and short follow-up of 17 months (IQR 12.5-29.9).

CONCLUSIONS

PSMA-PET/CT is useful in this group of men but requires further investigation. Avidity (higher SUVmax) that correlates to the mpMRI prostate lesion should be considered for targeted biopsy.

Automatic segmentation of prostate MRI based on 3D pyramid pooling Unet

PURPOSE

Automatic segmentation of prostate magnetic resonance (MR) images is crucial for the diagnosis, evaluation, and prognosis of prostate diseases (including prostate cancer). In recent years, the mainstream segmentation method for the prostate has been converted to convolutional neural networks. However, owing to the complexity of the tissue structure in MR images and the limitations of existing methods in spatial context modeling, the segmentation performance should be improved further.

METHODS

In this study, we proposed a novel 3D pyramid pool Unet that benefits from the pyramid pooling structure embedded in the skip connection (SC) and the deep supervision (DS) in the up-sampling of the 3D Unet. The parallel SC of the conventional 3D Unet network causes low-resolution information to be sent to the feature map repeatedly, resulting in blurred image features. To overcome the shortcomings of the conventional 3D Unet, we merge each decoder layer with the feature map of the same scale as the encoder and the smaller scale feature map of the pyramid pooling encoder. This SC combines the low-level details and high-level semantics at two different levels of feature maps. In addition, pyramid pooling performs multifaceted feature extraction on each image behind the convolutional layer, and DS learns hierarchical representations from comprehensive aggregated feature maps, which can improve the accuracy of the task.

RESULTS

Experiments on 3D prostate MR images of 78 patients demonstrated that our results were highly correlated with expert manual segmentation. The average relative volume difference and Dice similarity coefficient of the prostate volume area were 2.32% and 91.03%, respectively.

CONCLUSION

Quantitative experiments demonstrate that, compared with other methods, the results of our method are highly consistent with the expert manual segmentation.

The function of Prostate Health Index in detecting clinically significant prostate cancer in the PI-RADS 3 population: a multicenter prospective study

PURPOSE

The purpose of this study is to identify patients in the prostate imaging reporting and data system (PI-RADS) 3 population who need biopsy by using prostate health index (PHI) and other clinical parameters in order to avoid unnecessary biopsies.

METHODS

A total of 302 patients from four hospital were enrolled, and 92 patients with PI-RADS 3 were included finally. All patients were biopsy-naïve and had suspicion of prostate cancer (PCa) with PSA level in 4-20 ng/ml and a normal digital rectal exam. Univariable and stepwise forward multivariable logistic regression analyses were used to evaluated the risk factors. The sensitivity, specificity, and positive and negative predictive values of different cut-off value of PHI were calculated for the diagnosis of clinically significant prostate cancer (CSPCa).

RESULTS

The overall patient's mean age was 65.65 ± 9.55 years, median PSA was 7.68 (5.28-12.07) ng/ml and median PHI was 43.80 (33.09-64.69). PCa was identified in 32.61% (30/92) of PI-RADS 3 and CSPCa was identified in 28.26% (26/92) of PI-RADS 3. The risk factors for detecting PCa and CSPCa in multivariable regression analysis were age and PHI. When the biopsy was restricted to those PHI ≥ 43.5, 42.39% unnecessary biopsied could avoid. The sensitivity, specificity, positive predictive value and negative predictive value for the detection of CSPCa in the PHI ≥ 43.5 were 92.31%, 63.64%, 50% and 95.45% respectively.

CONCLUSION

The inclusion of PHI in the diagnosis of the PI-RADS 3 population may avoid many unnecessary biopsies. The multivariable models could increase the detection of cancer.

Do cancer detection rates differ between transperineal and transrectal micro-ultrasound mpMRI-fusion-targeted prostate biopsies? A propensity score-matched study

INTRODUCTION

High-resolution micro-ultrasound (micro-US) is a novel precise imaging modality that allows targeted prostate biopsies and multiparametric magnet resonance imaging (mpMRI) fusion. Its high resolution relying on a 29 MHz transducer allows real-time visualisation of prostate cancer lesions; this might overcome the inaccuracy of conventional MRI-US fusion biopsy strategies. We compared cancer detection rates in patients who underwent transrectal (TR-B) versus transperineal (TP-B) MR-micro-US fusion biopsy.

MATERIALS AND METHODS

1:2 propensity score matching was performed in 322 consecutive procedures: 56 TR-B and 266 TP-B. All prostate biopsies were performed using ExactVuTM micro-US system with mpMRI image fusion. Clinically significant disease was defined as grade group ≥2. The primary objective was to evaluate the detection of clinically significant disease according to access route. The secondary outcomes were to compare the respective detection rates of random and targeted biopsies stratified per access route and to evaluate micro-US for its potential added value.

RESULTS

47 men undergoing TR-B and 88 undergoing TP-B were matched for age, PSA, clinical stage, prostate volume, PIRADS score, number of mpMRI-visible lesions and indication to biopsy. The detection rates of clinically significant and of any prostate cancer did not differ between the two groups (45% TR-B vs 42% TP-B; p = 0.8, and 57% TR-B vs 59% TP-B; p = 0.9, respectively). Detection rates also did not differ significantly between random (p = 0.4) and targeted biopsies (p = 0.7) stratified per access route. Micro-US targeted biopsy detected 36 MRI-invisible lesions in 33 patients; 19% of these lesions were positive for clinically significant disease. Overall, micro-US targeted biopsies upgraded 2% of patients to clinically significant disease that would have been missed otherwise.

CONCLUSIONS

MR-micro-US-fusion TR-B and TP-B have similar diagnostic yields in terms of detection rates of clinically significant prostate cancer. Micro-US targeted biopsy appears to have an additional diagnostic value over systematic and MRI-targeted biopsies.

Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

Multiparametric MRI of the prostate is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer, with a negative MRI enabling safe avoidance of biopsy and a positive result enabling MRI-directed sampling of lesions. The diagnostic pathway consists of several steps, from initial patient presentation and preparation to performing and interpreting MRI, communicating the imaging findings, outlining the prostate and intra-prostatic target lesions, performing the biopsy and assessing the cores. Each component of this pathway requires experienced clinicians, optimized equipment, good inter-disciplinary communication between specialists, and standardized workflows in order to achieve the expected outcomes. Assessment of quality and mitigation measures are essential for the success of the MRI-directed prostate cancer diagnostic pathway. Quality assurance processes including Prostate Imaging-Reporting and Data System, template biopsy, and pathology guidelines help to minimize variation and ensure optimization of the diagnostic pathway. Quality control systems including the Prostate Imaging Quality scoring system, patient-level outcomes (such as Prostate Imaging-Reporting and Data System MRI score assignment and cancer detection rates), multidisciplinary meeting review and audits might also be used to provide consistency of outcomes and ensure that all the benefits of the MRI-directed pathway are achieved.

Diagnostic value of combining PI-RADS v2.1 with PSAD in clinically significant prostate cancer

PURPOSE

To investigate the diagnostic value of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CsPCa). We also aimed to combine PI-RADS v2.1 with prostate-specific antigen (PSA) derivatives to improve the predictive value of CsPCa.

METHODS

We retrospectively collected relevant data who underwent standard MRI examinations of the prostate and subjected to a prostate biopsy at Shenzhen People's hospital from November 2014 to November 2019. Included 125 cases of CsPCa and 383 cases of non-CsPCa. All cases were scored using the PI-RADS v2.1. The clinical data collected included age, PSA, free PSA/total PSA, prostate volume and PSA density (PSAD). A univariate analysis was performed to identify statistically significant indicators. Logistic regression was used to analyze the predictive value of the multi-parameter combination on CsPCa.

RESULTS

Except age, the difference in all of indicators between the CsPCa group and non-CsPCa group was statistically significant. The PI-RADS score and PSAD value had the highest diagnostic value. Logistic regression analysis revealed that the PI-RADS score and PSAD value were independent predictors of CsPCa, with a regression model AUC of 0.935. CsPCa detection rates were low when the PI-RADS score ≤ 2 or the PI-RADS score = 3 and the PSAD value ≤ 0.33 ng/ml/ml.

CONCLUSION

Combining the PI-RADS score and PSAD value improved the predictive performance of CsPCa. Patients with a PI-RADS score ≤ 2 or a PI-RADS score = 3 and a PSAD value ≤ 0.33 ng/ml/ml can avoid an unnecessary biopsy.

Combination of MRI prostate and 18F-DCFPyl PSMA PET/CT detects all clinically significant prostate cancers in treatment-naive patients: An international multicentre retrospective study

INTRODUCTION

Clinical and biochemical assessment and biopsies can miss clinically significant prostate cancers (csPCa) in up to 20% of patients and diagnose clinically insignificant tumours leading to overtreatment. This retrospective study analyses the accuracy of ¹⁸ F-DCFPyL PET/CT in detecting csPCa as a primary diagnostic tool and directly compares it with mpMRI prostate in treatment-naive patients. The two modalities are then correlated to determine whether they are better in combination, than either alone.

METHODS

This is a retrospective dual-institution study of patients who underwent contemporaneous MRI and PSMA-PET between January 2017 and March 2020 with histologic confirmation. The images were re-reviewed and concordance between modalities assessed. Results were compared with histopathology to determine the ability of MRI and PSMA-PET to detect csPCA.

RESULTS

MRI and PSMA-PET detected the same index lesion in 90.8% of cases with a kappa of 0.82. PET detected an additional 6.2% of index lesions which were MRI occult. MRI detected an additional 3.1% which were PET occult. No additional csPCa was identified on pathology which was not seen on imaging. The sensitivity of PSMA-PET in detecting csPCa is 96.7% and that of MRI is 93.4% with no statistically significant difference between the two (P = 0.232). Both modalities detected all four cases of non-csPCa with these being considered false positives.

CONCLUSION

Both mpMRI and 18F-DCFPyL-PSMA-PET/CT have high sensitivity for detecting csPCa with high agreement between modalities. There were no synchronous csPCa lesions detected on pathology that were not detected on imaging too.

Magnetic Resonance Imaging-Based Predictive Models for Clinically Significant Prostate Cancer: A Systematic Review

Simple Summary

Magnetic resonance imaging (MRI) has allowed the early detection of PCa to evolve towards clinically significant PCa (csPCa), decreasing unnecessary prostate biopsies and overdetection of insignificant tumours. MRI identifies suspicious lesions of csPCa, predicting the semi-quantitative risk through the prostate imaging report and data system (PI-RADS), and enables guided biopsies, increasing the sensitivity of csPCa. Predictive models that individualise the risk of csPCa have also evolved adding PI-RADS score (MRI-PMs), improving the selection of candidates for prostate biopsy beyond the PI-RADS category. During the last five years, many MRI-PMs have been developed. Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness through a systematic review. We have found high heterogeneity between MRI technique, PI-RADS versions, biopsy schemes and approaches, and csPCa definitions. MRI-PMs outperform the selection of candidates for prostate biopsy beyond MRI alone and PMs based on clinical predictors. However, few developed MRI-PMs are externally validated or have available risk calculators (RCs), which constitute the appropriate requirements used in routine clinical practice.

Abstract

MRI can identify suspicious lesions, providing the semi-quantitative risk of csPCa through the Prostate Imaging-Report and Data System (PI-RADS). Predictive models of clinical variables that individualise the risk of csPCa have been developed by adding PI-RADS score (MRI-PMs). Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness. A systematic review was performed after a literature search performed by two independent investigators in PubMed, Cochrane, and Web of Science databases, with the Medical Subjects Headings (MESH): predictive model, nomogram, risk model, magnetic resonance imaging, PI-RADS, prostate cancer, and prostate biopsy. This review was made following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria and studied eligibility based on the Participants, Intervention, Comparator, and Outcomes (PICO) strategy. Among 723 initial identified registers, 18 studies were finally selected. Warp analysis of selected studies was performed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical predictors in addition to the PI-RADS score in developed MRI-PMs were age, PCa family history, digital rectal examination, biopsy status (initial vs. repeat), ethnicity, serum PSA, prostate volume measured by MRI, or calculated PSA density. All MRI-PMs improved the prediction of csPCa made by clinical predictors or imaging alone and achieved most areas under the curve between 0.78 and 0.92. Among 18 developed MRI-PMs, 7 had any external validation, and two RCs were available. The updated PI-RADS version 2 was exclusively used in 11 MRI-PMs. The performance of MRI-PMs according to PI-RADS was only analysed in a single study. We conclude that MRI-PMs improve the selection of candidates for prostate biopsy beyond the PI-RADS category. However, few developed MRI-PMs meet the appropriate requirements in routine clinical practice.

Combining prostate-specific antigen density with prostate imaging reporting and data system score version 2.1 to improve detection of clinically significant prostate cancer: A retrospective study

Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of &lt;0.15 ng/mL2, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer.

Predicting Grade group 2 or higher cancer at prostate biopsy by 4Kscore in blood and uCaP microRNA model in urine

Elevated prostate-specific antigen (PSA) levels often lead to unnecessary and possibly harmful transrectal ultrasound guided biopsy, e.g. when the biopsy is negative or contains only low-grade insignificant cancer, unlikely to become symptomatic in the man's normal lifespan. A model based on four-kallikrein markers in blood (commercialized as 4Kscore) predicts risk of Grade group 2 or higher prostate cancer at biopsy, reducing unnecessary biopsies. We assessed whether these results extend to a single institution prostate biopsy cohort of Danish men and are enhanced by three microRNAs from urine (referred to as uCaP). The 4Kscore measured in cryopreserved blood from 234 men referred for 10+ core biopsy to Aarhus University Hospital, 29 with PSA > 25 ng/ml. We explored uCaP in urine from 157 of these men. Combined with age and DRE findings, both 4Kscore and uCaP could accurately predict Grade group 2 or higher prostate cancer (all patients: AUC = 0.802 and 0.797; PSA ≤ 25: AUC = 0.763 and 0.759). There was no additive effect when combining the 4Kscore and uCaP. Limitations include a study cohort with higher risk than commonly reported for biopsy cohorts. Our findings further support the clinical use of the 4Kscore to predict Grade group 2 or higher cancers in men being considered for biopsy.

Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study

Background: To evaluate the clinical utility of an Artificial Intelligence (AI) radiology solution, Quantib Prostate, for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Methods: Prostate mpMRI exams of 108 patients were retrospectively studied. The diagnostic performance of an expert radiologist (>8 years of experience) and of an inexperienced radiologist aided by Quantib software were compared. Three groups of patients were assessed: patients with positive mpMRI, positive target biopsy, and/or at least one positive random biopsy (group A, 73 patients); patients with positive mpMRI and a negative biopsy (group B, 14 patients), and patients with negative mpMRI who did not undergo biopsy (group-C, 21 patients). Results: In group A, the AI-assisted radiologist found new lesions with positive biopsy correlation, increasing the diagnostic PCa performance when compared with the expert radiologist, reaching an SE of 92.3% and a PPV of 90.1% (vs. 71.7% and 84.4%). In group A, the expert radiologist found 96 lesions on 73 mpMRI exams (17.7% PIRADS3, 56.3% PIRADS4, and 26% PIRADS5). The AI-assisted radiologist found 121 lesions (0.8% PIRADS3, 53.7% PIRADS4, and 45.5% PIRADS5). At biopsy, 33.9% of the lesions were ISUP1, 31.4% were ISUP2, 22% were ISUP3, 10.2% were ISUP4, and 2.5% were ISUP5. In group B, where biopsies were negative, the AI-assisted radiologist excluded three lesions but confirmed all the others. In group-C, the AI-assisted radiologist found 37 new lesions, most of them PIRADS 3, with 32.4% localized in the peripherical zone and 67.6% in the transition zone. Conclusions: Quantib software is a very sensitive tool to use specifically in high-risk patients (high PIRADS and high Gleason score).

Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI

Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.

Automatic prostate and peri-prostatic fat segmentation based on pyramid mechanism fusion network for T2-weighted MRI

BACKGROUND AND OBJECTIVE

Automatic and accurate segmentation of prostate and peri-prostatic fat in male pelvic MRI images is a critical step in the diagnosis and prognosis of prostate cancer. The boundary of prostate tissue is not clear, which makes the task of automatic segmentation very challenging. The main issues, especially for the peri-prostatic fat, which is being offered for the first time, are hazy boundaries and a large form variation.

METHODS

We propose a pyramid mechanism fusion network (PMF-Net) to learn global features and more comprehensive context information. In the proposed PMF-Net, we devised two pyramid techniques in particular. A pyramid mechanism module made of dilated convolutions of varying rates is inserted before each down sample of the fundamental network architecture encoder. The module is intended to address the issue of information loss during the feature coding process, particularly in the case of segmentation object boundary information. In the transition stage from encoder to decoder, pyramid fusion module is designed to extract global features. The features of the decoder not only integrate the features of the previous stage after up sampling and the output features of pyramid mechanism, but also include the features of skipping connection transmission under the same scale of the encoder.

RESULTS

The segmentation results of prostate and peri-prostatic fat on numerous diverse male pelvic MRI datasets show that our proposed PMF-Net has higher performance than existing methods. The average surface distance (ASD) and Dice similarity coefficient (DSC) of prostate segmentation results reached 10.06 and 90.21%, respectively. The ASD and DSC of the peri-prostatic fat segmentation results reached 50.96 and 82.41%.

CONCLUSIONS

The results of our segmentation are substantially connected and consistent with those of expert manual segmentation. Furthermore, peri-prostatic fat segmentation is a new issue, and good automatic segmentation has substantial therapeutic implications.

Prostate cancer in PI-RADS scores 1 and 2 version 2.1: a comparison to previous PI-RADS versions

PURPOSE

To evaluate the validity of PI-RADS categories 1 and 2 version 2.1 (V2.1) as predictors of the absence of carcinoma and to reevaluate lesions that were analysed as suspicious prior to PI-RADS or according to PI-RADS versions 1 and 2 and classified as PI-RADS 1 or 2 in V2.1.

METHODS

Retrospective evaluation of 1170 multiparametric MRIs performed at one academic teaching hospital (2012-2019). Study cohort comprised 188 men that achieved PI-RADS scores 1 or 2 (V2.1) and underwent systematic and targeted biopsy, split into one group with suspect findings in the original reports that were created prior to PI-RADS or with version 1 and 2, and another group with unremarkable reports. Differences in presence of prostate cancer and PSA density were assessed by Chi-square and Fisher's exact test, and the negative predictive value (NPV) for both groups was conducted.

RESULTS

The NPV for clinically significant carcinoma (csCa) was 89.1% for 55 men with suspect findings in the original report and 93.2% for 133 men with negative MRI. There was no difference between the groups regarding the detection of csCa (p = 0.103). PSA density was significantly higher in the group with suspect original reports (p = 0.015).

CONCLUSION

A PI-RADS score 1 or 2 appears less likely to miss existing prostate cancer, although a small amount of csCa can be overlooked. In case of clinical suspicion or elevated PSA density and PI-RADS score 1 or 2, an individual decision has to be taken if biopsy is necessary or if monitoring is sufficient.

The benefit of adopting Microultrasound in the prostate cancer imaging pathway: A lesion-by-lesion analysis: Biopsies prostatiques guidée par micro-échographie, quel bénéfice ? Une analyse lésion par lésion

INTRODUCTION

- Microultrasound (MicroUS) is a novel imaging modality relying on a high-frequency transducer which confers a three-fold improvement in spatial resolution as compared with conventional transrectal ultrasound. We evaluated the diagnostic value of MRI-MicroUS fusion biopsy and determined the additional benefit of employing MicroUS.

METHODS

- Retrospective analysis of consecutive treatment-naïve men undergoing MRI-MicroUS fusion biopsy between May 2018 and March 2019. Pre-biopsy MRI was systematically reviewed in a dedicated meeting where suspicious lesions PIRADS ≥ 3 were registered and uploaded in the ExactVu MicroUS device. MRI and MicroUS lesions were individually marked in a PIRADS v2 scheme. The biopsy protocol included MRI-MicroUS fusion and MicroUS targeted biopsies; systematic biopsies were performed at clinician's discretion. The diagnostic value was evaluated in terms of detection rate of clinically significant prostate cancer, defined as Gleason pattern ≥ 4 at histology.

RESULTS

- In all, 148 patients with a median age of 69 years (IQR 63-74) and median PSA density of 0.16 ng/ml/cc (0.10-0.23) were included. Clinically significant cancer was detected in 42.5% (63/148) patients. MRI detected 89 lesions in the peripheral zone; 73% (65/89) were visible on MicroUS. Clinically significant cancer was detected in 46.1% (30/65) MRI and MicroUS visible lesions, and in 4.2% (1/24) lesions only visible on mpMRI. MicroUS additionally identified 35 suspicious lesions non-visible on MRI of which clinically significant cancer was present in 25.7% (9/35).

CONCLUSION

- Adding MicroUS to the conventional pathway seems to increase the detection rate of clinically significant disease in unselected men undergoing biopsy. © 2022 Elsevier Masson SAS. All rights reserved.