Prostate volume index and prostatic chronic inflammation predicted low tumor load in 945 patients at baseline prostate biopsy

A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model

Purpose

This study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).

Methods

We retrospectively analyzed 232 participants from our institution who underwent both B-mode ultrasound and shear wave elastography (SWE) prior to prostate biopsy between June 2022 and December 2023. A random allocation placed the participants into training and test cohorts with a 7:3 distribution. We developed a nomogram that integrates DLR with clinical factors within the training cohort, which was subsequently validated using the test cohort. The diagnostic performance and clinical applicability were evaluated with receiver operating characteristic (ROC) curve analysis and decision curve analysis.

Results

In our study, the bi-parametric ultrasound-based DLR model demonstrated an area under the curve (AUC) of 0.80 (95%CI: 0.70-0.91) in the test set, surpassing the performance of both the radiomics and deep learning models individually. By integrating clinical factors, a composite model, presented as the nomogram, was developed and exhibited superior diagnostic performance, achieving an AUC of 0.87 (95%CI: 0.77-0.95) in the test set. The performance exceeded that of the DLR (P = 0.049) and the clinical model (AUC = 0.79, 95%CI: 0.69-0.86, P = 0.041). Furthermore, the decision curve analysis indicated that the composite model provided a greater net benefit across a various high-risk threshold than the DLR or the clinical model alone.

Conclusion

To our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable.

A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score

PURPOSE

This study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa).

METHODS

The demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed.

RESULTS

The AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model.

CONCLUSION

P.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.

New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3

Purpose

The aim of this study was to explore a new model of clinical decision-making to predict the occurrence of clinically significant prostate cancer (csPCa).

Patients and Methods

The demographic and clinical characteristics of 152 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), adjusted PSAD of peripheral zone (aPSADPZ), and peripheral zone volume ratio (PZ ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves.

Results

The AUCs of PSA, PSAD, aPSADPZ, and PZ ratio were 0.521, 0.645, 0.745, and 0.717 for prostate cancer (PCa) diagnosis, while the corresponding values were 0.590, 0.678, 0.780, and 0.731 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of csPCa. The new model significantly improved the diagnostic accuracy of csPCa (0.865 vs. 0.741, p = 0.0284) compared with the base model. In addition, the new model was better than the base model for predicting csPCa in the low or medium probability while the number of patients with csPCa predicted by the new model was in good agreement with the actual number of patients with csPCa in the high-risk threshold.

Conclusions

This study demonstrates that aPSADPZ has a higher predictive accuracy for csPCa diagnosis than the conventional indicators. Including aPSADPZ, PZ ratio, and age can improve csPCa diagnosis and avoid unnecessary biopsies.

Endogenous testosterone density is an independent predictor of pelvic lymph node invasion in high-risk prostate cancer: results in 201 consecutive patients treated with radical prostatectomy and extended pelvic lymph node dissection

Objective

To evaluate the influence of endogenous testosterone density (ETD) on pelvic lymph node invasion (PLNI) in high risk (HR) prostate cancer (PCa) treated with radical prostatectomy (RP) and staged with extended pelvic lymph node dissection (ePLND).

Materials and methods

ETD was evaluated as the ratio of endogenous testosterone (ET) on prostate volume (PV). HR-PCa was assessed according to the European Association of Urology (EAU) system. The association of ETD and other routinely clinical factors (BPC: percentage of biopsy positive cores; PSA: prostate specific antigen; ISUP: tumor grade system according to the International Society of Urologic Pathology; cT: tumor clinical stage) with the risk of PLNI was assessed by the logistic regression model.

Results

Overall, 201 out of 805 patients (24.9%) were classified HR and PLNI occurred in 42 subjects (20.9%). On multivariate analysis, PLNI was independently predicted by BPC (OR 1.020; 95% CI 1.006–1.035; p = 0.019), ISUP > 3 (OR 2.621; 95% CI 1.170–5.869; p = 0.019) and ETD (OR 0.932; 95% CI 0.870–0.999; p = 0.045). After categorizing continuous clinical predictors, the risk of PLNI was independently increased by ETD up to the median (OR 2.379; 95% CI 1.134–4.991; p = 0.022), BPC > 50% (OR 3.125; 95% CI 1.520–6.425; p = 0.002) as well as by ISUP > 3 (OR 2.219; 95% CI 1.031–4.776; p = 0.042).

Conclusions

As ETD measurements decreased, patients were more likely to have PLNI. In HR disease with PLNI, the influence of PCa on ETD should be addressed by higher level studies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11255-022-03103-w.

Endogenous testosterone density as ratio of endogenous testosterone levels on prostate volume predicts tumor upgrading in low-risk prostate cancer

OBJECTIVES

To evaluate preoperative endogenous testosterone (ET) density (ETD), defined as the ratio of ET on prostate volume, and tumor upgrading risk in low-risk prostate cancer (PCa).

MATERIALS AND METHODS

From November 2014 to December 2019, 172 low-risk patients had ET (nmol/L) measured. ETD, prostate-specific antigen density (PSAD) and the ratio of percentage of biopsy positive cores (BPC) to prostate volume (PV), defined as BPC density (BPCD), were evaluated. Associations with tumor upgrading in the surgical specimen were assessed by statistical methods.

RESULTS

Overall, 121 patients (70.3%) had tumor upgrading, which was predicted by BPCD (odds ratio, OR = 4.640; 95% CI 1.903-11.316; p = 0.001; overall accuracy: 70.3%). On multivariate analysis, tumor upgrading and clinical density factors related to each other for BPCD being predicted by ETD (regression coefficient, b = 0.032; 95% CI 0.021-0.043; p < 0.0001), PSAD (b = 1.962; 95% CI 1.067-2.586; p < 0.0001) and tumor upgrading (b = 0.259; 95% CI 0.112-0.406; p = 0.001). According to the model, as BPCD increased, ETD and PSAD increased, but the increase was higher for upgraded cases who showed either higher tumor load but significantly lower mean levels of either ET or PSA.

CONCLUSIONS

As ETD increased, higher tumor loads were assessed; however, in upgraded patients, lower ET was also detected. ETD might stratify low-risk disease for tumor upgrading features.

Prostatic Inflammation in Prostate Cancer: Protective Effect or Risk Factor?

The relationship between prostatic chronic inflammation (PCI) and prostate cancer (PCa) is unclear and controversial. Some authors reported that a history of chronic prostatitis may be correlated with PCa induction, while others associate chronic inflammation with less aggressive disease or consider inflammation as a possible protective factor against PCa. Four different types of prostatitis are known: bacterial acute prostatic inflammation, bacterial chronic prostatic inflammation, abacterial prostatitis/chronic pelvic pain syndrome, and asymptomatic prostatic chronic inflammation. Prostatic inflammation is underestimated during daily clinical practice, and its presence and degree often go unmentioned in the pathology report of prostate biopsies. The goal of this report is to further our understanding of how PCI influences the biology of PCa. We investigated the main pathogenetic mechanisms responsible for prostatic inflammation, including the cellular response and inflammatory mediators to describe how inflammation modifies the prostatic environment and can lead to benign or malignant prostatic diseases. We found that prostatic inflammation might have a pivotal role in the pathogenesis of prostatic diseases. Details about PCI in all prostate biopsy reports should be mandatory. This will help us better understand the prostatic microenvironment pathways involved in PCa biology, and it will allow the development of specific risk stratification and a patient-tailored therapeutic approach to prostatic diseases.

Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer

Objective

To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies.

Patients and Methods

We retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator.

Results

A total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit.

Conclusion

We developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients.

Endogenous testosterone mirrors prostate cancer aggressiveness: correlation between basal testosterone serum levels and prostate cancer European Urology Association clinical risk classes in a large cohort of Caucasian patients

OBJECTIVE

To evaluate the association between basal total testosterone (TT) levels with the European Association of Urology (EAU) risk classes at the time of diagnosis of prostate cancer (PCA).

METHODS

A retrospective review of prospectively collected data was carried out between November 2014 and March 2018. Preoperative basal TT levels and PSA were measured in 601 consecutive Caucasian patients who were not under androgen deprivation and undergoing surgery at a tertiary referral center. Patients were classified into low (reference group), intermediate- and high-risk/locally advanced classes. The multinomial logistic regression model evaluated associations of TT and other clinical factors with EAU risk classes.

RESULTS

One hundred twenty four patients (24%) were low risk, 316 (52.6%) were intermediate, 199 (16.5%) were high risk and 42 (7%) were locally advanced. Median circulating basal TT levels increased along EAU classes. TT, PSA, percentage of biopsy-positive cores and tumor clinical stage were independently associated with the high-risk class (odds ratio, OR = 1.002; p = 0.030) but were not associated with intermediate-risk or locally advanced PCA when compared to the low-risk class. TT above the median value was an independent predictor of high-risk class PCA.

CONCLUSIONS

Basal TT levels are positively associated with low, intermediate and high EAU risk classes. The association is significant for the high-risk class when compared to the low-risk group, but was lost in locally advanced risk class. In PCA patients, high TT serum levels are associated with high-risk disease. Endogenous TT should be considered as a biological marker for assessing EAU PCA risk classes.

Incidental prostate cancer after transurethral resection of the prostate: analysis of incidence and risk factors in 458 patients

BACKGROUND

The aim of this study is to evaluate the incidence and risk factors of incidental prostate cancer (IPCA) in a contemporary cohort of lower urinary tract symptoms (LUTS) patients who underwent trans-urethral resection of the prostate (TURP).

METHODS

A series of 458 consecutive patients who underwent TURP were evaluated between January 2016 to June 2018. Evaluated factors included age (years), Body Mass Index (BMI; kg/square meters), treatment with inhibitors of 5-alpha reductase, previous prostate biopsies, basal prostate specific antigen (PSA) levels (ng/mL), serum leukocyte count (×10⁹/L), weight of resected prostate tissue (grams), grade and stage of IPCA. The multivariate logistic regression model evaluated associations of significant clinical factors with the risk of IPCA.

RESULTS

Overall, IPCA was detected in 30 of 454 patients (6.6%). A mean of 21.8 g of tissue was resected. The mean number of positive chips was 5.6 (mean percentage 3.9%) with tumor grade group 1 in 22 cases (73.4%) and tumor stage cT1a in 23 patients (76.7%). On multivariate analysis, independent factors that were positively associated with the risk of IPCA were BMI (odds ratio, OR=1.121; P=0.017) and leukocyte count (OR=1.144; P=0.027).

CONCLUSIONS

In a contemporary cohort of patients undergoing TURP for the treatment of LUTS, the risk of IPCA was not negligible with a rate of being 6.6%. BMI and serum leukocyte count were found to be independent factors that were positively associated with the risk of IPCA.

The impact of extended pelvic lymph node dissection on the risk of hospital readmission within 180 days after robot assisted radical prostatectomy

OBJECTIVE

To evaluate the factors associated with the risk of hospital readmission after robot assisted radical prostatectomy (RARP) with or without extended pelvic lymph node dissection (ePLND) for prostate cancer (PCA) over a long term.

MATERIALS AND METHODS

The risk of readmission was evaluated by clinical, pathological, and perioperative factors. Skilled and experienced surgeons performed the procedures. Patients were followed for complications and hospital readmission for a period of six months. The logistic regression model and Cox's proportional hazards assessed the association of factors with the risk of readmission.

RESULTS

From January 2013 to December 2018, 890 patients underwent RARP; ePLND was performed in 495 of these patients. Hospital readmission was detected in 25 cases (2.8%); moreover, it was more frequent when RARP was performed with ePLND (4.4% of cases) than without (0.8% of patients). On the final multivariate model, ePLND was the only independent factor that was positively associated with the risk of hospital readmission (hazard ratio, HR = 5935; 95%CI 1777-19,831; p = 0.004).

CONCLUSIONS

Over the long term after RARP for PCA, the risk of hospital readmission is associated with ePLND. In patients who underwent RARP and ePLND, 4.4% of them had a readmission, compared to RARP alone, in which only 0.8% of cases had a readmission. When ePLND is planned for staging pelvic lymph nodes, patients should be informed of the increased risk of hospital readmission.