Risk Factors and Contemporary Management Options for Pain and Discomfort Experienced During a Prostate Biopsy.

PURPOSE OF REVIEW: Prostate fusion biopsy, an innovative imaging modality for diagnosing prostate cancer, presents certain challenges for patients including discomfort and emotional distress, leading to nonadherence to treatment and follow-ups. To inform clinicians and offer pain relief alternatives to patients, this review delves into the risk factors for increased pain and modern management options to alleviate pain during prostate biopsy. RECENT FINDINGS: Individual responses to pain vary, and the overall experience of pain during a prostate biopsy has been contributed to numerous factors such as patient age, prostate volume, previous biopsy experience, and more. As a result, several strategies aim to mitigate pain during in-office procedures. Notably, techniques including pharmacological analgesics, hand holding, heating pads, entertainment/virtual reality, and distraction have shown significant efficacy. Existing studies explore risk factors influencing pain intensity during prostate biopsy and effective pain management strategies. This review consolidates available information to guide clinicians in enhancing patient comfort and thus, encourage surveillance adherence.

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage.

Importance: Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood. Objective: To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US. Design, Setting, and Participants: In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024. Exposure: Diagnosis of prostate cancer. Main Outcomes and Measures: The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard. Results: A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy. Conclusions and Relevance: This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology.

OBJECTIVE: To compute a dense prostate cancer risk map for the individual patient post-biopsy from magnetic resonance imaging (MRI) and to provide a more reliable evaluation of its fitness in prostate regions that were not identified as suspicious for cancer by a human-reader in pre- and intra-biopsy imaging analysis. METHODS: Low-level pre-biopsy MRI biomarkers from targeted and non-targeted biopsy locations were extracted and statistically tested for representativeness against biomarkers from non-biopsied prostate regions. A probabilistic machine learning classifier was optimized to map biomarkers to their core-level pathology, followed by extrapolation of pathology scores to non-biopsied prostate regions. Goodness-of-fit was assessed at targeted and non-targeted biopsy locations for the post-biopsy individual patient. RESULTS: Our experiments showed high predictability of imaging biomarkers in differentiating histopathology scores in thousands of non-targeted core-biopsy locations (ROC-AUCs: 0.85-0.88), but also high variability between patients (Median ROC-AUC [IQR]: 0.81-0.89 [0.29-0.40]). CONCLUSION: The sparseness of prostate biopsy data makes the validation of a whole gland risk mapping a non-trivial task. Previous studies i) focused on targeted-biopsy locations although biopsy-specimens drawn from systematically scattered locations across the prostate constitute a more representative sample to non-biopsied regions, and ii) estimated prediction-power across predicted instances (e.g., biopsy specimens) with no patient distinction, which may lead to unreliable estimation of model fitness to the individual patient due to variation between patients in instance count, imaging characteristics, and pathologies. SIGNIFICANCE: This study proposes a personalized whole-gland prostate cancer risk mapping post-biopsy to allow clinicians to better stage and personalize focal therapy treatment plans.

Cytologic evaluation of upper urinary tract specimens: An institutional retrospective study using The Paris System for Reporting Urine Cytology second edition with histopathologic follow-up.

OBJECTIVE: Cytologic evaluation of the upper urinary tract (UUT) can be challenging due to instrumentation artefacts. This study retrospectively reviewed UUT specimens using The Paris System for Reporting Urinary Cytopathology, second edition (TPS 2.0), compared it with the original reporting system (ORS) and correlated it with histopathologic follow-up. METHODS: An institutional database was reviewed for the UUT biopsy/resection histopathologic specimens, and we included 52 UUT cytology specimens pertinent to these cases in the study. These specimens were blindly reviewed and reclassified using TPS 2.0. The correlation between TPS 2.0, ORS and histopathologic follow-up was assessed. RESULTS: The UUT cytology specimens corresponded to 21 (40.4%) high-grade urothelial carcinoma (HGUC), 27 (51.9%) low-grade urothelial carcinoma (LGUC) and 4 (7.7%) benign cases on follow-up. For HGGC cases, the associated TPS categories included unsatisfactory (n = 1, 4.8%), negative for HGUC (NHGUC; n = 3, 14.3%), atypical urothelial cells (AUC; n = 6, 28.6%), suspicious for HGUC (SHGUC; n = 3, 14.3%) and HGUC (n = 8, 38.1%), while ORS categorised the specimens as unsatisfactory (n = 1, 4.8%), negative for malignant cells (NFMC; n = 3, 14.3%), AUC (n = 5, 23.8%), low-grade urothelial carcinoma (LGUC; n = 0, 0%), SHGUC (n = 5, 23.8%) and HGUC (n = 7, 33.3%). The risks of high-grade malignancy among cytologic categories were similar between ORS and TPS (p > 0.05). The majority of LGUC were classified as AUC similarly by ORS and TPS (55.6% vs. 59.3%). CONCLUSIONS: Our study demonstrated comparable performance between TPS 2.0 and ORS for UUT cytology specimens. Cytological diagnosis of UUT specimens remains challenging, especially for LGUC.

Association between sociodemographic factors and diagnosis of lethal prostate cancer in early life.

OBJECTIVE: A subset of patients are diagnosed with lethal prostate cancer (CaP) early in life before prostate-specific antigen (PSA) screening is typically initiated. To identify opportunities for improved detection, we evaluated patient sociodemographic factors associated with advanced vs. localized (CaP) diagnosis across the age spectrum. METHODS: We conducted a retrospective cohort study using the National Cancer Database, identifying patients diagnosed with CaP from 2004 to 2020. We compared characteristics of patients diagnosed at the advanced (cN1 or M1) versus localized (cT1-4N0M0) stage. Using multivariable logistic regression, we evaluated the associations among patient clinical and sociodemographic factors and advanced diagnosis, stratifying patients by age as ≤55 (before screening is recommended for most patients), 56 to 65, 66 to 75, and ≥76 years. RESULTS: We identified 977,722 patients who met the inclusion criteria. The mean age at diagnosis was 65.3 years and 50,663 (5.1%) had advanced disease. Overall, uninsured (OR = 3.20, 95% CI 3.03-3.78) and Medicaid-insured (OR 2.58, 95% CI 2.48-2.69) vs. privately insured status was associated with higher odds of diagnosis with advanced disease and this effect was more pronounced for younger patients. Among patients ≤55 years, uninsured (OR 4.14, 95% CI 3.69-4.65) and Medicaid-insured (OR 3.39, 95% CI 3.10-3.72) vs. privately insured patients were associated with higher odds of advanced cancer at diagnosis. Similarly, residence in the lowest vs. highest income quartile was associated with increased odds of advanced CaP in patients ≤55 years (OR 1.15, 95% CI 1.02-1.30). Black vs. White race was associated with increased odds of advanced CaP at diagnosis later in life (OR 1.17, 95% CI 1.09-1.25); however, race was not significantly associated with advanced stage CaP in those ≤55 years (P = 0.635). CONCLUSIONS: Sociodemographic disparities in diagnosis at advanced stages of CaP were more pronounced in younger patients, particularly with respect to insurance status. These findings may support greater attention to differential use of early CaP screening based on patient health insurance.

Prostate cancer risk stratification using magnetic resonance imaging-ultrasound fusion vs systematic prostate biopsy.

BACKGROUND: Image-guided approaches improve the diagnostic yield of prostate biopsy and frequently modify estimates of clinical risk. To better understand the impact of magnetic resonance imaging-ultrasound fusion targeted biopsy (MRF-TB) on risk assessment, we compared the distribution of National Comprehensive Cancer Network (NCCN) risk groupings, as calculated from MRF-TB vs systematic biopsy alone. METHODS: We performed a retrospective analysis of 713 patients who underwent MRF-TB from January 2017 to July 2021. The primary study objective was to compare the distribution of National Comprehensive Cancer Network risk groupings obtained using MRF-TB (systematic + targeted) vs systematic biopsy. RESULTS: Systematic biopsy alone classified 10% of samples as very low risk and 18.7% of samples as low risk, while MRF-TB classified 10.5% of samples as very low risk and 16.1% of samples as low risk. Among patients with benign findings, low-risk disease, and favorable/intermediate-risk disease on systematic biopsy alone, 4.6% of biopsies were reclassified as high risk or very high risk on MRF-TB. Of 207 patients choosing active surveillance, 64 (31%), 91 (44%), 42 (20.2%), and 10 (4.8%) patients were classified as having very low-risk, low-risk, and favorable/intermediate-risk and unfavorable/intermediate-risk criteria, respectively. When using systematic biopsy alone, 204 patients (28.7%) were classified as having either very low-risk and low-risk disease per NCCN guidelines, while 190 men (26.6%) received this classification when using MRF-TB. CONCLUSION: The addition of MRF-TB to systematic biopsy may change eligibility for active surveillance in only a small proportion of patients with prostate cancer. Our findings support the need for routine use of quantitative risk assessment over risk groupings to promote more nuanced decision making for localized cancer.

NCCN Guidelines® Insights: Prostate Cancer Early Detection, Version 1.2023.

The NCCN Guidelines for Prostate Cancer Early Detection provide recommendations for individuals with a prostate who opt to participate in an early detection program after receiving the appropriate counseling on the pros and cons. These NCCN Guidelines Insights provide a summary of recent updates to the NCCN Guidelines with regard to the testing protocol, use of multiparametric MRI, and management of negative biopsy results to optimize the detection of clinically significant prostate cancer and minimize the detection of indolent disease.

Prediction of Adverse Pathology at Radical Prostatectomy in Grade Group 2 and 3 Prostate Biopsies Using Machine Learning.

PURPOSE: There is ongoing clinical need to improve estimates of disease outcome in prostate cancer. Machine learning (ML) approaches to pathologic diagnosis and prognosis are a promising and increasingly used strategy. In this study, we use an ML algorithm for prediction of adverse outcomes at radical prostatectomy (RP) using whole-slide images (WSIs) of prostate biopsies with Grade Group (GG) 2 or 3 disease. METHODS: We performed a retrospective review of prostate biopsies collected at our institution which had corresponding RP, GG 2 or 3 disease one or more cores, and no biopsies with higher than GG 3 disease. A hematoxylin and eosin-stained core needle biopsy from each site with GG 2 or 3 disease was scanned and used as the sole input for the algorithm. The ML pipeline had three phases: image preprocessing, feature extraction, and adverse outcome prediction. First, patches were extracted from each biopsy scan. Subsequently, the pre-trained Visual Geometry Group-16 convolutional neural network was used for feature extraction. A representative feature vector was then used as input to an Extreme Gradient Boosting classifier for predicting the binary adverse outcome. We subsequently assessed patient clinical risk using CAPRA score for comparison with the ML pipeline results. RESULTS: The data set included 361 WSIs from 107 patients (56 with adverse pathology at RP). The area under the receiver operating characteristic curves for the ML classification were 0.72 (95% CI, 0.62 to 0.81), 0.65 (95% CI, 0.53 to 0.79) and 0.89 (95% CI, 0.79 to 1.00) for the entire cohort, and GG 2 and GG 3 patients, respectively, similar to the performance of the CAPRA clinical risk assessment. CONCLUSION: We provide evidence for the potential of ML algorithms to use WSIs of needle core prostate biopsies to estimate clinically relevant prostate cancer outcomes.

Multi-institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI-RADS 3 lesions.

BACKGROUND: Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk fac- tors that predict CSPCa in men with PI-RADS 3 lesions. METHODS: This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model. RESULTS: Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%. CONCLUSIONS: For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas. LAY SUMMARY: Among men with an equivocal lesion (Prostate Imaging-Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate-specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy. However, men with at least one negative biopsy have a lower risk of CSPCa. A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa.

Clinical utility of MR/ultrasound fusion-guided biopsy in patients with lower suspicion lesions on active surveillance for low-risk prostate cancer.

BACKGROUND: The utility of Multiparametric magnetic resonance imaging (mpMRI) guided prostate biopsy among patients with prostate cancer (CaP) managed with active surveillance (AS) with low-suspicion lesions remains unsettled. METHODS: We performed a retrospective analysis of 415 men with low-risk CaP managed with active surveillance. We selected men with mpMRI visible index lesions scored as 2 or 3 according to Prostate Imaging Reporting and Data System (PI-RADS) version 2. The primary outcome was detection of clinically significant prostate cancer (csCaP) was defined as Gleason grade group ≥ 2. We assessed the diagnostic accuracy of biopsy approaches using area under the receiver operator characteristic (ROC) curve and evaluated factors associated with csCaP in these patients using multivariate logistic regression. RESULTS: CsCaP was identified in 22 of 125 patients (17.6%) with PI-RADS 2 or 3 index lesions during surveillance prostate biopsies. These included 10 (45.5%) diagnosed by systematic biopsy alone, 9 (40.9%) by targeted alone, and 3 (13.6%) by both approaches. On multivariable analysis, the only significant variable predicting the detection of csCaP in men with low-risk imaging mpMRI characteristics was higher PSAD (OR per 0.1 unit=2.26, 95% CI 1.25-4.06, P = 0.007. A PSAD cutoff of 0.1, 0.12 and 0.15 resulted in a negative predictive value (NPV) of 90.9%, 87.1% and 86.2%, respectively. When stratified by PI-RADS score, a PSAD cutoff of 0.1, 0.12 and 0.15 resulted in NPV of 96.2%, 90.6% and 89.7% and 86.2%, 84.2% and 83.3% for detection of csCaP in PI-RADS 2 and 3 lesions, respectively. In patients with PIRDAS 2 lesions, using a PSAD of 0.1 would potentially allow 51% of patients to avoid biopsy with only a 3.8% chance of missing csCaP. CONCLUSION: In men with clinical low-risk prostate cancer on active surveillance with PI-RADS 2 and 3 lesions, there is an almost 18% risk of upgrade to csCaP. Integration of PSAD may be a useful adjunctive tool in identifying patients at highest risk for upgrade despite favorable imaging findings. In men with PIRADS 2 lesions with PSAD ≤0.12 biopsy can be avoided. For men with PIRADS 2 lesions with PSAD ≤0.15 informed decision making regarding the AS intensity should include that these patients have a low risk (>10%) of developing csCaP. In men with PIRADS 3 lesions with PSAD >0.1, shared decision making should include discussion of a >10% miss rate of csCaP.

Real-Time MRI-Guided Prostate Interventions.

Prostate cancer (PCa) is the second most common cause of cancer death in males. Targeting MRI-visible lesions has led to an overall increase in the detection of clinically significant PCa compared to the prior practice of random ultrasound-guided biopsy of the prostate. Additionally, advances in MRI-guided minimally invasive focal treatments are providing new options for patients with PCa. This review summarizes the currently utilized real-time MRI-guided interventions for PCa diagnosis and treatment.

Association Between a 22-feature Genomic Classifier and Biopsy Gleason Upgrade During Active Surveillance for Prostate Cancer.

BACKGROUND: Although the Decipher genomic classifier has been validated as a prognostic tool for several prostate cancer endpoints, little is known about its role in assessing the risk of biopsy reclassification for patients on active surveillance, a key event that often triggers treatment. OBJECTIVE: To evaluate the association between Decipher genomic classifier scores and biopsy Gleason upgrading among patients on active surveillance. DESIGN SETTING AND PARTICIPANTS: This was a retrospective cohort study among patients with low- and favorable intermediate-risk prostate cancer on active surveillance who underwent biopsy-based Decipher testing as part of their clinical care. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We evaluated the association between the Decipher score and any increase in biopsy Gleason grade group (GG) using univariable and multivariable logistic regression. We compared the area under the receiver operating characteristic curve (AUC) for models comprising baseline clinical variables with or without the Decipher score. RESULTS AND LIMITATIONS: We identified 133 patients for inclusion with a median age of 67.7 yr and median prostate-specific of 5.6 ng/ml. At enrollment, 75.9% had GG1 and 24.1% had GG2 disease. Forty-three patients experienced biopsy upgrading. On multivariable logistic regression, the Decipher score was significantly associated with biopsy upgrading (odds ratio 1.37 per 0.10 unit increase, 95% confidence interval [CI] 1.05-1.79; p = 0.02). The Decipher score was associated with upgrading among patients with biopsy GG 1 disease, but not GG2 disease. The discriminative ability of a clinical model (AUC 0.63, 95% CI 0.51-0.74) was improved by integration of the Decipher score (AUC 0.69, 95% CI 0.58-0.80). CONCLUSIONS: The Decipher genomic classifier score was associated with short-term biopsy Gleason upgrading among patients on active surveillance. PATIENT SUMMARY: The results from this study indicate that among patients with prostate cancer undergoing active surveillance, those with higher Decipher scores were more likely to have higher-grade disease found over time. These findings indicate that the Decipher test might be useful for guiding the intensity of monitoring during active surveillance, such as more frequent biopsy for patients with higher scores.

Optimal Use of Tumor-Based Molecular Assays for Localized Prostate Cancer.

PURPOSEOF REVIEW: The use of genomic testing for prostate cancer continues to grow; however, utilization remains institutionally dependent. Herein, we review current tissue-based markers and comment on current use with active surveillance and prostate MRI. RECENT FINDINGS: While data continues to emerge, several studies have shown a role for genomic testing for treatment selection. Novel testing options include ConfirmMDx, ProMark, Prolaris, and Decipher, which have shown utility in select patients. The current body of literature on this specific topic remains very limited; prospective trials with long-term follow-up are needed to improve our understanding on how these genomic tests fit when combined with our current clinical tools. As the literature matures, it is likely that newer risk calculators that combine our classic clinical variables with genomic and imaging data will be developed to bring about standard protocols for prostate cancer decision-making.

Extent of High-Grade Prostatic Adenocarcinoma in Multiparametric Magnetic Resonance Imaging-Targeted Biopsy Enhances Prediction of Pathologic Stage.

CONTEXT.­: Multiparametric magnetic resonance imaging (mpMRI) of prostate with targeted biopsy has enhanced detection of high-grade prostatic adenocarcinoma (HG PCa). However, utility of amount of HG PCa (Gleason pattern 4/5) in mpMRI-targeted biopsies versus standard 12-core biopsies in predicting adverse outcomes on radical prostatectomy (RP) is unknown. OBJECTIVE.­: To examine the utility of amount of HG PCa in mpMRI-targeted biopsies versus standard 12-core biopsies in predicting adverse RP outcomes. DESIGN.­: We performed a retrospective review of prostate biopsies that had corresponding RP, 1 or more mpMRI-targeted biopsy, and Grade Group 2 disease or higher. For the 169 cases identified, total millimeters of carcinoma and HG PCa and longest length HG PCa in a single core were recorded for 12-core biopsies and each set of mpMRI-targeted biopsies. For RP specimens, Gleason grade, extraprostatic extension, seminal vesicle involvement, and lymph node metastasis were recorded. The main outcome studied was prostate-confined disease at RP. A logistic regression model was used to test which pre-RP variables related to this outcome. RESULTS.­: Univariate analysis showed significant associations with adverse RP outcomes in 5 of 8 quantifiable variables; longest millimeter HG PCa in a single 12-core biopsy, highest Grade Group in any core, and total millimeter HG in mpMRI-targeted biopsies showed no statistical association (P = .54, P = .13, and P = .55, respectively). In multivariate analysis, total millimeter carcinoma in all cores, highest Grade Group in any core, and longest millimeter HG PCa in a single mpMRI-targeted core provided additional predictive value (P < .001, P = .004, and P = .03, respectively). CONCLUSIONS.­: Quantitation of HG PCa in mpMRI-targeted biopsies provides additional value over 12-core biopsies alone in predicting nonorgan confined prostate cancer at RP. Linear millimeters of HG PCa in mpMRI-targeted biopsies is a significant parameter associated with higher pathologic stage and could be of value in risk models.

Facility-Level Variation in Use of Locoregional Therapy for Metastatic Prostate Cancer.

INTRODUCTION: We sought to understand patient- and institution-level factors associated with use of locoregional therapy for newly diagnosed metastatic prostate cancer in the era before the availability of evidence supporting its efficacy. METHODS: We queried the National Cancer Database to identify patients diagnosed with metastatic prostate adenocarcinoma (stage M1) between 2004 and 2017. We assessed patient factors associated with definitive local therapy with radiotherapy or radical prostatectomy using multilevel logistic regression accounting for clustering within institutions. We further characterized trends in facility-level use and examined institutional factors associated with utilization. RESULTS: We identified 35,933 patients with M1 prostate cancer at 1,188 facilities. A total of 4,146 patients (11.5%) received local therapy for M1 disease (radiation therapy in 3,378 and radical prostatectomy in 768). Use of local treatment was concentrated among a smaller number of facilities: 50% of all local therapy was delivered at 161 facilities (14% of total). At the patient level, uninsured status (OR 0.62, 95% CI 0.49-0.79, p <0.01) and high comorbidity (Charlson-Deyo score, OR 0.39, 95% CI 0.26-0.6, p <0.01) were associated with lower odds of local therapy. High-utilizing facilities (top quartile) were more commonly community centers (OR 1.76, 95% CI 10.7-2.95, p <0.01) and differed by geographic region (South Atlantic vs West South Central region: OR 0.48, 95% CI 0.25-0.88, p=0.02). CONCLUSIONS: In the period before locoregional therapy was supported by clinical practice guidelines, locoregional therapy use varied significantly at the facility level and was driven by a smaller number of high-utilizing facilities. These findings can contextualize expected increase in the use of local therapy for metastatic prostate cancer.

Targeting prostate cancer with Clostridium perfringens enterotoxin functionalized nanoparticles co-encapsulating imaging cargo enhances magnetic resonance imaging specificity.

Magnetic resonance is a key imaging tool for the detection of prostate cancer; however, better tools focusing on cancer specificity are required to distinguish benign from cancerous regions. We found higher expression of claudin-3 (CLDN-3) and -4 (CLDN-4) in higher grade than lower-grade human prostate cancer biopsies (n = 174), leading to the design of functionalized nanoparticles (NPs) with a non-toxic truncated version of the natural ligand Clostridium perfringens enterotoxin (C-CPE) that has a strong binding affinity to Cldn-3 and Cldn-4 receptors. We developed a first-of-its-type, C-CPE-NP-based MRI detection tool in a prostate tumor-bearing mouse model. NPs with an average diameter of 152.9 ±â€¯15.7 nm (RS1) had a 2-fold enhancement of tumor specificity compared to larger (421.2 ±â€¯33.8 nm) NPs (RS4). There was a 1.8-fold (P < 0.01) and 1.6-fold (P < 0.01) upregulation of the tumor-to-liver signal intensities of C-RS1 and C-RS4 (functionalized NPs) compared to controls, respectively. Also, tumor specificity was 3.1-fold higher (P < 0.001) when comparing C-RS1 to C-RS4. This detection tool improved tumor localization of contrast-enhanced MRI, supporting potential clinical applicability.

Changes in Prostate-Specific Antigen Testing Relative to the Revised US Preventive Services Task Force Recommendation on Prostate Cancer Screening.

IMPORTANCE: In April 2017, the US Preventive Services Task Force (USPSTF) published a draft guideline that reversed its 2012 guidance advising against prostate-specific antigen (PSA)-based screening for prostate cancer in all men (grade D), instead endorsing individual decision-making for men aged 55 to 69 years (grade C). OBJECTIVE: To evaluate changes in rates of PSA testing after revisions in the USPSTF guideline on prostate cancer screening. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used deidentified claims data from Blue Cross Blue Shield beneficiaries aged 40 to 89 years from January 1, 2013, through December 31, 2019. EXPOSURES: Publication of the USPSTF's draft (April 2017) and final (May 2018) recommendation on prostate cancer screening. MAIN OUTCOMES AND MEASURES: Age-adjusted rates of PSA testing in bimonthly periods were calculated, and PSA testing rates from calendar years before (January 1 to December 31, 2016) and after (January 1 to December 31, 2019) the guideline change were compared. Interrupted time series analyses were used to evaluate the association of the draft (April 2017) and published (May 2018) USPSTF guideline with rates of PSA testing. Changes in rates of PSA testing were further evaluated among beneficiaries within the age categories reflected in the guideline: 40 to 54 years, 55 to 69 years, and 70 to 89 years. RESULTS: The median number of eligible beneficiaries for each bimonthly period was 8 087 565 (range, 6 407 602-8 747 308), and the median age of all included eligible beneficiaries was 53 years (IQR, 47-59 years). Between 2016 and 2019, the mean (SD) rate of PSA testing increased from 32.5 (1.1) to 36.5 (1.1) tests per 100 person-years, a relative increase of 12.5% (95% CI, 1.1%-24.4%). During the same period, mean (SD) rates of PSA testing increased from 20.6 (0.8) to 22.7 (0.9) tests per 100 person-years among men aged 40 to 54 years (relative increase, 10.1%; 95% CI, -2.8% to 23.7%), from 49.8 (1.9) to 55.8 (1.8) tests per 100 person-years among men aged 55 to 69 years (relative increase, 12.1%; 95% CI, -0.2% to 25.2%), and from 38.0 (1.4) to 44.2 (1.4) tests per 100 person-years among men aged 70 to 89 years (relative increase, 16.2%; 95% CI, 4.2%-29.0%). Interrupted time series analysis revealed a significantly increasing trend of PSA testing after April 2017 among all beneficiaries (0.30 tests per 100 person-years for each bimonthly period; P < .001). CONCLUSIONS AND RELEVANCE: This large national cohort study found that rates of PSA testing increased after the USPSTF's draft statement in 2017, reversing trends seen after earlier guidance against PSA testing for all patients. Increased testing was also observed among older men, who may be less likely to benefit from prostate cancer screening.

Adoption of New Risk Stratification Technologies Within US Hospital Referral Regions and Association With Prostate Cancer Management.

Importance: The clinical decisions that arise from prostate magnetic resonance imaging (MRI) and genomic testing in patients with prostate cancer are not well understood. Objective: To evaluate the association between regional uptake of prostate MRI and genomic testing and observation vs treatment for prostate cancer. Design, Setting, and Participants: This retrospective cohort study of commercial insurance claims for prostate MRI and genomic testing included 65 530 patients 40 to 89 years of age newly diagnosed with prostate cancer from July 1, 2012, through June 30, 2019. Exposures: Patient- and regional-level use of prostate MRI and genomic testing. Main Outcomes and Measures: Observation vs definitive treatment for prostate cancer. Patient-level analyses examined the association between receipt of testing or residing in a hospital referral region (HRR) that adopted testing and observation. In regional-level analyses, the dependent variable was the change in the proportion of patients observed for prostate cancer at the HRR level between 2 periods: July 1, 2012, to June 30, 2014, and July 1, 2017, to June 20, 2019. The independent study variables included HRR-level changes in the proportion of men undergoing prostate MRI and genomic testing between these periods, and the models were adjusted for contextual factors associated with prostate cancer care and socioeconomic status. Results: This study identified 65 530 patients, including 27 679 in the early period (mean [SD] age, 58.0 [5.9] years) and 37 851 in the late period (mean [SD] age, 59.0 [5.7] years). Use of prostate MRI increased significantly from 7.2% (95% CI, 6.9%-7.5%) to 16.7% (95% CI, 16.3%-17.1%) from the early to late period. Use of genomic testing increased significantly from 1.3% (95% CI, 1.1%-1.4%) to 12.7% (95% CI, 12.3%-13.0%) from the early to late period. Compared with the lowest, the highest HRR quartiles of prostate MRI and genomic testing uptake were associated with an adjusted 4.1% (SE, 1.1%; P < .001) and 2.5% (SE, 1.1%; P = .03) absolute increase in the proportion of patients receiving observation, respectively. Conclusions and Relevance: In this cohort study, uptake of prostate MRI and genomic testing was associated with increased use of initial observation vs treatment for prostate cancer. Marked geographic variation supports the need for further patient-level research to optimize the dissemination and outcome of testing.

The stanford prostate cancer calculator: Development and external validation of online nomograms incorporating PIRADS scores to predict clinically significant prostate cancer.

BACKGROUND: While multiparametric MRI (mpMRI) has high sensitivity for detection of clinically significant prostate cancer (CSC), false positives and negatives remain common. Calculators that combine mpMRI with clinical variables can improve cancer risk assessment, while providing more accurate predictions for individual patients. We sought to create and externally validate nomograms incorporating Prostate Imaging Reporting and Data System (PIRADS) scores and clinical data to predict the presence of CSC in men of all biopsy backgrounds. METHODS: Data from 2125 men undergoing mpMRI and MR fusion biopsy from 2014 to 2018 at Stanford, Yale, and UAB were prospectively collected. Clinical data included age, race, PSA, biopsy status, PIRADS scores, and prostate volume. A nomogram predicting detection of CSC on targeted or systematic biopsy was created. RESULTS: Biopsy history, Prostate Specific Antigen (PSA) density, PIRADS score of 4 or 5, Caucasian race, and age were significant independent predictors. Our nomogram-the Stanford Prostate Cancer Calculator (SPCC)-combined these factors in a logistic regression to provide stronger predictive accuracy than PSA density or PIRADS alone. Validation of the SPCC using data from Yale and UAB yielded robust AUC values. CONCLUSIONS: The SPCC combines pre-biopsy mpMRI with clinical data to more accurately predict the probability of CSC in men of all biopsy backgrounds. The SPCC demonstrates strong external generalizability with successful validation in two separate institutions. The calculator is available as a free web-based tool that can direct real-time clinical decision-making.

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

BACKGROUND: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. METHODS: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. RESULTS: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). CONCLUSIONS: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.