Assessment of PI-RADS v2 for the Detection of Prostate Cancer

A systematic review and meta-analysis comparing the 2019 and 2005 Bosniak classification systems for assessing renal cysts and cystic renal masses: diagnostic accuracy and inter-rater agreement evaluation

OBJECTIVES

The Bosniak classification system (BCS) is integral in clinical decision-making for renal cysts and cystic renal masses, with updates in 2005 and 2019 aiming to enhance diagnostic accuracy and clinical utility. Despite these revisions, challenges in inter-rater agreement and practical applicability persist, underscoring the need for comprehensive evaluation of both versions' effectiveness in guiding patient care.

METHODS

This meta-analysis adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and systematically reviewed studies comparing the 2005 and 2019 BCS versions using CT or MRI. We searched PubMed, Embase, Scopus, and Web of Science and used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) for quality assessment. In this study, we focused on diagnostic performance and inter-observer agreement. Statistical analysis involved bivariate random-effects modelling and assessing pooled sensitivity, specificity, and inter-rater reliability.

RESULTS

We included 11 articles. The 2019 Bosniak classification showed higher diagnostic performance for CT (sensitivity: 85.7%, specificity: 81.9%) and MRI (sensitivity: 96.2%, specificity: 70.9%) compared to the 2005 version. Inter-rater reliability was better with the 2019 classification (CT kappa: 0.813, MRI kappa: 0.601) than with the 2005 version. The quality assessment indicated a low risk of bias overall, though some studies had high risk in specific areas.

CONCLUSION

The Bosniak 2019 classification provides improved diagnostic specificity and inter-rater reliability compared to the 2005 version. Its adoption may enhance clinical decision-making and reduce overtreatment in managing cystic renal masses.

ADVANCES IN KNOWLEDGE

This paper is novel in being the first meta-analysis to demonstrate that the Bosniak 2019 classification system significantly enhances inter-rater agreement, and overall diagnostic performance compared to the 2005 version, particularly in CT imaging, thus offering a more accurate and reliable tool for evaluating cystic renal masses.

Comparison of visual interpretation of [I-123] FP-CIT SPECT scans versus reference-based quantitative analysis utilizing a Japanese normal database

OBJECTIVE

Dopamine transporter single-photon emission computed tomography (DAT-SPECT) plays an important role in diagnosing parkinsonism. Recently, a reference-based quantitative analysis utilizing a Japanese normal database for DAT-SPECT was developed. This study aimed to investigate the frequency and trends of cases wherein the analysis- and physician-based diagnoses diverged.

METHODS

Two physicians performed an interpretation task twice on 195 DAT-SPECT scans. After assessing intra- and intertester agreements, disagreements were resolved by consensus. For the reference-based quantitative analysis, the calibrated specific binding ratio (cSBR), calibrated asymmetry index (cAI), and Z-scores were measured. Images were grouped according to physician consensus and the negative-positive difference from thresholds (Z-score of less than -2.0 and/or cAI of more than 12.22) as follows: group 1 (physician, normal; quantitative analysis, normal; n  = 70), group 2 (abnormal; normal; n  = 4), group 3 (normal; abnormal; n  = 31), and group 4 (abnormal; abnormal; n  = 90).

RESULTS

Median cSBRs and Z-scores decreased in order from group 1 to group 4. Median cAI values increased in the order of groups 1, 3, 2, and 4. Significant differences were observed between groups 1 and 2 for cSBRs and cAIs; groups 2 and 3 for Z-scores; groups 2 and 4 for cSBRs and Z-scores; and groups 1 and 3, 1 and 4, and 3 and 4 for all parameters (Kruskal-Wallis and Steel-Dwass tests).

CONCLUSION

In approximately 18% of cases, the visual interpretation of physicians diverged from the reference-based quantitative analysis based on a Japanese normal database. It is crucial to appropriately utilize DAT-SPECT reference-based quantitative analysis as a diagnostic aid.

Prostate cancer detection with transrectal in-bore MRI biopsies: impact of prostate volume and lesion features

OBJECTIVES

To systematically analyze the diagnostic outcome of transrectal in-bore MRI-guided biopsies as a function of prostate volume and lesion features.

METHODS

This single-center study retrospectively included 184 consecutive patients with transrectal in-bore MRI biopsies and histological analysis after multiparametric MRI diagnostics of at least one PI-RADS ≥ 3 lesion. Diagnostic and biopsy MRI data were analyzed for a number of patient and imaging features, specifically prostate volume, lesion size, lesion location (longitudinal, sagittal and segmental) and lesion depth. Features were then compared for statistically significant differences in the cancer detection rate (CDR) of clinically significant (cs-PCa) and any prostate cancer (any-PCa) using categorical and continuous variables.

RESULTS

A total of 201 lesions were biopsied detecting cs-PCa in 26% and any-PCa in 68%, respectively. In subgroup analyses of all features, the CDR of cs-PCa differed significantly between ranges of lesion size only (p < 0.001, largest for large lesions). In multivariable analysis, however, only PI-RADS score and PSA showed a significant association with a higher risk of cs-PCa.

CONCLUSIONS

The cancer detection rates of transrectal in-bore MRI-guided biopsies did not vary significantly for prostate volume, lesion size or lesion location. This suggests that the diagnostic performance of such an approach is not necessarily compromised for challenging biopsy settings like large glands, small lesions or eccentric locations. A translation of these findings to other cohorts might be limited by the low detection rate for clinically significant cancer.

CRITICAL RELEVANCE STATEMENT

This systematic analysis indicates that the diagnostic performance of transrectal in-bore biopsies might not be substantially impaired by patient-specific factors like prostate volume, lesion size, and lesion location, making it a viable option for challenging biopsy cases as well.

KEY POINTS

The impact of prostate and lesion features on in-bore MRI biopsy performance is controversial. Neither prostate volume, lesion size, nor location showed significant impact on cancer detection. In-bore biopsy does not seem to be limited by challenging sampling geometries.

Clinical and Imaging Predictors of False-Positive and False-Negative Results in Prostate Multiparametric MRI Using PI-RADS Version 2

Purpose To evaluate predictors of false-positive (FP) and false-negative (FN) results for prostate cancer at prostate multiparametric MRI (mpMRI) using the Prostate Imaging and Reporting Data System version 2 (PI-RADS v2). Materials and Methods This was a single-center retrospective cohort study of 2548 consecutive patients who underwent prostate mpMRI examinations (October 2016-July 2022) containing zero or one PI-RADS v2 category 3-5 lesions. Prostate mpMRI examinations were interpreted by 13 radiologists. FP results were defined as prospective PI-RADS v2 score of 3 or higher but benign or grade group 1 prostate cancer at subsequent combined targeted and systematic biopsy. FN results were defined as prospective PI-RADS v2 score 2 or lower but grade group 2 or higher prostate cancer at subsequent combined targeted and systematic biopsy. Predictors of FP and FN results were assessed by logistic regression. Results Among the 2548 patients (mean age, 65.7 years ± 7.6 [SD]; all male) analyzed, 52.0% (831 of 1597) had FP results and 15.8% (150 of 951) had FN results at mpMRI. FP results were more likely for younger patients (odds ratio [OR], 0.95/y; P < .001), smaller lesions (OR, 0.62/mm; P < .001), transition zone lesions (OR, 1.74 vs peripheral zone; P = .006), and patients with low prostate-specific antigen (PSA) density (OR, 0.55 per 0.1 ng/mL2 increase; P < .001). FN results were more likely for older patients (OR, 1.03/y; P = .01) and patients with high PSA density (OR, 2.05 per 0.1 ng/mL2 increase; P < .001). Conclusion PSA density and patient age independently predicted FP and FN results for detection of prostate cancer at mpMRI using PI-RADS v2. These factors are not part of the PI-RADS v2 algorithm and may inform mpMRI interpretation to improve prostate cancer diagnosis. Keywords: MR Imaging, Prostate, PI-RADS, Prostate MRI, Prostate Cancer ©RSNA, 2025.

Ultrasound and advanced imaging techniques in prostate cancer diagnosis: A comparative study of mpMRI, TRUS, and PET/CT

ObjectiveThis study aims to assess and compare the diagnostic performance of three advanced imaging modalities-multiparametric magnetic resonance imaging (mpMRI), transrectal ultrasound (TRUS), and positron emission tomography/computed tomography (PET/CT)-in detecting prostate cancer in patients with elevated PSA levels and abnormal DRE findings.MethodsA retrospective analysis was conducted on 150 male patients aged 50-75 years with elevated PSA and abnormal DRE. The diagnostic accuracy of each modality was assessed through sensitivity, specificity, and the area under the curve (AUC) to compare performance in detecting clinically significant prostate cancer (Gleason score ≥ 7).ResultsMpMRI demonstrated the highest diagnostic performance, with a sensitivity of 90%, specificity of 85%, and AUC of 0.92, outperforming both TRUS (sensitivity 76%, specificity 78%, AUC 0.77) and PET/CT (sensitivity 82%, specificity 80%, AUC 0.81). MpMRI detected clinically significant tumors in 80% of cases. Although TRUS and PET/CT had similar detection rates for significant tumors, their overall accuracy was lower. Minor adverse events occurred in 5% of patients undergoing TRUS, while no significant complications were associated with mpMRI or PET/CT.ConclusionThese findings suggest that mpMRI is the most reliable imaging modality for early detection of clinically significant prostate cancer. It reduces the need for unnecessary biopsies and optimizes patient management.

PI-RADS in Predicting csPCa: A Comparison Between Academic and Nonacademic Centers

INTRODUCTION

The introduction of multiparametric prostate magnetic resonance imaging (mpMRI) has revolutionized prostate cancer (PCa) diagnosis, enhancing the localization of clinically significant prostate cancer (csPCa) and guiding targeted biopsies. However, significant disparities in the execution, interpretation, and reporting of prostate MRI examinations across centers necessitate greater standardization and accuracy. This study compares the diagnostic efficacy of mpMRI from academic and nonacademic centers in detecting csPCa and identifies factors associated with csPCa detection.

MATERIALS AND METHODS

Between July 2018 and October 2023, we prospectively followed 810 men at SS. Annunziata Hospital of Chieti who underwent MRI/US fusion biopsies due to elevated prostate-specific antigen (PSA) and/or abnormal digital rectal examination (DRE). Patients with mpMRI-documented suspicious lesions classified as PI-RADS ≥ 3 were included. Patients were divided into two groups based on the source of their mpMRI (academic or nonacademic centers). All biopsies were conducted using the MRI/US fusion technique. Clinical, mpMRI, and pathological data were collected and analyzed. Statistical analyses were performed using R software.

RESULTS

The cohort included 354 patients from academic centers and 456 from nonacademic centers. There were no significant differences in patient demographics, such as age and PSA levels, between the groups. Patients at academic centers were more likely to receive a higher number of elevated PI-RADS scores compared to those at nonacademic centers (PI-RADS > 3: 72.6% vs. 62.3%, p = 0.003). Histopathological analysis revealed no significant differences in the ISUP grade distribution between groups. Increased age, PSA levels, and positive DRE were significantly associated with higher odds of detecting csPCa. Median PSA density was significantly higher in patients with csPCa compared to those without csPCa (0.14 vs. 0.11 ng/mL/cm³, p < 0.001). Academic centers exhibited a higher odds ratio for csPCa detection in patients with PI-RADS scores > 3 compared to nonacademic centers.

CONCLUSION

Our study highlights significant variability in PI-RADS score assignments between academic and nonacademic centers, affecting csPCa detection rates. This variability underscores the need for greater standardization in PI-RADS scoring to reduce disparities and improve diagnostic uniformity across centers.

Detecting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions Using T2w-Derived Radiomics Feature Maps in 3T Prostate MRI

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann-Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70-0.84). The diagnostic performance for TZ lesions (AUC: 0.83-1.00) was superior to PZ lesions (AUC: 0.74-0.85). In the bivariate models, performance in the PZ improved with AUCs >0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions.

Preoperative multidisciplinary team meeting improves the incidence of positive margins in pathological T2 prostate cancer

PURPOSE

Positive surgical margins (PSM) after robot-assisted radical prostatectomy (RARP) for prostate cancer (PCa) can increase the risk of biochemical recurrence and PCa-specific mortality. We aimed to evaluate the impact of multidisciplinary team meetings (MDTM) on reducing the incidence of PSM following RARP.

METHODS

We retrospectively collected the clinical data of consecutive patients undergoing RARP at Hiroshima University between February 2017 and October 2023. The MDTM, comprising a radiologist, uropathologist, and urologist, reviewed the preoperative magnetic resonance imaging (MRI) and prostate biopsy results of each patient before RARP and considered the areas requiring attention during RARP. Surgeons were categorized as experienced or non-experienced based on the number of RARP procedures performed.

RESULTS

In the pT2 population, the PSM rate was significantly lower in cases evaluated using the MDTM than in those not (11.1% vs. 24.0%; p = 0.0067). Cox regression analysis identified that a PSA level > 7 ng/mL (hazard ratio 2.2799) and nerve-sparing procedures (hazard ratio 2.2619) were independent predictors of increased PSM risk while conducting an MDTM (hazard ratio 0.4773) was an independent predictor of reduced PSM risk in the pT2 population. In the pathological T3 population, there was no significant difference in PSM rates between cases evaluated and not evaluated at an MDTM. In cases evaluated at an MDTM, similar PSM rates were observed regardless of surgeon experience (10.4% for non-experienced and 11.9% for experienced surgeons; p = 0.9999).

CONCLUSIONS

An MDTM can improve the PSM rate of pT2 PCa following RARP.

Perspectives on technology: Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability

OBJECTIVES

To explore the topic of Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability, including a discussion of major sources, mitigation approaches, and future directions.

METHODS

A narrative review of PI-RADS interobserver variability.

RESULTS

PI-RADS was developed in 2012 to set technical standards for prostate magnetic resonance imaging (MRI), reduce interobserver variability at interpretation, and improve diagnostic accuracy in the MRI-directed diagnostic pathway for detection of clinically significant prostate cancer. While PI-RADS has been validated in selected research cohorts with prostate cancer imaging experts, subsequent prospective studies in routine clinical practice demonstrate wide variability in diagnostic performance. Radiologist and biopsy operator experience are the most important contributing drivers of high-quality care among multiple interrelated factors including variability in MRI hardware and technique, image quality, and population and patient-specific factors such as prostate cancer disease prevalence. Iterative improvements in PI-RADS have helped flatten the curve for novice readers and reduce variability. Innovations in image quality reporting, administrative and organisational workflows, and artificial intelligence hold promise in improving variability even further.

CONCLUSION

Continued research into PI-RADS is needed to facilitate benchmark creation, reader certification, and independent accreditation, which are systems-level interventions needed to uphold and maintain high-quality prostate MRI across entire populations.

PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.

The Impact of Prostate Volume on Prostate Cancer Detection: Comparing Magnetic Resonance Imaging with Transrectal Ultrasound in Biopsy-naïve Men

Background and objective

This study aimed to determine the difference in prostate volume (PV) derived from transrectal ultrasound (TRUS) and multiparametric magnetic resonance imaging (mpMRI), and to further investigate the role of TRUS prostate-specific antigen density (PSAD) and mpMRI-PSAD in prostate cancer (PCa) detection in biopsy-naïve men.

Methods

Patients who underwent an initial prostate biopsy within 3 mo after mpMRI between January 2016 and December 2021 were analyzed retrospectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both TRUS-PSAD and mpMRI-PSAD for PCa detection were calculated and compared. The Pearson correlation coefficient, Bland-Altman plot, and receiver operating characteristic curve were also utilized to explore the interests of this study.

Key findings and limitations

The median prostate-specific antigen level of 875 patients was 9.79 (interquartile range [IQR]: 7.09-13.50) ng/ml. The median mpMRI-PV and TRUS-PV were 41.92 (IQR: 29.29-60.73) and 41.04 (IQR: 29.24-57.27) ml, respectively, demonstrating a strong linear correlation (r = 0.831, 95% confidence interval: 0.809, 0.850; p < 0.01) and sufficient agreement. No significant difference was observed in terms of the sensitivity, specificity, PPV, and NPV between TRUS-PSAD and mpMRI-PSAD for any PCa and clinically significant PCa (csPCa) detection. The overall discriminative ability of TRUS-PSAD for detecting PCa or non-PCa, as well as csPCa and non-csPCa, was comparable with that of mpMRI-PSAD, and similar results were also observed in the subsequent analysis stratified by mpMRI-PV quartiles, prostate-specific antigen level, and age. The limitations include the retrospective and single-center nature and a lack of follow-up information.

Conclusions and clinical implications

TRUS-PV and MRI-PV exhibited a strong linear correlation and reached sufficient agreement. The efficiency of TRUS-PSAD and mpMRI-PSAD for PCa detection was comparable. TRUS could be used for PV estimation and dynamic monitoring of PSAD, and TRUS-PSAD could effectively guide clinical decision-making and optimize diagnostic strategies.

Patient summary

In this work, prostate volume (PV) derived from transrectal ultrasound (TRUS) exhibited a strong linear correlation with the PV derived from multiparametric magnetic resonance imaging (mpMRI). The efficiency of TRUS prostate-specific antigen density (PSAD) and mpMRI-PSAD for the detection of prostate cancer was comparable. TRUS could be used for PV estimation and TRUS-PSAD could help in clinical decision-making and optimizing diagnostic strategies.

A Comparative Evaluation of Multiparametric Magnetic Resonance Imaging and Micro-Ultrasound for the Detection of Clinically Significant Prostate Cancer in Patients with Prior Negative Biopsies

BACKGROUND

The diagnostic process for prostate cancer after a negative biopsy is challenging. This study compares the diagnostic accuracy of micro-ultrasound (mUS) with multiparametric magnetic resonance imaging (mpMRI) for such cases.

METHODS

A retrospective cohort study was performed, targeting men with previous negative biopsies and using mUS and mpMRI to detect prostate cancer and clinically significant prostate cancer (csPCa).

RESULTS

In our cohort of 1397 men, 304 had a history of negative biopsies. mUS was more sensitive than mpMRI, with better predictive value for negative results. Importantly, mUS was significantly associated with csPCa detection (adjusted odds ratio [aOR]: 6.58; 95% confidence interval [CI]: 1.15-37.8; p = 0.035).

CONCLUSIONS

mUS may be preferable for diagnosing prostate cancer in previously biopsy-negative patients. However, the retrospective design of this study at a single institution suggests that further research across multiple centers is warranted.

Four-quadrant vector mapping of hybrid multidimensional MRI data for the diagnosis of prostate cancer

PURPOSE

The interpretation of prostate multiparametric magnetic resonance imaging (MRI) is subjective in nature, and there is large inter-observer variability among radiologists and up to 30% of clinically significant cancers are missed. This has motivated the development of new MRI techniques and sequences, especially quantitative approaches to improve prostate cancer diagnosis. Using hybrid multidimensional MRI, apparent diffusion coefficient (ADC) and T2 have been shown to change as a function of echo time (TE) and b-values, and that this dependence is different for cancer and benign tissue, which can be exploited for prostate cancer diagnosis. The purpose of this study is to investigate whether four-quadrant vector mapping of hybrid multidimensional MRI (HM-MRI) data can be used to diagnose prostate cancer (PCa) and determine cancer aggressiveness.

METHODS

Twenty-one patients with confirmed PCa underwent preoperative MRI prior to radical prostatectomy. Axial HM-MRI were acquired with all combinations of TE = 47, 75, 100 ms and b-values of 0, 750, 1500 s/mm2 , resulting in a 3 × 3 data matrix associated with each voxel. Prostate Quadrant (PQ) mapping analysis represents HM-MRI data for each voxel as a color-coded vector in the four-quadrant space of HM-MRI parameters (a 2D matrix of signal values for each combination of b-value and TE) with associated amplitude and angle information representing the change in T2 and ADC as a function of b-value and TE, respectively.

RESULTS

Cancers have a higher PQ4 (22.50% ± 21.27%) and lower PQ2 (69.86% ± 28.24%) compared to benign tissue: peripheral, transition, and central zone (PQ4 = 0.13% ± 0.56%, 5.73% ± 15.07%, 2.66% ± 4.05%, and PQ2 = 98.51% ± 3.05%, 86.18% ± 21.75%, 93.38% ± 9.88%, respectively). Cancers have a higher vector angle (206.5 ± 41.8°) and amplitude (0.017 ± 0.013) compared to benign tissue. PQ metrics showed moderate correlation with Gleason score (|ρ| = 0.388-0.609), with more aggressive cancers being associated with increased PQ4 and angle and reduced PQ2 and amplitude. A combination of four-quadrant analysis metrics provided an area under the curve of 0.904 (p < 0.001) for the differentiation of prostate cancer from benign prostatic tissue.

CONCLUSIONS

Four-quadrant vector mapping of HM-MRI data provides effective cancer markers, with cancers associated with high PQ4 and high vector angle and lower PQ2 and vector amplitude.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Patients With "Gray Zone" PSA Levels: Application of Prostate MRI and MRS in the Diagnosis of Prostate Cancer

Improving the detection rates of prostate cancer (PCa) and avoiding unnecessary prostate biopsies in men with prostate-specific antigen (PSA) levels within the gray zone require urgent attention. In this context, rapid advances in MR technology in recent years may offer a promising possibility. A systematic review to evaluate the applications of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in detecting PCa and clinically significant PCa (csPCa) in men with PSA levels within the gray zone. The study type is defined as systematic review. In July 2022, out of 229 studies identified by the database search and from other sources, 23 articles related to the selected topic of interest were included in this review. No field strength or sequence restrictions. The data including the study population, study characteristics, as well as basic MRI characteristics, from the final studies included in this review, were extracted independently by two reviewers. The major results of the original study were summarized and no additional statistical analysis was performed. Among the 23 studies included in this review, 17 focused on the applications of MRS and MRI for the prebiopsy diagnosis of PCa. Nine of these 17 articles used Prostate Imaging Reporting and Data System (PI-RADS) score to interpret MRI results, thereby confirming the practicality of the PI-RADS score in predicting PCa and csPCa. The remaining six articles evaluated the applications of MRI and MRS in guiding prostate biopsy. Although there was a variation in the biopsy modalities used in these studies, both MRI- and MRS-guided prostate biopsies were observed to improve the detection rates of PCa and csPCa in patients with PSA levels within the gray zone. MRS and MRI showed good performance in the detection of PCa and csPCa before biopsy. In addition, MRS- or MRI-guided prostate-targeted biopsies were able to improve the detection rates of PCa and csPCa. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging

PURPOSE OF REVIEW

The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging.

RECENT FINDINGS

Artificial intelligence has been applied to prostate cancer MR imaging to improve its diagnostic accuracy and reproducibility of interpretation. Multiple models have been tested for gland segmentation and volume calculation, automated lesion detection, localization, and characterization, as well as prediction of tumor aggressiveness and tumor recurrence. Studies show, for example, that very robust automated gland segmentation and volume calculations can be achieved and that lesions can be detected and accurately characterized. Although results are promising, we should view these with caution. Most studies included a small sample of patients from a single institution and most models did not undergo proper external validation. More research is needed with larger and well-design studies for the development of reliable artificial intelligence tools.

Staging accuracy of MRI of the prostate with special reference to the influence of the time of last ejaculation on the detection of seminal vesicle invasion

AIM

To evaluate the Prostate Imaging Reporting and Data System, version 2.1 (PIRADS V2.1) criteria for seminal vesicle invasion (SVI) and examine whether the timing of last ejaculation influences the detection of SVI.

MATERIALS AND METHODS

The study population consisted of 68 patients (34 with SVI, 34 without SVI, matching groups by age and prostate volume) who underwent PIRADS V2.1-compliant multiparametric magnetic resonance imaging (MRI; 34 at 1.5 T, 34 at 3 T). Before the examination, the time of last ejaculation (38/68 ≤ 5 days, 30/68 > 5 days) was collected via a questionnaire. The five PIRADS V2.1 criteria for SVI with subsequent overall assessment were evaluated retrospectively by two independent examiners (examiner 1 with >10 years of experience, examiner 2 with 6 months of experience) in a single-blinded fashion for all patients using a questionnaire and a six-point scale (0 = no, 1 = very likely not, 2 = probably not, 3 = possible, 4 = probable, 5 = certain).

RESULTS

E1 achieved high specificity (100%) and positive predictive value (PPV; 100%) in the overall assessment, independent of the time of last ejaculation (sensitivity = 76.5%, negative predictive value [NPV] = 81%). The area under the curve (AUC) value was 0.882; for E2, it was 0.765. At ≤5 days, the AUC values of E1 and E2 differed significantly (0.867 versus 0.681, p=0.016), as did the diffusion restriction criterion (0.833 versus 0.681, p=0.028). E1 showed high AUC values independent of time. E2 had better values for all criteria at >5 days than at ≤5 days. There were no significant differences between the examiners in all observations at >5 days.

CONCLUSION

The PIRADS V2.1 criteria are well suited for an experienced examiner to detect SVI independent of time point. An inexperienced examiner will benefit from patients being abstinent >5 days prior to MRI.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Usefulness of grayscale values of hypoechoic lesions matched with target lesions observed on magnetic resonance imaging for the prediction of clinically significant prostate cancer

Background

To analyze grayscale values for hypoechoic lesions matched with target lesions evaluated using prebiopsy magnetic resonance imaging (MRI) according to the Prostate Imaging-Reporting and Data System (PI-RADS).

Methods

We collected data on 420 target lesions in patients who underwent MRI/transrectal ultrasound fusion-targeted biopsies between January 2017 and September 2020. Images of hypoechoic lesions that matched the target lesions on MRI were stored in a picture archiving and communication system, and their grayscale values were estimated using the red/green/blue scoring method through an embedded function. We analyzed imaging data using grayscale values.

Results

Of the 420 lesions, 261 (62.1%) were prostate cancer lesions. There was no difference in the median grayscale values between benign and prostate cancer lesions. However, grayscale ranges (41.8–98.5 and 42.6–91.8) were significant predictors of prostate cancer and clinically significant prostate cancer (csPC) in multivariable logistic regression analyses. Area under the curve for detecting csPC using grayscale values along with conventional variables (age, prostate-specific antigen levels, prostate volume, previous prostate biopsy results, and PI-RADS scores) was 0.839, which was significantly higher than that for detecting csPC using only conventional variables (0.828; P = 0.036). Subgroup analysis revealed a significant difference for PI-RADS 3 lesions between grayscale values for benign and cancerous lesions (74.5 vs. 58.8, P = 0.008). Grayscale values were the only significant predictive factor (odds ratio = 4.46, P = 0.005) for csPC.

Conclusions

Distribution of grayscale values according to PI-RAD 3 scores was potentially useful, and the grayscale range (42.6–91.8) was a potential predictor for csPC diagnosis.

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.

Prostate MRI percentage tumor involvement or "PI-RADS percent" as a predictor of adverse surgical pathology

BACKGROUND

This study assesses magnetic resonance imaging (MRI) prostate % tumor involvement or "PI-RADs percent" as a predictor of adverse pathology (AP) after surgery for localized prostate cancer (PCa). Two separate variables, "All PI-RADS percent" (APP) and "Highest PI-RADS percent" (HPP), are defined as the volume of All PI-RADS 3-5 score lesions on MRI and the volume of the Highest PI-RADS 3-5 score lesion each divided by TPV, respectively.

METHOD

An analysis was done of an IRB approved prospective cohort of 557 patients with localized PCa who had targeted biopsy of MRI PIRADs 3-5 lesions followed by RARP from April 2015 to May 2020 performed by a single surgeon at a single center. AP was defined as ISUP GGG ≥3, pT stage ≥T3 and/or LNI. Univariate and multivariable analyses were used to evaluate APP and HPP at predicting AP with other clinical variables such as Age, PSA at surgery, Race, Biopsy GGG, mpMRI ECE and mpMRI SVI. Internal and External Validation demonstrated predicted probabilities versus observed probabilities.

RESULTS

AP was reported in 44.5% (n = 248) of patients. Multivariable regression showed both APP (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.14, p = 0.0007) and HPP (OR: 1.10; 95% CI: 1.04-1.16; p = 0.0007) were significantly associated with AP with individual area under the operating curves (AUCs) of 0.6142 and 0.6229, respectively, and AUCs of 0.8129 and 0.8124 when incorporated in models including preoperative PSA and highest biopsy GGG.

CONCLUSIONS

Increasing PI-RADS Percent was associated with a higher risk of AP, and both APP and HPP may have clinical utility as predictors of AP in GGG 1 and 2 patients being considered for AS.

PATIENT SUMMARY

Using PIRADs percent to predict AP for presurgical patients may help risk stratification, and for low and low volume intermediate risk patients, may influence treatment decisions.

PSA density is complementary to prostate MP-MRI PI-RADS scoring system for risk stratification of clinically significant prostate cancer

BACKGROUND

While prostate multiparametric-magnetic resonance imaging (MP-MRI) has improved the diagnosis of clinically significant prostate cancer (CSPC), the complementary use of prostate-specific antigen (PSA) levels to risk-stratify for CSPC requires further study. The objective of this project was to determine if prostate MP-MRI and PSA can provide complementary insights into CSPC risk stratification.

METHODS

In an IRB-approved study, pathologic outcomes from patients who underwent MR/US fusion-targeted prostate biopsy were stratified by various parameters including PSA, PSA density (PSAD), age, race, and PI-RADS v2 score. CSPC was defined as a Gleason score ≥7. Logistic regression was used to determine odds ratios (OR) with 95% confidence intervals (CI). P values were reported as two-sided with p < 0.05 considered statistically significant. ROC curves were generated for assessing the predictive value of tests and sensitivity + specificity optimization was performed to determine optimal testing cutoffs.

RESULTS

A total of 327 patients with 709 lesions total were analyzed. PSAD and PI-RADS scores provided complementary predictive value for diagnosis of CSPC (AUC PSAD: 0.67, PI-RADS: 0.72, combined: 0.78, p < 0.001). When controlling for PI-RADS score, age, and race, multivariate analysis showed that PSAD was independently associated with CSPC (OR 1.03 per 0.01 PSAD increase, 95% CI 1.02-105, p < 0.001). The optimal cutoff of PSAD ≥ 0.1 ng/ml/cc shows that a high versus low PSAD was roughly equivalent to an increase in 1 in PI-RADS score for the presence of CSPC (4% of PI-RADS ≤3 PSAD low, 6% of PI-RADS 3 PSAD high vs. 5% of PI-RADS 4 PSAD low, 22% of PI-RADS 4 PSAD high vs. 29% of PI-RADS 5 PSAD low, 46% of PI-RADS 5 PSAD high were found to have CSPC).

CONCLUSIONS

PSAD with a cutoff of 0.1 ng/ml/cc appears to be a useful marker that can stratify the risk of CSPC in a complementary manner to prostate MP-MRI.

Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review

Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.

Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists' measures

PURPOSE

To validate prostate tissue composition measured using hybrid multi-dimensional MRI (HM-MRI) by comparing with reference standard (ground truth) results from pathologists' interpretation of clinical histopathology slides following whole mount prostatectomy.

MATERIALS AND METHODS

36 prospective participants with biopsy-confirmed prostate cancer underwent 3 T MRI prior to radical prostatectomy. Axial HM-MRI was acquired with all combinations of echo times of 57, 70, 150, 200 ms and b-values of 0, 150, 750, 1500 s/mm² and data were fitted using a 3-compartment signal model using custom software to generate volumes for each tissue component (stroma, epithelium, lumen). Three experienced genitourinary pathologists independently as well as in consensus reviewed each histology image and provide an estimate of percentage of epithelium and lumen for regions-of-interest corresponding to MRI (n = 165; 64 prostate cancers and 101 benign tissue). Agreement statistics using total deviation index (TDI_0.9) was performed for tissue composition measured using HM-MRI and reference standard results from pathologists' consensus.

RESULTS

Based on the initial results showing typical variation among pathologists TDI_0.9 = 25%, we determined we will declare acceptable agreement if the 95% one-sided upper confident limit of TDI_0.9 is less than 30%. The results of tissue composition measurement from HM-MRI compared to ground truth results from the consensus of 3 pathologists, reveal that ninety percent of absolute paired differences (TDI_0.9) were within 18.8% and 22.4% in measuring epithelium and lumen, respectively. We are 95% confident that 90% of absolute paired differences were within 20.6% and 24.2% in measuring epithelium and lumen, respectively. These were less than our criterion of 30% and inter-pathologists' agreement (22.3% for epithelium and 24.2% for lumen) and therefore we accept the agreement performance of HM-MRI. The results revealed excellent area under the ROC curve for differentiating cancer from benign tissue based on epithelium (HM-MRI: 0.87, pathologists: 0.97) and lumen volume (HM-MRI: 0.85, pathologists: 0.77).

CONCLUSION

The agreement in tissue composition measurement using hybrid multidimensional MRI and consensus of pathologists is on par with the inter-raters (pathologists) agreement.

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

Structured reporting in radiologic education - Potential of different PI-RADS versions in prostate MRI controlled by in-bore MR-guided biopsies

Objectives:

To evaluate the efficiency of structured reporting in radiologic education – based on the example of different PI-RADS score versions for multiparametric MRI (mpMRI) of the prostate.

Methods:

MpMRI of 688 prostate lesions in 180 patients were retrospectively reviewed by an experienced radiologist and by a student using PI-RADS V1 and V2. Data sets were reviewed for changes according to PI-RADS V2.1. The results were correlated with results obtained by MR-guided biopsy. Diagnostic potency was evaluated by ROC analysis. Sensitivity, specificity and correct-graded samples were evaluated for different cutpoints. The agreement between radiologist and student was determined for the aggregation of the PI-RADS score in three categories. The student’s time needed for evaluation was measured.

Results:

The area under curve of the ROC analysis was 0.782/0.788 (V1/V2) for the student and 0.841/0.833 (V1/V2) for the radiologist. The agreement between student and radiologist showed a Cohen‘s weighted κ coefficient of 0.495 for V1 and 0.518 for V2. Median student’s time needed for score assessment was 4:34 min for PI-RADSv1 and 2:00 min for PI-RADSv2 (p < 0.001). Re-evaluation for V2.1 changed the category in 1.4% of all ratings.

Conclusion:

The capacity of prostate cancer detection using PI-RADS V1 and V2 is dependent on the reader‘s experience. The results from the two observers indicate that structured reporting using PI-RADS and, controlled by histopathology, can be a valuable and quantifiable tool in students‘ or residents’ education. Herein, V2 was superior to V1 in terms of inter-observer agreement and time efficacy.

Advances in knowledge:

Structured reporting can be a valuable and quantifiable tool in radiologic education. Structured reporting using PI-RADS can be used by a student with good performance. PI-RADS V2 is superior to V1 in terms of inter-observer agreement and time efficacy.

State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review

We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.

Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan

Purpose

The clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans.

Methods

We retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: “negative”, “equivocal”, and “suspicious” for the presence of PCa.

Results

Univariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml² cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case.

Conclusion

Our multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study

OBJECTIVE

The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans.

MATERIALS AND METHODS

We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated.

RESULTS

The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001).

CONCLUSIONS

Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.

Urban-Rural Differences in Clinical Characteristics of Prostate Cancer at Initial Diagnosis: A Single-Center Observational Study in Anhui Province, China

Background

People residing in rural areas have higher prostate cancer (PCa) mortality to incidence ratio (M/I) and worse prognosis than those in urban areas of China. Clinical characteristics at initial diagnosis are significantly associated with biochemical recurrence, overall survival, and PCa disease-free survival.

Objective

This study aimed at investigating the clinical characteristics at initial diagnosis of urban and rural PCa patients and to establish a logistic regression model for identifying independent predictors for high-grade PCa.

Materials and Methods

Clinical characteristics for PCa patients were collected from the largest prostate biopsy center in Anhui province, China, from December 2015 to March 2019. First, urban-rural disparities in clinical characteristics were evaluated at initial diagnosis. Second, based on pathological findings, we classified all participants into the benign+ low/intermediate-grade PCa or high-grade PCa groups. Univariate and multivariate logistic regression analyses were performed to identify independent factors for predicting high-grade PCa, while a nomogram for predicting high-grade PCa was generated based on all independent factors. The model was evaluated using area under receiver-operating characteristic (ROC) curve as well as calibration curve analyses and compared to a model without the place of residence factor of individuals.

Results

Statistically significant differences were observed between urban and rural PCa patients with regard to tPSA, PSA density (PSAD), and Gleason score (GS) (p < 0.05). Logistic regression analysis revealed that tPSA [OR = 1.060, 95% confidence interval (CI): 1.024, 1.098], PSAD (OR = 14.678, 95%CI: 4.137, 52.071), place of residence of individuals (OR = 5.900, 95%CI: 1.068, 32.601), and prostate imaging reporting and data system version 2 (PI-RADS v2) (OR = 4.360, 95%CI: 1.953, 9.733) were independent predictive factors for high-grade PCa. The area under the curve (AUC) of the nomogram was greater than that of the model without the place of residence of individuals. The calibration curve of the nomogram indicated that the prediction curve was basically fitted to the standard curve, suggesting that the prediction model had a better calibration ability.

Conclusions

Compared to urban PCa patients, rural PCa patients presented elevated tPSA, PSAD levels, and higher pathological grades. The place of residence of the individuals was an independent predictor for high-grade PCa in Anhui Province, China. Therefore, appropriate strategies, such as narrowing urban-rural gaps in access to health care and increasing awareness on the importance of early detection should be implemented to reduce PCa mortality rates.

Robotic Transrectal Computed Tomographic Ultrasound with Artificial Neural Network Analysis: First Validation and Comparison with MRI-Guided Biopsies and Radical Prostatectomy

INTRODUCTION

There is still a lack of availability of high-quality multiparametric magnetic resonance imaging (mpMRI) interpreted by experienced uro-radiologists to rule out clinically significant PC (csPC). Consequently, we developed a new imaging method based on computed tomographic ultrasound (US) supported by artificial neural network analysis (ANNA).

METHODS

Two hundred and two consecutive patients with visible mpMRI lesions were scanned and recorded by robotic CT-US during mpMRI-TRUS biopsy. Only significant index lesions (ISUP ≥2) verified by whole-mount pathology were retrospectively analyzed. Their visibility was reevaluated by 2 blinded investigators by grayscale US and ANNA.

RESULTS

In the cohort, csPC was detected in 105 cases (52%) by mpMRI-TRUS biopsy. Whole-mount histology was available in 44 cases (36%). In this subgroup, mean PSA level was 8.6 ng/mL, mean prostate volume was 33 cm3, and mean tumor volume was 0.5 cm3. Median PI-RADS and ISUP of index lesions were 4 and 3, respectively. Index lesions were visible in grayscale US and ANNA in 25 cases (57%) and 30 cases (68%), respectively. Combining CT-US-ANNA, we detected index lesions in 35 patients (80%).

CONCLUSIONS

The first results of multiparametric CT-US-ANNA imaging showed promising detection rates in patients with csPC. US imaging with ANNA has the potential to complement PC diagnosis.

Magnetic Resonance Imaging (MRI)-Targeted Biopsy in Patients with Prostate-Specific Antigen (PSA) Levels <20 ng/mL: A Single-Center Study in Northeastern China

Background

We investigated the feasibility of applying magnetic resonance imaging (MRI)-targeted biopsy (TB) in patients with prostate-specific antigen (PSA) levels <20 ng/mL.

Material/Methods

We retrospectively analyzed 218 patients with PSA levels <20 ng/mL and suspicious lesions according to the Prostate Imaging Recording and Data System version 2.0 (PI-RADS v2). All 218 men underwent transperineal MRI-TB, followed by template-guided 12-core systematic biopsy (SB). Of the 218 patients undergoing TB, 100 received MRI-ultrasound-assisted software fusion biopsy (FB) and 118 received cognitive biopsy (CB). Clinically significant prostate cancer (csPCa) was defined as a Gleason score ≥3+4.

Results

The overall TB positive rate was similar to that of SB (P=0.156), but with a higher diagnostic rate for csPCa (P=0.034). SB misdiagnosed csPCa in 11.47% of cases; TB misdiagnosed csPCa in 5.50% of cases. SB+TB detected more tumors with a Gleason score of 7 than did SB alone (43 vs 22). Detection rates of csPCa were similar for CB and FB (P=0.217). In total, 47 men had 2 MRI-determined suspicious areas. Of 265 suspicious areas, 143 (53.96%) had a PI-RADS v2 score of 3; 92 (34.71%) had a score of 4; and 30 (11.32%) had a score of 5. The positive detection rates for csPCa in patients with PI-RADS v2 scores of 3, 4, and 5, were 11.19%, 48.91%, and 80.00%, respectively.

Conclusions

TB increased the positive biopsy detection rate but missed some cases of csPCa. TB combined with SB may be the most suitable biopsy for patients with PSA <20 ng/mL.

Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging

BACKGROUND

Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance or whole-mount histopathology (WMHP).

PURPOSE

To compare the performance of a previously proposed deep learning algorithm, FocalNet, and expert radiologists in the detection of PCa on mpMRI with WMHP as the reference.

STUDY TYPE

Retrospective, single-center study.

SUBJECTS

A total of 553 patients (development cohort: 427 patients; evaluation cohort: 126 patients) who underwent 3-T mpMRI prior to radical prostatectomy from October 2010 to February 2018.

FIELD STRENGTH/SEQUENCE

3-T, T2-weighted imaging and diffusion-weighted imaging.

ASSESSMENT

FocalNet was trained on the development cohort to predict PCa locations by detection points, with a confidence value for each point, on the evaluation cohort. Four fellowship-trained genitourinary (GU) radiologists independently evaluated the evaluation cohort to detect suspicious PCa foci, annotate detection point locations, and assign a five-point suspicion score (1: least suspicious, 5: most suspicious) for each annotated detection point. The PCa detection performance of FocalNet and radiologists were evaluated by the lesion detection sensitivity vs. the number of false-positive detections at different thresholds on suspicion scores. Clinically significant lesions: Gleason Group (GG) ≥ 2 or pathological size ≥ 10 mm. Index lesions: the highest GG and the largest pathological size (secondary).

STATISTICAL TESTS

Bootstrap hypothesis test for the detection sensitivity between radiologists and FocalNet.

RESULTS

For the overall differential detection sensitivity, FocalNet was 5.1% and 4.7% below the radiologists for clinically significant and index lesions, respectively; however, the differences were not statistically significant (P = 0.413 and P = 0.282, respectively).

DATA CONCLUSION

FocalNet achieved slightly lower but not statistically significant PCa detection performance compared with GU radiologists. Compared with radiologists, FocalNet demonstrated similar detection performance for a highly sensitive setting (suspicion score ≥ 1) or a highly specific setting (suspicion score = 5), while lower performance in between.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

The Comparison of Imaging and Clinical Methods to Estimate Prostate Volume: A Single-Centre Retrospective Study

BACKGROUND

Prostate volume (PV) is a useful tool in risk stratification, diagnosis, and follow-up of numerous prostatic diseases including prostate cancer and benign prostatic hypertrophy. There is currently no accepted ideal PV measurement method.

OBJECTIVE

This study compares multiple means of PV estimation, including digital rectal examination (DRE), transrectal ultrasound (TRUS), and magnetic resonance imaging (MRI), and radical prostatectomy specimens to determine the best volume measurement style.

METHODS

A retrospective, observational, single-site study with patients identified using an institutional database was performed. A total of 197 patients who underwent robot-assisted radical prostatectomy were considered. Data collected included age, serum PSA at the time of the prostate biopsy, clinical T stage, Gleason score, and PVs for each of the following methods: DRE, TRUS, MRI, and surgical specimen weight (SPW) and volume.

RESULTS

A paired t test was performed, which reported a statistically significant difference between PV measures (DRE, TRUS, MRI ellipsoid, MRI bullet, SP ellipsoid, and SP bullet) and the actual prostate weight. Lowest differences were reported for SP ellipsoid volume (M = -2.37; standard deviation [SD] = 10.227; t[167] = -3.011; and p = 0.003), MRI ellipsoid volume (M = -4.318; SD = 9.53; t[167] = -5.87; and p = 0.000), and MRI bullet volume (M = 5.31; SD = 10.77; t[167] = 6.387; and p = 0.000).

CONCLUSION

The PV obtained by MRI has proven to correlate with the PV obtained via auto-segmentation software as well as actual SPW, while also being more cost-effective and time-efficient. Therefore, demonstrating that MRI estimated the PV is an adequate method for use in clinical practice for therapeutic planning and patient follow-up.

Diagnostic Performance of PI-RADS v2, Proposed Adjusted PI-RADS v2 and Biparametric Magnetic Resonance Imaging for Prostate Cancer Detection: A Preliminary Study

Purpose: To evaluate the diagnostic performance of PI-RADS v2, proposed adjustments to PI-RADS v2 (PA PI-RADS v2) and biparametric magnetic resonance imaging (MRI) for prostate cancer detection. Methods: A retrospective cohort of 224 patients with suspected prostate cancer was included from January 2016 to November 2018. All the patients underwent a multi-parametric MR scan before biopsy. Two radiologists independently evaluated the MR examinations using PI-RADS v2, PA PI-RADS v2, and a biparametric MRI protocol, respectively. Receiver operating characteristic (ROC) curves for the three different protocols were drawn. Results: In total, 90 out of 224 cases (40.18%) were pathologically diagnosed as prostate cancer. The area under the ROC curves (AUC) for diagnosing prostate cancers by biparametric MRI, PI-RADS v2, and PA PI-RADS v2 were 0.938, 0.935, and 0.934, respectively. For cancers in the peripheral zone (PZ), the diagnostic sensitivity was 97.1% for PI-RADS v2/PA PI-RADS v2 and 96.2% for biparametric MRI. Moreover, the specificity was 84.0% for biparametric MRI and 58.0% for PI-RADS v2/PA PI-RADS v2. For cancers in the transition zone (TZ), the diagnostic sensitivity was 93.4% for PA PI-RADS v2 and 88.2% for biparametric MRI/PI-RADS v2. Furthermore, the specificity was 95.4% for biparametric MRI/PI-RADS v2 and 78.0% for PA PI-RADS v2. Conclusions: The overall diagnostic performance of the three protocols showed minimal differences. For lesions assessed as being category 3 using the biparametric MRI protocol, PI-RADS v2, or PA PI-RADS v2, it was thought prostate cancer detection could be improved. Attention should be paid to false positive results when PI-RADS v2 or PA PI-RADS v2 are used.

Diagnostic Accuracy and Interobserver Agreement of PI-RADS Version 2 and Version 2.1 for the Detection of Transition Zone Prostate Cancers

BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience. METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa. RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p < .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9%, 79.4%, and 75.4% among all readers for PI-RADS v2.1 compared with 79.4%, 71.8%, and 73.8% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8%, 90.9%, and 89.9% among all readers for PI-RADS v2.1 compared with 81.4%, 89.9%, and 88.5% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0%, 89.2%, and 87.3%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1%, 92.9%, and 92.1%) in detecting csPCa. CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1. CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.

Value of an online PI-RADS v2.1 score calculator for assessment of prostate MRI

Purpose

To evaluate the value of a browser-based PI-RADS Score Calculator (PCalc) compared to MRI reporting using the official PI-RADS v2.1 document (PDoc) for non-specialized radiologists in terms of reporting efficiency, interrater agreement and diagnostic accuracy for detection of clinically significant prostate cancer (PCa).

Methods

Between 09/2013 and 04/2015, 100 patients (median age, 64.8; range 47.5-78.2) who underwent prostate-MRI at a 3 T scanner and who received transperineal prostate mapping biopsy within <6 months were included in this retrospective study. Two non-specialized radiology residents (R1, R2) attributed a PI-RADS version 2.1 score for the most suspect (i. e. index) lesion (i) using the original PI-RADS v2.1 document only and after a 6-week interval (ii) using a browser-based PCalc. Reading time was measured. Reading time differences were assessed using Wilcoxon signed rank test. Intraclass-correlation Coefficient (ICC) was used to assess interrater agreement (IRA). Parameters of diagnostic accuracy and ROC curves were used for assessment of lesion-based diagnostic accuracy.

Results

Cumulative reading time was 32:55 (mm:ss) faster when using the PCalc, the difference being statistically significant for both readers (p < 0.05). The difference in IRA between the image sets (ICC 0.55 [0.40, 0.68]) and 0.75 [0.65, 0.82] for the image set with PDoc and PCalc, respectively) was not statistically significant. There was no statistically significant difference in lesion-based diagnostic accuracy (AUC 0.83 [0.74, 0.92] and 0.82 [95 %CI: 0.74, 0.91]) for images assessed with PDoc as compared to PCalc (AUC 0.82 [0.74, 0.91] and 0.74 [95 %CI: 0.64, 0.83]) for R1 and R2, respectively.

Conclusion

Non-specialized radiologists may increase reading speed in prostate MRI with the help of a browser-based PI-RADS Score Calculator compared to reporting using the official PI-RADS v2.1 document without impairing interreader agreement or lesion-based diagnostic accuracy for detection of clinically significant PCa.

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.

Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

OBJECTIVE

To investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).

METHODS

This was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.

RESULTS

A total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940-0.996)] than PI-RADS [0.905 (0.844-0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749-0.936) vs. 0.845 (0.731-0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P < 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).

CONCLUSIONS

The radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

Detection and staging of radio-recurrent prostate cancer using multiparametric MRI

OBJECTIVE

We determined the sensitivity and specificity of multiparametric magnetic resonance imaging (MP-MRI) in detection of locally recurrent prostate cancer and extra prostatic extension in the post-radical radiotherapy setting. Histopathological reference standard was whole-mount prostatectomy specimens. We also assessed for any added value of the dynamic contrast enhancement (DCE) sequence in detection and staging of local recurrence.

METHODS

This was a single centre retrospective study. Participants were selected from a database of males treated with salvage prostatectomy for locally recurrent prostate cancer following radiotherapy. All underwent pre-operative prostate-specific antigen assay, positron emission tomography CT, MP-MRI and transperineal template prostate mapping biopsy prior to salvage prostatectomy. MP-MRI performance was assessed using both Prostate Imaging-Reporting and Data System v. 2 and a modified scoring system for the post-treatment setting.

RESULTS

24 patients were enrolled. Using Prostate Imaging-Reporting and Data System v. 2, sensitivity, specificity, positive predictive value and negative predictive value was 64%, 94%, 98% and 36%. MP-MRI under staged recurrent cancer in 63%. A modified scoring system in which DCE was used as a co-dominant sequence resulted in improved diagnostic sensitivity (61%-76%) following subgroup analysis.

CONCLUSION

Our results show MP-MRI has moderate sensitivity (64%) and high specificity (94%) in detecting radio-recurrent intraprostatic disease, though disease tends to be under quantified and under staged. Greater emphasis on dynamic contrast images in overall scoring can improve diagnostic sensitivity.

ADVANCES IN KNOWLEDGE

MP-MRI tends to under quantify and under stage radio-recurrent prostate cancer. DCE has a potentially augmented role in detecting recurrent tumour compared with the de novo setting. This has relevance in the event of any future modified MP-MRI scoring system for the irradiated gland.

Improved COVID-19 ICU admission and mortality outcomes following treatment with statins: a systematic review and meta-analysis

INTRODUCTION

Approximately 1% of the world population has now been infected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). With cases still rising and vaccines just beginning to rollout, we are still several months away from seeing reductions in daily case numbers, hospitalisations, and mortality. Therefore, there is a still an urgent need to control the disease spread by repurposing existing therapeutics. Owing to antiviral, anti-inflammatory, immunomodulatory, and cardioprotective actions, statin therapy has been considered as a plausible approach to improve COVID-19 outcomes.

MATERIAL AND METHODS

We carried out a meta-analysis to investigate the effect of statins on 3 COVID-19 outcomes: intensive care unit (ICU) admission, tracheal intubation, and death. We systematically searched the PubMed, Web of Science, Scopus, and ProQuest databases using keywords related to our aims up to November 2, 2020. All published observational studies and randomised clinical trials on COVID-19 and statins were retrieved. Statistical analysis with random effects modelling was performed using STATA16 software.

RESULTS

The final selected studies (n = 24 studies; 32,715 patients) showed significant reductions in ICU admission (OR = 0.78, 95% CI: 0.58-1.06; n = 10; I 2 = 58.5%) and death (OR = 0.70, 95% CI: 0.55-0.88; n = 21; I 2 = 82.5%) outcomes, with no significant effect on tracheal intubation (OR = 0.79; 95% CI: 0.57-1.11; n = 7; I 2= 89.0%). Furthermore, subgroup analysis suggested that death was reduced further by in-hospital application of stains (OR = 0.40, 95% CI: 0.22-0.73, n = 3; I 2 = 82.5%), compared with pre-hospital use (OR = 0.77, 95% CI: 0.60-0.98, n = 18; I 2 = 81.8%).

CONCLUSIONS

These findings call attention to the need for systematic clinical studies to assess both pre- and in-hospital use of statins as a potential means of reducing COVID-19 disease severity, particularly in terms of reduction of ICU admission and total mortality reduction.

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

Cutoff Values of Prostate Imaging Reporting and Data System Version 2.1 Score in Men With Prostate-specific Antigen Level 4 to 10 ng/mL: Importance of Lesion Location

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) has been shown to have a good performance in predicting cancer among patients with a prostate-specific antigen (PSA) level of 4 to 10 ng/mL. However, lesion location on mpMRI has never been separately considered.

PATIENTS AND METHODS

Patients with PSA level of 4 to 10 ng/mL were prospectively enrolled and underwent transrectal ultrasound-guided prostate biopsy. Patient information was collected, and logistic regression analysis was performed to determine the predictive factors of clinically significant prostate cancer (csPCa). Patients were grouped by lesion location to determine the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 cutoff value in predicting csPCa.

RESULTS

Among 222 patients, 121 were diagnosed with PCa and 92 had csPCa. Age, prostate volume, PSA density, location (peripheral zone, csPCa only), and PI-RADS v2.1 score were correlated with PCa and csPCa, and PI-RADS v2.1 score was the best predictor. A PI-RADS v2.1 score of 4 was the best cutoff value for predicting csPCa in patients with lesions only in the transitional zone with respect to the Youden index (0.5896) and negative predictive value (93.10%) with acceptable sensitivity (81.82%) and specificity (77.14%). An adjustment of the cutoff value to 3 for lesions in the peripheral zone would increase the negative predictive value (92.00%) and decrease the false negative rate (2.90%) with an acceptable sensitivity (97.10%) and specificity (30.67%).

CONCLUSION

PI-RADS v2.1 score is an effective predictor of csPCa in patients with PSA levels of 4 to 10 ng/mL. Patients with transitional zone or peripheral zone lesions should undergo biopsy if the PI-RADS v2.1 score is ≥ 4 or ≥ 3, respectively.

Active surveillance in prostate cancer management: where do we stand now?

Prostate cancer (PCa) is the most common cancer in men, with a steadily rising incidence, affecting on average one in six men during their lifetime. The increase in morbidity is related to the increasing overall life expectancy, prostate-specific antigen testing, implementation of new molecular markers for cancer detection and the more frequent application of multiparametric magnetic resonance imaging. There is growing evidence demonstrating that active surveillance (AS) is an alternative to immediate intervention in patients with very low- and low-risk prostate cancer. Ongoing reports from multiple studies have consistently demonstrated a very low rate of metastases and prostate cancer specific mortality in selected cohorts of patients. As a matter of fact, AS has been adopted by many institutions as a safe and effective management strategy. The aim of our review is to summarize the contemporary data on AS in patients affected with PCa with the intention to present the most clinically useful and pertinent AS protocols.

Multivariable Models Incorporating Multiparametric Magnetic Resonance Imaging Efficiently Predict Results of Prostate Biopsy and Reduce Unnecessary Biopsy

Purpose

We sought to develop diagnostic models incorporating mpMRI examination to identify PCa (Gleason score≥3+3) and CSPCa (Gleason score≥3+4) to reduce overdiagnosis and overtreatment.

Methods

We retrospectively identified 784 patients according to inclusion criteria between 2016 and 2020. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Age, PSA derivatives, prostate volume, and mpMRI parameters were assessed as predictors for PCa and CSPCa. The multivariable models based on clinical parameters were evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA).

Results

Univariate analysis showed that age, tPSA, PSAD, prostate volume, MRI-PCa, MRI-seminal vesicle invasion, and MRI-lymph node invasion were significant predictors for both PCa and CSPCa (each p≤0.001). PSAD has the highest diagnostic accuracy in predicting PCa (AUC=0.79) and CSPCa (AUC=0.79). The multivariable models for PCa (AUC=0.92, 95% CI: 0.88–0.96) and CSPCa (AUC=0.95, 95% CI: 0.92–0.97) were significantly higher than the combination of derivatives for PSA (p=0.041 and 0.009 for PCa and CSPCa, respectively) or mpMRI (each p<0.001) in diagnostic accuracy. And the multivariable models for PCa and CSPCa illustrated better calibration and substantial improvement in DCA at threshold above 10%, compared with PSA or mpMRI derivatives. The PCa model with a 30% cutoff or CSPCa model with a 20% cutoff could spare the number of biopsies by 53%, and avoid the number of benign biopsies over 80%, while keeping a 95% sensitivity for detecting CSPCa.

Conclusion

Our multivariable models could reduce unnecessary biopsy without comprising the ability to diagnose CSPCa. Further prospective validation is required.

Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy

Background

To determine the added value of preoperative prostate multiparametric MRI (mpMRI) supplementary to clinical variables and their role in predicting post prostatectomy adverse findings and biochemically recurrent cancer (BCR).

Methods

All consecutive patients treated at HUS Helsinki University Hospital with robot assisted radical prostatectomy (RALP) between 2014 and 2015 were included in the analysis. The mpMRI data, clinical variables, histopathological characteristics, and follow-up information were collected. Study end-points were adverse RALP findings: extraprostatic extension, seminal vesicle invasion, lymph node involvement, and BCR. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, Cancer of the Prostate Risk Assessment (CAPRA) score and the Partin score were combined with any adverse findings at mpMRI. Predictive accuracy for adverse RALP findings by the regression models was estimated before and after the addition of MRI results. Logistic regression, area under curve (AUC), decision curve analyses, Kaplan-Meier survival curves and Cox proportional hazard models were used.

Results

Preoperative mpMRI data from 387 patients were available for analysis. Clinical variables alone, MSKCC nomogram or Partin tables were outperformed by models with mpMRI for the prediction of any adverse finding at RP. AUC for clinical parameters versus clinical parameters and mpMRI variables were 0.77 versus 0.82 for any adverse finding. For MSKCC nomogram versus MSKCC nomogram and mpMRI variables the AUCs were 0.71 and 0.78 for any adverse finding. For Partin tables versus Partin tables and mpMRI variables the AUCs were 0.62 and 0.73 for any adverse finding. In survival analysis, mpMRI-projected adverse RP findings stratify CAPRA and MSKCC high-risk patients into groups with distinct probability for BCR.

Conclusions

Preoperative mpMRI improves the predictive value of commonly used clinical variables for pathological stage at RP and time to BCR. mpMRI is available for risk stratification prebiopsy, and should be considered as additional source of information to the standard predictive nomograms.

Multicenter analysis of clinical and MRI characteristics associated with detecting clinically significant prostate cancer in PI-RADS (v2.0) category 3 lesions

OBJECTIVES

We sought to identify clinical and magnetic resonance imaging (MRI) characteristics in men with the Prostate Imaging - Reporting and Data System (PI-RADS) category 3 index lesions that predict clinically significant prostate cancer (CaP) on MRI targeted biopsy.

MATERIALS AND METHODS

Multicenter study of prospectively collected data for biopsy-naive men (n = 247) who underwent MRI-targeted and systematic biopsies for PI-RADS 3 index lesions. The primary endpoint was diagnosis of clinically significant CaP (Grade Group ≥2). Multivariable logistic regression models assessed for factors associated with clinically significant CaP. The probability distributions of clinically significant CaP based on different levels of predictors of multivariable models were plotted in a heatmap.

RESULTS

Men with clinically significant CaP had smaller prostate volume (39.20 vs. 55.10 ml, P < 0.001) and lower apparent diffusion coefficient (ADC) values (973 vs. 1068 μm2/s, P = 0.013), but higher prostate-specific antigen (PSA) density (0.21 vs. 0.13 ng/ml2, P = 0.027). On multivariable analyses, lower prostate volume (odds ratio [OR]: 0.95, 95% confidence interval [CI]: 0.92-0.97), lower ADC value (OR: 0.99, 95% CI: 0.99-1.00), and Prostate-specific antigen density >0.15 ng/ml2 (OR: 3.51, 95% CI 1.61-7.68) were independently associated with significant CaP.

CONCLUSION

Higher PSA density, lower prostate volume and ADC values are associated with clinically significant CaP in biopsy-naïve men with PI-RADS 3 lesions. We present regression-derived probabilities of detecting clinically significant CaP based on various clinical and imaging values that can be used in decision-making. Our findings demonstrate an opportunity for MRI refinement or biomarker discovery to improve risk stratification for PI-RADS 3 lesions.