Mobile phone apps for the prediction of prostate cancer: External validation of the Coral and Rotterdam apps

ObjectiveTo analyze the performance of two mobile phone apps—the Rotterdam prostate cancer risk app and the Coral app—in a cohort of patients undergoing prostate biopsies.MethodsA consecutive series of men undergoing prostate biopsies were enrolled in two centers. Indications for prostate biopsy included abnormal prostate-specific antigen levels (PSA >4 ng/mL) and/or an abnormal digital rectal examination (DRE). Prostate cancer risk and high-grade prostate cancer risk were assessed using the Rotterdam prostate cancer risk app (iOS) and the Coral app (iOS). The usability of the apps was also assessed and compared using the Post-Study System Usability Questionnaire (PSSUQ) developed by IBM.ResultsOverall, 1682 patients with a median age of 68 (62–73) years were enrolled. The Rotterdam app outperformed the Coral app in the prediction of prostate cancer (AUC: 0.70 versus 0.631, p = 0.001) and of high-grade prostate cancer (0.75 versus 0.69, p = 0.001) (Fig. 1). PSSUQ data revealed that both Rotterdam and Coral applications were comparable in terms of usefulness (87% versus 83%, p = 0.708), information quality (74% versus 72%, p = 0.349), interface quality (79% versus 74%, p = 0.216) and satisfaction (76% versus 76%, p = 0.935), respectively. In terms of preferences, 26/50 (54%) preferred the Rotterdam app, while 24/50 (46%) preferred the Coral app.ConclusionIn our experience the Rotterdam App outperformed the Coral App for the prediction of prostate cancer or high-grade cancer diagnosis. In particular we confirmed, using the Rotterdam app, that only one out of ten patients with a low Rotterdam score will harbor high-grade prostate cancer on biopsy.     

Negative Biopsy Histology in Men With PI-RADS Score 5 in Daily Clinical Practice: Incidence of Granulomatous Prostatitis

IntroductionThe purpose of this study was to evaluate the biopsy histology of men who underwent transperineal multi-parametric magnetic resonance imaging (mpMRI)/transrectal ultrasound fusion biopsy for Prostate Imaging Reporting and Data System (PI-RADS) score 5 lesions.Patients and MethodsFrom January 2016 to June 2019, 105 men with PI-RADS score 5 underwent mpMRI/transrectal ultrasound fusion biopsy combined with systematic prostate biopsy. All the patients underwent a 3.0 Tesla pelvic mpMRI for the first time before prostate biopsy. In detail, the detection rate for clinically significant prostate cancer (PCa) and the follow-up of the patients without proven diagnosis of PCa has been reported.ResultsIn 91 (86.7%) of 105 patients, a stage T1c PCa was diagnosed, and 89 (84.5%) of 105 of them were classified as clinically significant PCa. Among the 16 (15.5%) of 105 patients with absence of cancer, 5 (31.5%) of 16 had an aspecific granulomatous prostatitis, 1 (6.2%) of 16 had a specific granulomatous prostatitis secondary to prostatic Mycobacterium Tubercolosis, and 10 (62.3%) of 16 had a diagnosis of normal parenchyma. The 6 patients with granulomatous prostatitis underwent specific antibiotic therapy followed by laboratory (ie, semen and urine cultures) and clinical evaluation. Six months from prostate biopsy, none of the 16 patients underwent repeat prostate biopsy because prostate-specific antigen (PSA) (15/16 cases) plus PSA density significantly decreased; in addition, in all the cases the initial PI-RADS score 5 was downgraded at mpMRI revaluation to PI-RADS score ≤ 3.ConclusionThe reduction of PSA plus PSA density values and the downgrading of PI-RADS score to ≤ 3 allow avoiding a repeated prostate biopsy in men with initial mpMRI PI-RADS score 5 lesion and negative biopsy histology.     

The Use of Multiparametric Magnetic Resonance Imaging for Follow-up of Patients Included in Active Surveillance Protocol. Can PSA Density Discriminate Patients at Different Risk of Reclassification?

IntroductionThe objective of this study was to test Prostate Imaging Reporting and Data System (PI-RADS) classification on multiparametric magnetic resonance imaging (mpMRI) and MRI-derived prostate-specific antigen density (PSAD) in predicting the risk of reclassification in men in active surveillance (AS), who underwent confirmatory or per-protocol follow-up biopsy.Materials and MethodsThree hundred eighty-nine patients in AS underwent mpMRI before confirmatory or follow-up biopsy. Patients with negative (−) mpMRI underwent systematic random biopsy. Patients with positive (+) mpMRI underwent targeted fusion prostate biopsies + systematic random biopsies. Different PSAD cutoff values were tested (< 0.10, 0.10-0.20, ≥ 0.20). Multivariable analyses assessed the risk of reclassification, defined as clinically significant prostate cancer of grade group 2 or more, during follow-up according to PSAD, after adjusting for covariates.ResultsOne hundred twenty-seven (32.6%) patients had mpMRI(−); 72 (18.5%) had PI-RADS 3, 150 (38.6%) PI-RADS 4, and 40 (10.3%) PI-RADS 5 lesions. The rate of reclassification to grade group 2 PCa was 16%, 22%, 31%, and 39% for mpMRI(−) and PI-RADS 3, 4, and 5, respectively, in case of PSAD < 0.10 ng/mL²; 16%, 25%, 36%, and 44%, in case of PSAD 0.10 to 0.19 ng/mL²; and 25%, 42%, 55%, and 67% in case of PSAD ≥ 0.20 ng/mL². PSAD ≥ 0.20 ng/mL² (odds ratio [OR], 2.45; P = .007), PI-RADS 3 (OR, 2.47; P = .013), PI-RADS 4 (OR, 2.94; P < .001), and PI-RADS 5 (OR, 3.41; P = .004) were associated with a higher risk of reclassification.ConclusionPSAD ≥ 0.20 ng/mL² may improve predictive accuracy of mpMRI results for reclassification of patients in AS, whereas PSAD < 0.10 ng/mL² may help selection of patients at lower risk of harboring clinically significant prostate cancer. However, the risk of reclassification is not negligible at any PSAD cutoff value, also in the case of mpMRI(−).     

Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging

BackgroundWe aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patients with spiculation, lobulation, or pleural indentation on CT.Patients and MethodsWe retrospectively recruited 915 patients with solitary solid pulmonary nodules and suspicious signs of malignancy. Data including clinical characteristics and subjective CT findings were obtained. A 3-dimensional U-Net-based DL model was used for tumor segmentation and extraction of 3-dimensional radiomics features. We used the Maximum Relevance and Minimum Redundancy (mRMR) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm to select the intranodular, perinodular, and gross nodular radiomics features. We propose a medical image DL (IDL) model, a clinical image DL (CIDL) model, a radiomics DL (RDL) model, and a clinical image radiomics DL (CIRDL) model to preoperatively differentiate GNs from solid lung cancer. Five-fold cross-validation was used to select and evaluate the models. The prediction performance of the models was evaluated using receiver operating characteristic and calibration curves.ResultsThe CIRDL model achieved the best performance in differentiating between GNs and solid lung cancer (area under the curve [AUC] = 0.9069), which was significantly higher compared with the IDL (AUC = 0.8322), CIDL (AUC = 0.8652), intra-RDL (AUC = 0.8583), peri-RDL (AUC = 0.8259), and gross-RDL (AUC = 0.8705) models.ConclusionThe proposed CIRDL model is a noninvasive diagnostic tool to differentiate between granuloma nodules and solid lung cancer nodules and reduce the need for invasive diagnostic and surgical procedures.     

Which Prostate Biopsy in Men Enrolled in Active Surveillance? Experience in 110 Men Submitted to Scheduled Three-Years Transperineal Saturation Biopsy Combined With Fusion Targeted Cores

IntroductionThe reclassification rate for clinically significant prostate cancer (csPCa) has been evaluated in men enrolled in active surveillance (AS) protocol who previously underwent confirmatory biopsy.Materials and MethodsFrom May 2013 to September 2017, 110 patients (median age 63 years) with very low risk PCa underwent 3-years scheduled prostate biopsy performing repeated transperineal saturation biopsy (SPBx); in addition, the mpMRI lesions characterized by Prostate Imaging Reporting and Data System (PI-RADS) version 2 scores ≥ 3 were submitted to additional mpMRI/TRUS fusion biopsies (4 cores). The reclassification rate for csPCa (over 3 or more than 10% of positive cores, ISUP Grade Group/GG ≥ 2, greatest percentage of cancer > 50%) has been evaluated.ResultsSix (5.4%) patients with PI-RADS score 3 (4 men) vs. 4 (2 men) were reclassified based on upgraded (GG2); SPBx and MRI/TRUS fusion biopsy diagnosed 100% and 0% of csPCa, respectively. Of the remaining 104 (94.5%) patients, 75 (72.2%) were found to have very low-risk PCa and in 29 (27.8%) cancer was absent (normal parenchyma).ConclusionSPBx combined with mpMRI at confirmatory and repeated evaluation allow to reduce the reclassification rate during AS follow up (5.4% of the cases at 3 years from diagnosis).     

Can We Improve the Preoperative Prediction of Prostate Cancer Recurrence With Multiparametric MRI?

IntroductionThe use of multiparametric magnetic resonance imaging (mpMRI) to assess prostate cancer (PCa) has increased over the past decade. We aimed to assess if preoperative mpMRI lesion score, a variable routinely available for men undergoing pre-biopsy MRI, improves the performance of commonly used preoperative predictive models for PCa recurrence.Patients and MethodsWe analyzed data from 372 patients with PCa treated with radical prostatectomy in 2012 to 2017 and assessed with pre-biopsy mpMRI within 6 months prior to surgery. Suspicious areas for cancer were scored on a standardized 5-point scale. Cox regression was used to assess the association between mpMRI score and the risk of postoperative biochemical recurrence. Two different models were tested accounting for factors included in the Kattan nomogram and in the D’Amico risk-classification.ResultsOverall, 53% and 30% of patients were found with a lesion scored 4 or 5 at pre-biopsy mpMRI, respectively. Risk varied widely by mpMRI (29% 2-year risk of biochemical recurrence for a score of 5 vs. 5% for a score of 1-2), and mpMRI score was associated with large hazard ratios after adjusting for stage, grade, and prostate-specific antigen: 1.66, 1.96, and 2.71 for scores 3, 4, and 5, respectively. However, 95% confidence intervals were very wide (0.19-14.20, 0.26-14.65, and 0.36-20.55, respectively) and included 1.ConclusionsOur data did not show that preoperative models, commonly used to assess PCa risk, were improved after including the pre-biopsy mpMRI score. However, the value of pre-biopsy mpMRI to improve preoperative risk models should be investigated in larger data sets.     

The risk of prostate cancer for men on aspirin,statin or antidiabetic medications

BackgroundA decreased risk of prostate cancer (PCa) has been suggested in men taking aspirin, statins and metformin, although the evidence has been conflicting. We estimated the association between prescribed medications, prostate specific antigen (PSA) levels and the risk of either any PCa or high-grade PCa.MethodsThis population-based cohort study included 185,667 men having a first recorded PSA test and 18,574 men having a first prostate biopsy in Stockholm County, Sweden for the period 2007–2012. Detailed clinical information including PSA levels, biopsy results, comorbidities and educational level were obtained from population-based registers. High-grade prostate cancer was defined as a Gleason score of seven or higher. Differences in PSA levels by medication status were estimated using linear regression on log PSA values. PCa risk was estimated using multivariate logistic regression.ResultsCompared with men who were not on medication, the PSA level at the first PSA test was lower among men using 75 mg/dose aspirin (−3.9% change in PSA concentration; 95% confidence interval (CI): −5.8 to −2.1), statin (−4.6%; 95% CI: −6.2 to −2.9), metformin (−14%; 95% CI: −17 to −12) and insulin (−16%; 95% CI: −18 to −14). Men using any statins had an increased risk of both high-grade PCa (odds ratio (OR) 1.25; 95% CI: 1.10–1.42) and PCa of any grade (OR 1.16; 95% CI 1.04–1.29). There were no significant associations between aspirin or any antidiabetic medication and the risk of PCa.ConclusionWe found no protective effect of aspirin, statins or antidiabetics in terms of risk for any PCa or high-grade PCa. Use of any statins was associated with an elevated risk of being diagnosed with high-grade prostate cancer.     

Performing Precise Biopsy in Naive Patients With Equivocal PI-RADS,Version 2, Score 3, Lesions: An MRI-based Nomogram to Avoid Unnecessary Surgical Intervention

PurposeThe primary objective of the present study was to avoid unnecessary prostate biopsy in biopsy-naive patients with Prostate Imaging Reporting and Data System, version 2 (PI-RADS v2), score 3, lesions.Materials and MethodsWe reviewed our prospectively maintained database from January 2012 to July 2018. Logistic regression analyses were performed to test different clinical factors as predictors of clinically significant prostate cancer (CSPCa) and build nomograms. Calibration curves were used to assess the concordance between the predictive value and the true risk. Decision curves were created to measure the overall net benefit.ResultsThe prostate cancer (PCa) and CSPCa detection rates were 37.2% (81 of 218) and 23.9% (52 of 218) in the PI-RADS v2, score 3, cohort. More PCa cases (61.7%; 50 of 81) and CSPCa cases (75%; 39 of 52) were found in the peripheral zone than in the transitional zone. Multivariate analysis showed that age, prostate-specific antigen density, lesion region, and apparent diffusion coefficient (ADC) were predictive factors for CSPCa and PCa. Internally validated calibration curves showed that the predicted risk of CSPCa was closer to the actual probability when the threshold was > 60%. Decision curves showed that a better net benefit was achieved when the model was used to guide clinical practice.ConclusionsMore cases of PCa and CSPCa were seen in the peripheral zone than in the transitional zone among patients with PI-RADS v2, score 3. The positive predictive value for a positive ADC (< 900 μm²/s) for the detection of CSPCa and PCa improved with an increasing prostate-specific antigen density. Biopsy can be avoided if the equivocal lesion has a negative ADC (> 900 μm²/s) and was in the transition zone.     

Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis

Despite the extensive development of prostate cancer (PCa) risk models that are used for patient–clinician decision-making for PCa screening, their predictive accuracy is unknown. In a meta-analysis of six different risk prediction models, results show that models have the potential to increase the sensitivity of PSA screening to detect any PCa (44% versus 21%).BackgroundDespite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve predictive accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis.DesignA systematic literature search of Medline was conducted to identify PCa predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model.ResultsThe systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported.ConclusionsRisk prediction models improve the predictive accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.     

Utility of Multiparametric Magnetic Resonance Imaging With PI-RADS,Version 2, in Patients With Prostate Cancer Eligible for Active Surveillance: Which Radiologic Characteristics Can Predict Unfavorable Disease?

BackgroundWe investigated the utility of multiparametric magnetic resonance imaging (mpMRI) using Prostate Imaging Reporting and Data System, version 2 (PI-RADSv2), scoring in patients with prostate cancer eligible for active surveillance (AS).Materials and MethodsThe medical records of the patients who had undergone mpMRI before radical prostatectomy from 2014 to 2018 were reviewed. All the patients had met the Prostate Cancer Research International AS criteria. PI-RADSv2 scores were assigned to 12 prostate regions. Unfavorable disease was stratified using the American Joint Committee on Cancer (AJCC) prognostic scale as stage IIB (Gleason score [GS], 3+4 and pathologic stage T2) and IIC-III (GS, ≥ 4+3 or pathologic stage T3).ResultsOf 376 eligible patients, 184 (48.9%), 129 (34.3%), and 63 (16.8%) had AJCC stage I, IIB, and IIC-III disease, respectively. The patients with IIC-III disease were older and had a higher prostate-specific antigen density than those with stage I or IIB disease. PI-RADS 5 lesions were more frequent in patients with stage IIC-III than in patients with stage I or IIB disease. Multivariable analysis revealed that ≥ 2 lesions with a PI-RADS 5 score was an independent predictor for unfavorable disease (hazard ratio [HR], 3.612; P < .001 for IIB; HR, 6.562; P < .001 for IIC-III), and PI-RADS score of ≥ 4 was limited for predicting AJCC stage IIB disease (HR, 2.387; P = .01).ConclusionmpMRI with PI-RADSv2 showed high negative predictive value for patients with prostate cancer eligible for AS. Multiple PI-RADS 4-5 lesions were associated with unfavorable disease compared with solitary lesions. Multiple PI-RADS 5 lesions were strongly associated with GS ≥ 4+3 or pathologic T3 disease. Targeted biopsy or radical treatment should be considered for these patients.     

A Combinatorial Neural Network Analysis Reveals a Synergistic Behaviour of Multiparametric Magnetic Resonance and Prostate Health Index in the Identification of Clinically Significant Prostate Cancer

BackgroundThe widespread use of prostate specific antigen (PSA) caused high rate of overdiagnosis. Overdiagnosis leads to unnecessary definitive treatments of prostate cancer (PCa) with detrimental side effects, such as erectile dysfunction and incontinence. The aim of this study was to evaluate the feasibility of an artificial neural network-based approach to develop a combinatorial model including prostate health index (PHI) and multiparametric magnetic resonance (mpMRI) to recognize clinically significant PCa at initial diagnosis.MethodsTo this aim we prospectively enrolled 177 PCa patients who underwent radical prostatectomy and had received PHI tests and mpMRI before surgery. We used artificial neural network to develop models that can identify aggressive PCa efficiently. The model receives as an input PHI plus PI-RADS score.ResultsThe output of the model is an estimate of the presence of a low or high Gleason score. After training on a dataset of 135 samples and optimization of the variables, the model achieved values of sensitivity as high as 80% and 68% specificity.ConclusionsOur preliminary study suggests that combining mpMRI and PHI may help to better estimate the risk category of PCa at initial diagnosis, allowing a personalized treatment approach. The efficiency of the method can be improved even further by training the model on larger datasets.     

Association of Obesity and Diabetes With Prostate Cancer Risk Groups in a Multiethnic Population

IntroductionObesity and diabetes mellitus (DM) have been associated with prostate cancer (PCa) risk, but data examining their combined effects on aggressive PCa are sparse, particularly among non-Hispanic Black and Hispanic men. We investigated the associations of obesity and DM in relation to National Comprehensive Cancer Network (NCCN) PCa risk groups in a racially-diverse patient population.Patients and MethodsWe abstracted demographic and clinical data from men who underwent radical prostatectomy at our institution between 2005 and 2019. Patients were classified into three NCCN PCa risk-groups: low, intermediate and high-risk. Logistic regression models were used to examine the independent and combined associations of body mass index (BMI)/obesity and DM with risks of intermediate and high-risk PCa, adjusting for age and race/ethnicity.ResultsA total of 1303 men with PCa (average age 60 ± 6.9 years) were analyzed. The majority were non-Hispanic Black (N = 493, 38%) or Hispanic (N = 407, 31%). The prevalence of obesity (BMI ≥ 30 kg/m²) and DM was 29.3% (N = 382) and 28.3% (N = 369), respectively. In multivariate analyses, obesity was independently associated with an odds ratio (OR) = 2.21 of high-risk PCa (95% CI: 1.28-3.81), while DM was associated with an OR = 1.49 (95% CI: 1.05-2.11) of intermediate-risk PCa. Compared to non-obese men without diabetes, men with BMI ≥ 30 and DM had increased risks of both intermediate (OR = 1.93; 95% CI 1.12-3.43) and high-risk PCa (OR = 2.40; 95% CI 1.22-4.73). Interestingly, most of the association of high-risk PCa was driven by obesity.ConclusionIn this multiethnic population both obesity and DM were independently associated with intermediate- and high-risk PCa; however, most of the association for high-risk cancer was driven by obesity. Our results corroborate findings that obesity increases the risk of aggressive PCa; however, results regarding DM need to be confirmed in other large multiethnic populations.     

The gut microbiota associated with high-Gleason prostate cancer

We have found that intestinal bacteria and their metabolites, short-chain fatty acids (SCFAs), promote cancer growth in prostate cancer (PCa) mouse models. To clarify the association between gut microbiota and PCa in humans, we analyzed the gut microbiota profiles of men with suspected PCa. One hundred and fifty-two Japanese men undergoing prostate biopsies (96 with cancer and 56 without cancer) were included in the study and randomly divided into two cohorts: a discovery cohort (114 samples) and a test cohort (38 samples). The gut microbiota was compared between two groups, a high-risk group (men with Grade group 2 or higher PCa) and a negative + low-risk group (men with negative biopsy or Grade group 1 PCa), using 16S rRNA gene sequencing. The relative abundances of Rikenellaceae, Alistipes, and Lachnospira, all SCFA-producing bacteria, were significantly increased in high-risk group. In receiver operating characteristic curve analysis, the index calculated from the abundance of 18 bacterial genera which were selected by least absolute shrinkage and selection operator regression detected high-risk PCa in the discovery cohort with higher accuracy than the prostate specific antigen test (area under the curve [AUC] = 0.85 vs 0.74). Validation of the index in the test cohort showed similar results (AUC = 0.81 vs 0.67). The specific bacterial taxa were associated with high-risk PCa. The gut microbiota profile could be a novel useful marker for the detection of high-risk PCa and could contribute to the carcinogenesis of PCa.     

Natural History of Patients with Prostate MRI Likert 1-3 and Development of RosCaP: a Multivariate Risk Score for Clinically Significant Cancer

IntroductionClinically significant prostate cancer (csCaP) with Gleason ≥3 + 4 is found in 10% negative prebiopsy multiparametric (mp) MRI cases and varies widely for equivocal mpMRI cases. The objective of this study was to investigate long-term outcomes of patients with negative and equivocal mpMRIs and to develop a predictive score for csCaP risk stratification in this group.Patients and MethodsPatients who underwent an upfront mpMRI between May 2015 and March 2018 with an MRI score Likert 1 to 3 were included in the study. Patients had either a CaP diagnosis at MRI-targeted biopsy or were not diagnosed and attended follow-up in the community. Outcomes were analysed through the Kaplan-Meier estimator and Cox Model. Regression coefficients of significant variables were used to develop a Risk of significant Cancer of the Prostate score (RosCaP).ResultsAt first assessment 281/469 patients had mpMRI only and 188/469 mpMRI and biopsy, 26 csCaP were found at biopsy, including 10/26 in Likert 3 patients. 12/371 patients discharged without CaP after first assessment were diagnosed with csCaP during a median of 34.2 months’ follow-up, 11/12 diagnosis occurred in patients omitting initial biopsy. csCaP diagnosis-free survival was 95.7% in the MRI group and 99.1% in the biopsy group. From these outcomes, a continuous RosCaP score was developed: RosCaP = 0.083 x Age - 0.202 x (1/PSA Density) + 0.786 (if Likert 3), and 4 risk classes were proposed. Limitations include retrospective design and absence of external validation.ConclusionAge, PSA Density and MRI Likert score were significantly associated to the risk of csCaP and utilised to devise the novel RosCap predictive score focused to support risk assessment in patients with negative or equivocal mpMRI results.     

BCG Administration after Prior Radiation Treatment for Prostate Cancer

IntroductionProstate radiotherapy is associated with worse oncologic outcomes in patients with bladder cancer. The underlying mechanism is incompletely understood but is thought to be related to an altered microenvironment promoting tumorigenesis. However, there is a gap in the literature regarding how the effect of BCG varies according to prior radiotherapy in patients with non–muscle invasive bladder cancer (NMIBC). In this context, we sought to evaluate oncologic outcomes in NMIBC patients who have previously undergone prostate radiotherapy compared to patients with no prior history of pelvic radiotherapy.MethodsThis is a retrospective cohort study that includes all patients who received intravesical for NMIBC at our institution from 2001 to 2019. Patients were stratified into 3 cohorts: prior radiotherapy (RT), radical prostatectomy (RP), and no prostate cancer (No PCa). The outcomes of interest were recurrence at 1-year, progression to muscle-invasive bladder cancer (MIBC), and progression to metastatic disease. Comparisons were also made between cohorts with respect to elapsed time from radiation therapy. Wilcoxon rank-sum test was used for comparing continuous variables, while χ² and Fischer's exact tests were used to examine categorical variables.ResultsIn 199 total patients who underwent BCG for NMIBC, 23 had a prior history of prostate radiotherapy treatment, while 17 underwent prior radical prostatectomy. Overall, 41.2% of patients had recurrence at 1 year. There was no difference in the number of induction or maintenance BCG administrations received between the cohorts within the first year. There was no significant difference in recurrence at 1 year between the 3 cohorts (P = .56). There was also no difference in progression to MIBC or progression to metastatic disease with P = .50 and 0.89, respectively.ConclusionThe risk of recurrence after induction BCG treatment for high-grade NMIBC does not vary according to prior radiation treatment for prostate cancer.     

Improving the Stratification of Patients With Intermediate-risk Prostate Cancer

BackgroundIntermediate-risk prostate cancer (IR PCa) phenotypes may vary from favorable to unfavorable. National Comprehensive Cancer Network (NCCN) criteria help distinguish between those groups. We studied and attempted to improve this stratification.Patients and MethodsA total of 4048 (NCCN favorable: 2015 [49.8%] vs. unfavorable 2033 [50.2%]) patients with IR PCa treated with radical prostatectomy were abstracted from an institutional database (2000-2018). Multivariable logistic regression models predicting upstaging and/or upgrading (Gleason Grade Group [GGG] IV-V and/or ≥ pT3 or pN1) in IR PCa were developed, validated, and directly compared with the NCCN IR PCa stratification.ResultsAll 4048 patients were randomly divided between development (n = 2024; 50.0%) and validation cohorts (n = 2024; 50.0%). The development cohort was used to fit basic (age, prostate-specific antigen, clinical T stage, biopsy GGG, and percentage of positive cores [all P < .001]) and extended models (age, prostate-specific antigen, clinical T stage, biopsy GGG, prostate volume, and percentage of tumor within all biopsy cores [all P < .001]). In the validation cohort, the basic and the extended models were, respectively, 71.4% and 74.7% accurate in predicting upstaging and/or upgrading versus 66.8% for the NCCN IR PCa stratification. Both models outperformed NCCN IR PCa stratification in calibration and decision curve analyses (DCA). Use of NCCN IR PCa stratification would have misclassified 20.1% of patients with ≥ pT3 or pN1 and/or GGG IV to V versus 18.3% and 16.4% who were misclassified using the basic or the extended model, respectively.ConclusionBoth newly developed and validated models better discriminate upstaging and/or upgrading risk than the NCCN IR PCa stratification.     

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

IntroductionMultiparametric 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 MethodsPatients 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.ResultsAmong 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%).ConclusionPI-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.     

Predictive and prognostic role of serum neopterin and tryptophan breakdown in prostate cancer

The γ‐interferon‐induced enzymes indoleamine 2,3‐dioxygenase and GTP‐cyclohydrolase are key players in tumor immune escape mechanisms. We quantified serum levels of neopterin and tryptophan breakdown (tryptophan, kynurenine, and kynurenine‐to‐tryptophan ratio) in addition to prostate‐specific antigen (PSA) in newly diagnosed prostate cancer (PCa) patients (n = 100) before radical prostatectomy (RP) as well as at time of biochemical recurrence (BCR) after RP (n = 50) in comparison to healthy men (n = 49). Effects of biomarkers on the risk of PCa diagnosis on transrectal biopsy, worse histopathological characteristics of the RP specimens, and cancer‐specific survival (CSS) after BCR were investigated. Neopterin (hazard ratio [HR], 2.46; 95% confidence interval [CI], 1.08–5.61; P = 0.032) and kynurenine (HR, 2.93; 95% CI, 1.26–6.79; P = 0.012) levels were univariately associated with CSS. When adjusted for other biomarkers, only neopterin remained an independent predictor of CSS (HR, 2.56; 95% CI, 1.07–6.12; P = 0.035). Only PSA was associated with an increased risk of PCa diagnosis on biopsy, univariately (odds ratio, 3.14; 95% CI, 1.68–5.88; P < 0.001) as well when adjusted for other biomarkers (odds ratio, 3.29; 95% CI, 1.70–6.35; P < 0.001). Moreover, only preoperative PSA was able to predict positive surgical margin (area under the receiver operating characteristic curve [AUC] = 0.71; 95% CI, 0.59–0.82; P = 0.001), higher Gleason score (AUC = 0.75; 95% CI, 0.66–0.85; P < 0.001) and extraprostatic involvement (AUC = 0.79; 95% CI, 0.69–0.88; P < 0.001) at RP specimens, respectively. Although serum neopterin and tryptophan breakdown cannot be considered as biomarkers in detecting PCa or in predicting worse final pathological findings, neopterin levels are useful for stratifying patients into different prognostic groups after BCR.     

The Clinical Significance of Maximum Tumor Diameter on MRI in Men Undergoing Radical Prostatectomy or Definitive Radiotherapy for Locoregional Prostate Cancer

IntroductionMaximum tumor diameter (MTD) on pretreatment magnetic resonance imaging (MRI) has the potential to further risk stratify for men with prostate cancer (PCa) prior to definitive local therapy. We aim to evaluate the prognostic impact of radiographic maximum tumor diameter (MTD) in men with localized prostate cancer.Patients and MethodsFrom a single-center retrospective cohort of men receiving definitive treatment for PCa (radical prostatectomy [RP] or radiotherapy [RT]) with available pretreatment MRI, we conducted univariable and multivariable Cox proportional-hazards models for progression using clinical variables including age, NCCN risk group, radiographic extracapsular extension (ECE), radiographic seminal vesical invasion (SVI), and MTD. RP and RT cohorts were analyzed separately. Covariates were used in a classification and regression tree (CART) analysis and progression-free survival was estimated with the Kaplan-Meier method and groups were compared using log-rank tests.ResultsThe cohort included 631 patients (n = 428 RP, n = 203 RT). CART analysis identified 4 prognostic groups for patients treated with RP and 2 prognostic groups in those treated with RT. In the RP cohort, NCCN low/intermediate risk group patients with MTD>=15 mm had significantly worse PFS than those with MTD <= 14 mm, and NCCN high-risk patients with radiographic ECE had significantly worse PFS than those without ECE. In the RT cohort, PFS was significantly worse in the cohort with MTD >= 23 mm than those <= 22 mm.ConclusionRadiographic MTD may be a useful prognostic factor for patients with locoregional prostate cancer. This is the first study to illustrate that the importance of pretreatment tumor size may vary based on treatment modality.     

A comparison of magnetic resonance imaging techniques used to secure biopsies in prostate cancer patients

Introduction: Prostate cancer (PCa) is the most common diagnosed malignancy among the male population in the United States. The incidence is increasing with an estimated amount of 175.000 cases in 2019.

Areas covered: Primarily, PCa is generally detected by an elevated or rising serum prostate-specific antigen (PSA) and digital rectal examination (DRE) followed by pathological examination. Histopathology ultimately confirms the presence of PCa and determines a Gleason score. However, PSA and DRE have low specificity and sensitivity, respectively. Subsequently, accurate assessment of the aggressiveness of PCa is essential to prevent overdiagnosis and thus overtreatment of low-risk or indolent cancers. By visualizing PCa suspicious lesions and sampling them during the targeted biopsy, it is likely that the diagnostic accuracy of significant PCa improves. This article reviews the current imaging techniques used to secure biopsies in patients with a suspicion of PCa. The advantages and limitations of each technique are described.

Expert opinion: Multiparametric magnetic resonance imaging (mpMRI) and subsequent-targeted biopsy have improved the diagnostic accuracy of PCa detection in men with an elevated or rising serum PSA. Prostate lesions visible on mpMRI are easily targeted during either in-bore MRI-guided biopsy, cognitive fusion biopsy or MRI-TRUS fusion biopsy.