Using clinical parameters to predict prostate cancer and reduce the unnecessary biopsy among patients with PSA in the gray zone

Transrectal versus transperineal prostate biopsy for cancer detection in patients with gray-zone prostate-specific antigen: a multicenter, real-world study

Knowledge about the effect of different prostate biopsy approaches on the prostate cancer detection rate (CDR) in patients with gray-zone prostate-specific antigen (PSA) is limited. We performed this study to compare the CDR among patients who underwent different biopsy approaches and had rising PSA levels in the gray zone. Two hundred and twenty-two patients who underwent transrectal prostate biopsy (TRB) and 216 patients who underwent transperineal prostate biopsy (TPB) between June 2016 and September 2022 were reviewed in this study. In addition, 110 patients who received additional targeted biopsies following the systematic TPB were identified. Clinical parameters, including age, PSA derivative, prostate volume (PV), and needle core count, were recorded. The data were fitted via propensity score matching (PSM), adjusting for potential confounders. TPB outperformed TRB in terms of the CDR (49.6% vs 28.3%, P = 0.001). The clinically significant prostate cancer (csPCa) detection rate was not significantly different between TPB and TRB (78.6% vs 68.8%, P = 0.306). In stratified analysis, TPB outperformed TRB in CDR when the age of patients was 65-75 years (59.0% vs 22.0%, P < 0.001), when PV was 25.00-50.00 ml (63.2% vs 28.3%, P < 0.001), and when needle core count was no more than 12 (58.5% vs 31.5%, P = 0.005). The CDR ( P = 0.712) and detection rate of csPCa ( P = 0.993) did not significantly differ among the systematic, targeted, and combined biopsies. TPB outperformed TRB in CDR for patients with gray-zone PSA. Moreover, performing target biopsy after systematic TPB provided no additional benefits in CDR.

Diagnostic value of 18FPSMA1007 PET/CT combined with prostate specific antigen derived indicators in gray area prostate cancer

OBJECTIVES

The incidence of prostate cancer is increasing every year, and precision diagnosis and treatment can help reduce unnecessary prostate punctures for prostate cancer patients in the gray area. This study aims to investigate the diagnostic value of 18F-prostate specific membrane antigen (PSMA) imaging combined with prostate specific antigen (PSA)-derived indicators for gray zone prostate cancer.

METHODS

A total of 107 patients who underwent 18F-PSMA PET/CT imaging for suspicious prostate cancer with tPSA of 4 to 10 μg/L (PSA gray zone) in a hospital were retrospectively included, and were divided into a prostate cancer group and a non-prostate cancer group based on pathological findings. Patients underwent PSA testing, 18F-PSMA, and abdominal ultrasound, and age, tPSA, fPSA, f/tPSA, prostate volume, PSA density (PSAD), maximum standardized uptake value (SUVmax), and molecular imaging prostate specific membrane antigen (miPSMA) score were compared between the 2 groups. Multivariate logistic regression was used to analyze the influencing factors the diagnosis of gray zone prostate cancer. Receiver operating characteristic (ROC) curves were constructed to evaluate the efficacy of PSAD and SUVmax alone and in combination in diagnosing gray zone prostate cancer.

RESULTS

The volume of the prostate cancer group [42.00(34.00, 58.00) cm3 vs 49.00(41.27, 60.41) cm3] was smaller than that of the non-prostate cancer group (Z=-2.376, P=0.017), and the PSAD [(0.18±0.06) μg/(L·cm3) vs 0.15±0.05 μg/(L·cm3)] and SUVmax [18.63(8.03, 28.57) vs 9.33(5.90, 13.52)] were higher than those in the non-prostate cancer group (both P<0.05). The percentage of miPSMA score ≥2 in the prostate cancer group was higher than that in the non-prostate cancer group (χ2=40.987, P<0.001). PSAD (OR=22.154, 95% CI 1.430 to 873.751, P=0.042) and SUVmax (OR=1.301, 95% CI 1.034 to 1.678, P=0.009) were independent influential factors for the diagnosis of prostate cancer in the gray zone. The optimal cut-off values of PSAD and SUVmax were 0.22 μg/(L·cm3) and 8.02, respectively, and the AUCs for the diagnosis of prostate cancer in the gray zone alone and in combination were 0.628 (95% CI 0.530 to 0.720, P<0.05) and 0.806 (95% CI 0.718 to 0.876, P<0.05), 0.847 (95% CI 0.765 to 0.910, P<0.05), with sensitivities of 41.03%, 76.92%, and 74.36% and specificities of 79.41%, 89.71%, and 92.65%, respectively.

CONCLUSIONS

PSAD and SUVmax are increased in patients with gray zone prostate cancer, and the combination of PSAD and SUVmax is of high value in diagnosing gray zone prostate cancer.

Value of serum free prostate-specific antigen density in the diagnosis of prostate cancer

PURPOSE

To investigate the value of serum free prostate-specific antigen density (fPSAD) in the diagnosis of prostate cancer (PCa).

METHODS

The data of 558 patients who underwent transrectal ultrasound-guided prostate biopsy were retrospectively analyzed. According to the pathological results, the patients were divided into a PCa group and a benign prostatic hyperplasia (BPH) group. Receiver operating characteristic curves were plotted, based on which the sensitivity, specificity, Youden index, concordance, and kappa values of free prostate-specific antigen (fPSA), the free-to-total f/tPSA, prostate-specific antigen density (PSAD), the free-to-total (f/t)/PSAD ratio, and fPSAD were compared. The patients were divided into three groups by PSA levels (PSA < 4 ng/mL, PSA = 4-10 ng/mL, and PSA > 10 ng/mL), into three groups by age (age < 60 year, age = 60-80y, and age > 80 years), and into two groups by prostate volume (PV) (PV ≤ 80 mL and PV > 80 mL) to compare the sensitivity, specificity, and concordance of indicators.

RESULTS

tPSA, PSAD, (f/t)/PSAD, and fPSAD had high accuracy in predicting PCa with AUC values of 0.820, 0.900, 0.846, and 0.867. fPSAD showed lower diagnostic sensitivity but significantly higher specificity and concordance for PCa than tPSA, f/tPSA, (f/t)/PSAD, or PSAD. Thus, fPSAD had the highest accuracy in the diagnosis of PCa. In the groups with different PSA, age, and PV stratification, the concordance of fPSAD was significantly higher (88.61%, 90.74%, and 90.38%) than that of other indicators.

CONCLUSION

With the optimal cutoff value of 0.062, fPSAD has a higher diagnostic value for PCa than tPSA, f/tPSA, (f/t)/PSAD, and PSAD, and can well predict the risk of PCa, significantly improve the clinical diagnostic rate of PCa, and reduce unnecessary biopsy.

Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI

BACKGROUND

Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL.

PURPOSE

To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa.

STUDY TYPE

Retrospective.

POPULATION

One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets.

FIELD STRENGTH/SEQUENCE

DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T.

ASSESSMENT

The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level.

STATISTICAL TESTS

The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant.

RESULTS

The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863).

DATA CONCLUSION

The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Economic evaluation of prostate cancer risk assessment methods: A cost-effectiveness analysis using population data

BACKGROUND

The current prostate cancer (PCa) screening standard of care (SOC) leads to unnecessary biopsies and overtreatment because decisions are guided by prostate-specific antigen (PSA) levels, which have low specificity in the gray zone (3-10 ng/mL). New risk assessment tools (RATs) aim to improve biopsy decision-making. We constructed a modeling framework to assess new RATs in men with gray zone PSA from the British Columbia healthcare system's perspective.

METHODS

We evaluated the cost-effectiveness of a new RAT used in biopsy-naïve men aged 50+ with a PSA of 3-10 ng/mL using a time-dependent state-transition model. The model was informed by engaging patient partners and using linked administrative health data, supplemented with published literature. The incremental cost-effectiveness ratio and the probability of the RAT being cost-effective were calculated. Probabilistic analysis was used to assess parameter uncertainty.

RESULTS

In the base case, a RAT based on an existing biomarker's characteristics was a dominant strategy associated with a cost savings of $44 and a quality-adjusted life years (QALY) gain of 0.00253 over 18 years of follow-up. At a cost-effectiveness threshold of $50,000/QALY, the probability that using a RAT is cost-effective relative to the SOC was 73%. Outcomes were sensitive to RAT costs and accuracy, especially the detection rate of high-grade PCa. Results were also impacted by PCa prevalence and assumptions about undetected PCa survival.

CONCLUSIONS

Our findings showed that a more accurate RAT to guide biopsy can be cost-effective. Our proposed general model can be used to analyze the cost-effectiveness of any novel RAT.

COVID-19 pandemic impact on screening and diagnosis of prostate cancer: a systematic review

INTRODUCTION

The healthcare level has been greatly affected by the COVID-19 pandemic compared with before the outbreak. This study aimed to review the impact of COVID-19 on the screening and diagnosis of prostate cancer (PCa).

METHOD

The current study was designed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020. The keywords used to perform the search strategy were COVID-19 and prostate neoplasms. The four primary electronic databases comprising PubMed/MEDLINE, Web of Science, Scopus and Embase were searched until 1 September 2022. After screening and selecting studies through the EndNote software, data were extracted from each included study by two independent authors. All studies were evaluated according to Newcastle-Ottawa Scale quality assessment tool.

RESULTS

As a result, 40 studies were included, categorised into two subjects. The majority of studies indicated a significant decrease in screening prostate-specific antibody tests during the COVID-19 pandemic compared with the pre-pandemic period, leading to delays in cancer diagnosis. The decrease in the number of diagnosed cases with low/intermediate stages to some extent was more than those with advanced stages. The PCa screening and diagnosis reduction ranged from nearly 0% to 78% and from 4.1% to 71.7%, respectively.

CONCLUSION

Our findings showed that during the COVID-19 lockdown, delays in PCa screening tests and diagnoses led to the negative health effects on patients with PCa. Thus, it is highly recommended performing regular cancer screening to reduce the impact of the COVID-19 lockdown.

PROSPERO REGISTRATION NUMBER

CRD42021291656.

PI-RADS 3 lesions: a critical review and discussion of how to improve management

Since the publication of PI-RADS v1 in 2012, the debate regarding the question of how to manage PI-RADS 3 lesions has been mostly unsolved. However, based on our review of the current literature we discuss possible solutions and improvements to the original classification, factors such as PSAD (Prostate Specific Antigen Density), age, and tumor volume, in the decision of whether to proceed with a biopsy or not.

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.

Health Care Costs Attributable to Prostate Cancer in British Columbia, Canada: A Population-Based Cohort Study

We aimed to estimate the total health care costs attributable to prostate cancer (PCa) during care phases by age, cancer stage, tumor grade, and primary treatment in the first year in British Columbia (BC), Canada. Using linked administrative health data, we followed a cohort of men aged ≥ 50 years at diagnosis with PCa between 2010 and 2017 (Cohort 1) from the diagnosis date until the date of death, the last date of observation, or 31 December 2019. Patients who died from PCa after 1 January 2010, were selected for Cohort 2. PCa attributable costs were estimated by comparing costs in patients to matched controls. Cohort 1 (n = 22,672) had a mean age of 69.9 years (SD = 8.9) and a median follow-up time of 5.2 years. Cohort 2 included 6942 patients. Mean PCa attributable costs were the highest during the first year after diagnosis ($14,307.9 [95% CI: $13,970.0, $14,645.8]) and the year before death ($9959.7 [$8738.8, $11,181.0]). Primary treatment with radiation therapy had significantly higher costs each year after diagnosis than a radical prostatectomy or other surgeries in advanced-stage PCa. Androgen deprivation therapy (and/or chemotherapy) had the highest cost for high-grade and early-stage cancer during the three years after diagnosis. No treatment group had the lowest cost. Updated cost estimates could inform economic evaluations and decision-making.

Serum organic acid metabolites can be used as potential biomarkers to identify prostatitis, benign prostatic hyperplasia, and prostate cancer

Background

Noninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa) are current clinical challenges.

Methods

The serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS). In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort.

Results

Organic acid metabolites had good sensitivity and specificity in differentiating prostatitis, BPH, and PCa. Three diagnostic models identified patients with PROSTATITIS: phenyllactic acid (area under the curve [AUC]=0.773), pyroglutamic acid (AUC=0.725), and pantothenic acid (AUC=0.721). Three diagnostic models identified BPH: citric acid (AUC=0.859), malic acid (AUC=0.820), and D-glucuronic acid (AUC=0.810). Four diagnostic models identified PCa: 3-hydroxy-3-methylglutaric acid (AUC=0.804), citric acid (AUC=0.918), malic acid (AUC=0.862), and phenyllactic acid (AUC=0.713). Two diagnostic models distinguished BPH from PCa: phenyllactic acid (AUC=0.769) and pyroglutamic acid (AUC=0.761). Three diagnostic models distinguished benign BPH from PROSTATITIS: citric acid (AUC=0.842), ethylmalonic acid (AUC=0.814), and hippuric acid (AUC=0.733). Six diagnostic models distinguished BPH from prostatitis: citric acid (AUC=0.926), pyroglutamic acid (AUC=0.864), phenyllactic acid (AUC=0.850), ethylmalonic acid (AUC=0.843), 3-hydroxy-3-methylglutaric acid (AUC=0.817), and hippuric acid (AUC=0.791). Three diagnostic models distinguished PCa patients with PROSTATITISA < 4.0 ng/mL from those with PSA > 4.0 ng/mL: 5-hydromethyl-2-furoic acid (AUC=0.749), ethylmalonic acid (AUC=0.750), and pyroglutamic acid (AUC=0.929). Conclusions: These results suggest that serum organic acid metabolites can be used as biomarkers to differentiate prostatitis, BPH, and PCa.

Hospital-based prostate cancer screening in vietnamese men with lower urinary tract symptoms: a classification and regression tree model

Background

Prostate cancer (PCa) is a common disease in men over 65 years of age, and should be detected early, while reducing unnecessary biopsies. This study aims to construct a classification and regression tree (CART) model (i.e., risk stratification algorithm) using multivariable approach to select Vietnamese men with lower urinary tract symptoms (LUTS) for PCa biopsy.

Methods

We conducted a case-control study on 260 men aged ≥ 50 years who visited MEDIC Medical Center, Vietnam in 2017–2018 with self-reported LUTS. The case group included patients with a positive biopsy and the control group included patients with a negative biopsy diagnosis of PCa. Bayesian Model Averaging (BMA) was used for selecting the most parsimonious prediction model. Then the CART with 5-fold cross-validation was constructed for selecting men who can benefit from PCa biopsy in steps by steps and intuitive way.

Results

BMA suggested five potential prediction models, in which the most parsimonious model including PSA, I-PSS, and age. CART advised the following cut-off points in the marked screening sequence: 18 < PSA < 33.5 ng/mL, I-PSS ≥ 19, and age ≥ 71. Patients with PSA ≥ 33.5 ng/mL have a PCa risk was 91.2%; patients with PSA < 18 ng/mL and I-PSS < 19 have a PCa risk was 7.1%. Patient with 18 ≤ PSA < 33.5ng/mL and I-PSS < 19 have a PCa risk is 70% if age ≥ 71; and is 16% if age < 71. In overall, CART reached high predictive value with AUC = 0.915. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CART at the 20% diagnosis probability threshold were 91.5%, 86.2%, 86.9%, 91.2%, and 88.9% respectively; at 80% diagnosis probability threshold were 79.2%, 92.3%, 91.2%, 81.6%, and 85.8% respectively.

Conclusion

CART combining PSA, I-PSS, and age has practical use in hospital-based PCa screening in Vietnamese men with lower urinary tract symptoms.

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.

On an Affordable Approach towards the Diagnosis and Care for Prostate Cancer Patients Using Urine, FTIR and Prediction Machines

Prostate cancer is a widespread form of cancer that affects patients globally and is challenging to diagnose, especially in its early stages. The common means of diagnosing cancer involve mostly invasive methods, such as the use of patient’s blood as well as digital biopsies, which are relatively expensive and require a considerable amount of expertise. Studies have shown that various cancer biomarkers can be present in urine samples from patients who have prostate cancers; this paper aimed to leverage this information and investigate this further by using urine samples from a group of patients alongside FTIR analysis for the prediction of prostate cancer. This investigation was carried out using three sets of data where all spectra were preprocessed with the linear series decomposition learner (LSDL) and post-processed using signal processing methods alongside a contrast across nine machine-learning models, the results of which showcased that the proposed modeling approach carries potential to be used for clinical prediction of prostate cancer. This would allow for a much more affordable and high-throughput means for active prediction and associated care for patients with prostate cancer. Further investigations on the prediction of cancer stage (i.e., early or late stage) were carried out, where high prediction accuracy was obtained across the various metrics that were investigated, further showing the promise and capability of urine sample analysis alongside the proposed and presented modeling approaches.

Pre-diagnosis urine exosomal RNA (ExoDx EPI score) is associated with post-prostatectomy pathology outcome

PURPOSE

ExoDx Prostate IntelliScore (EPI) is a non-invasive urine exosome RNA-based test for risk assessment of high-grade prostate cancer. We evaluated the association of pre-biopsy test results with post-radical prostatectomy (RP) outcomes to understand the potential utility of EPI to inform invasive treatment vs active surveillance (AS) decisions.

METHODS

Urine samples were collected from 2066 men scheduled for initial biopsy with PSA between 2 and 10 ng/mL, no history of prostate cancer, and ≥ 50 years across multiple clinical studies. 310 men proceeded to RP, of which 111 patients had Gleason group grade 1 (GG1) at biopsy and would have been potential candidates for AS. We compared pre-biopsy urine scores with ERSPC and PCPT multivariate risk calculator scores for men with GG1 at biopsy to post-RP pathology.

RESULTS

Urine EPI scores were significantly lower in men with GG1 at biopsy than in men with > GG1 (p = 0.04), while there were no differences in multivariate risk scores used in standard clinical practice (p > 0.05). Further, EPI scores were significantly lower in men with GG1 at biopsy who remained GG1 post-RP compared to men upgraded to ≥ GG3 post-RP (p < 0.001). In contrast, none of the multiparametric risk calculators showed significant differences (p > 0.05). Men with GG1 at biopsy and EPI score < 15.6 had zero rate of upgrading to ≥ GG3 post-RP compared to 16.0% for EPI scores ≥ 15.6.

CONCLUSIONS

The EPI urine biomarker outperformed the multivariate risk calculators in a homogenous risk group of pre-biopsy men. The EPI score was associated with low-risk pathology post-RP, with potential implications on informing AS decisions.

TRIAL REGISTRATION

NCT02702856, NCT03031418, NCT03235687, NCT04720599.

Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data

Objectives

Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.

Materials and Methods

The data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed.

Results

The results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.

Conclusions

In this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

Evaluating Incidence, Location, and Predictors of Positive Surgical Margin Among Chinese Men Undergoing Robot-Assisted Radical Prostatectomy

Purpose

To evaluate the incidence and locations of positive surgical margin (PSM) among Chinese men undergoing RARP and identify the preoperative predictors for PSM.

Methods

We retrospectively identified 393 patients who underwent RARP according to inclusion criteria by single surgeon in our hospital. PSM was defined as the presence of cancer adjacent to inked surface of the specimen and categorized into four groups based on locations: apex, posterolateral, base, and multifocal. Logistic regression analysis was performed to identify the predictors of overall and location-specific PSM.

Results

The overall PSM rate was 133/393 (34%). The PSM rates for pT2, pT3, and pT4 stage were 63/278 (23%), 50/89 (56%), and 20/26 (77%), respectively. The estimated rates for apical, posterolateral, basal, and multifocal PSM were 8%, 4%, 7%, and 14%, respectively. In univariate analysis, overall PSM related to tPSA, f/tPSA, percentage of positive needles, and Gleason score. Multifocal PSM correlated with smoking history, drinking history, tPSA, f/tPSA, percentage of positive needles, and Gleason score. In multivariate analysis, percentage of positive needles reminded the only independent predictor for overall (OR = 10.5, 95% CI: 2.58–44.4) and basal PSM (OR = 24.0, 95% CI: 3.22–179.4). The f/tPSA (OR = 2.59, 95% CI: 2.18–5.71) and percentage of positive needles (OR = 31.0, 95% CI: 3.17–303) were independent risk factors for multifocal PSM.

Conclusion

The multifocal sites were the most common location of positive surgical margin, followed by apical and basal sites among Chinese patients undergoing RARP. The percentage of positive needles was an independent predictor for overall, basal, and multifocal PSM.

Selective Microfluidic Capture and Detection of Prostate Cancer Cells from Urine without Digital Rectal Examination

Simple Summary

Prostate cancer is the second most common cancer and the fifth leading cause of cancer death in men worldwide. The current diagnosis methods for prostate cancer are invasive and costly. In particular, digital rectal examination (DRE) or prostate massage adds considerable discomfort to patients, reduces compliance to cancer screening schedules, and raises the cost of the diagnostic procedure. New technologies are urgently needed for the effective and yet noninvasive detection of these conditions. This manuscript describes streamlined biotechnology for the noninvasive detection of prostate cancer from malignant cells shed in urine. For the first time, a whole-cell immunocapture approach combined with photodynamic diagnostic principles is used in a device to detect whole cancer cells from unprocessed patient urine samples collected without prior DRE.

Abstract

Urine-based biomarkers have shown suitable diagnostic potential for prostate cancer (PCa) detection. Yet, until now, prostatic massage remains required prior to urine sampling. Here, we test a potential diagnostic approach using voided urine collected without prior digital rectal examination (DRE). In this study, we evaluated the diagnostic performance of a microfluidic-based platform that combines the principle of photodynamic diagnostic with immunocapture for the detection of PCa cells. The functionality and sensitivity of this platform were validated using both cultured cells and PCa patient urine samples. Quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) demonstrated this platform had a detection limit of fewer than 10 cells per 60 µL and successfully validated the presence of a PCa biomarker in the urine of cancer patients without prior DRE. This biosensing platform exhibits a sensitivity of 72.4% and a specificity of 71.4%, in suitable agreement with qRT-PCR data. The results of this study constitute a stepping stone in the future development of noninvasive prostate cancer diagnostic technologies that do not require DRE.

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 affinity peptide-antibody sandwich electrochemical biosensor for PSA based on the signal amplification of MnO2-functionalized covalent organic framework

This work describes a novel affinity peptide-antibody sandwich electrochemical strategy for the ultrasensitive detection of prostate-specific antigen (PSA). Herein, polydopamine-coated boron-doped carbon nitride (Au@PDA@BCN) was synthesized and used as a sensing platform to anchor gold nanoparticles and immobilize primary antibody. Meanwhile, AuPt metallic nanoparticle and manganese dioxide (MnO₂)-functionalized covalent organic frameworks (AuPt@MnO₂@COF) was facilely synthesized to serve as a nanocatalyst and ordered nanopore for the enrichment and amplification of signal molecules (methylene blue, MB). PSA affinity peptide was bound to AuPt@MnO₂@COF to form Pep/MB/AuPt@MnO₂@COF nanocomposites (probe). The peptide-PSA-antibody sandwich biosensor was constructed, and the redox signal of MB was measured with the existence of PSA. The fabricated sensor exhibited a linear response (0.00005-10 ng mL^-1) with a low detection limit of 16.7 fg mL^-1 under the optimum condition. Additionally, the sensor showed an excellent selectivity, ideal repeatability, and good stability for PSA detection in real samples. Furthermore, the porous structure of COF can enrich more MB molecules and increase the sensitivity of the biosensor. This study provides an efficient and ultrasensitive strategy for PSA detection and broadens the use of organic/inorganic porous nanocomposite in biosensing.

A multicenter retrospective study on evaluation of predicative factors for positive biopsy of prostate cancer in real-world setting

OBJECTIVE

This study aimed to evaluate the predictors for positive biopsy in prostate cancer (PCa) patients and develop a risk-stratification score model for positive biopsy rate in patients with prostate specific antigen (PSA) in the gray zone.

METHODS

In this retrospective, multicenter, real-world study, Chinese patients receiving prostate biopsy for the first time were included. The study evaluated the positive biopsy rate, predictors for positive biopsy and a risk prediction model for PSA 4-10 ng/mL PCa was developed. The univariate and multivariate logistic regression analyses were used to identify the risk factors.

RESULTS

A total of 2426 patients were included in the study. The biopsy positive rate was 47.57%, 25.77%, and 60.57% among overall patients, total PSA (t-PSA) 4-10 ng/mL patients, and PSA > 10 ng/mL patients respectively. Elderly age 60-74, ≥75, multi parametric magnetic resonance imaging (MP-MRI), pre-operative PSA > 10 and PSA density (PSAD) significantly increased the positive rate in overall population, and elderly age, MP-MRI, positive digital rectal examination and f-PSA were significant predictors for positive biopsy in PSA 4-10 ng/mL population. A risk prediction model for positive biopsy rate in patients with PSA in the gray zone was developed. Area under curve (AUC) was associated with low accuracy for all the variables used such as tPSA (0.53), PSAD (0.57), frequency of puncture (0.53) and MP-MRI (0.64) in prediction of biopsy positive rate.

CONCLUSION

Our study evaluated the significant predicative factors for positive biopsy and the PCa risk prediction model developed might help Clinicians to avoid unnecessary biopsy in patients with PSA in gray zone.

Detection of Prostate Cancer via IR Spectroscopic Analysis of Urinary Extracellular Vesicles: A Pilot Study

Extracellular vesicles (EVs) are membranous nanoparticles naturally released from living cells which can be found in all types of body fluids. Recent studies found that cancer cells secreted EVs containing the unique set of biomolecules, which give rise to a distinctive absorbance spectrum representing its cancer type. In this study, we aimed to detect the medium EVs (200–300 nm) from the urine of prostate cancer patients using Fourier transform infrared (FTIR) spectroscopy and determine their association with cancer progression. EVs extracted from 53 urine samples from patients suspected of prostate cancer were analyzed and their FTIR spectra were preprocessed for analysis. Characterization of morphology, particle size and marker proteins confirmed that EVs were successfully isolated from urine samples. Principal component analysis (PCA) of the EV’s spectra showed the model could discriminate prostate cancer with a sensitivity of 59% and a specificity of 81%. The area under curve (AUC) of FTIR PCA model for prostate cancer detection in the cases with 4–20 ng/mL PSA was 0.7, while the AUC for PSA alone was 0.437, suggesting the analysis of urinary EVs described in this study may offer a novel strategy for the development of a noninvasive additional test for prostate cancer screening.

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Background

Two diagnostic models of prostate cancer (PCa) and clinically significant prostate cancer (CS-PCa) were established using clinical data of among patients whose prostate-specific antigen (PSA) levels are in the gray area (4.0–10.0 ng/ml).

Material/Methods

Data from 181 patients whose PSA levels were in the gray area were retrospectively analyzed, and the following data were collected: age, digital rectal examination, total PSA, PSA density (PSAD), free/total PSA (f/t PSA), transrectal ultrasound, multiparametric magnetic resonance imaging (mpMRI), and pathological reports. Patients were diagnosed with benign prostatic hyperplasia (BPH) and PCa by pathology reports, and PCa patients were separated into non-clinically significant PCa (NCS-PCa) and CS-PCa by Gleason score. Afterward, predictor models constructed by above parameters were researched to diagnose PCa and CS-PCa, respectively.

Results

According to the analysis of included clinical data, there were 109 patients with BPH, 44 patients with NCS-PCa, and 28 patients with CS-PCa. Regression analysis showed PCa was correlated with f/t PSA, PSAD, and mpMRI (P<0.01), and CS-PCa was correlated with PSAD and mpMRI (P<0.01). The area under the receiver operating characteristic curves of 2 models for PCa (sensitivity=73.64%, specificity=64.23%) and for CS-PCa (sensitivity=71.41%, specificity=81.82%) were 0.79 and 0.87, respectively.

Conclusions

The prediction models had satisfactory diagnostic value for PCa and CS-PCa among patients with PSA in the gray area, and use of these models may help reduce overdiagnosis.

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.

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.