Number Of Biopsy Cores Research Articles

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer

ObjectiveRenal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data on the concordance rate between RTB results and final pathology after surgery are unavailable. Therefore, we aimed to develop a machine learning algorithm to optimize RTB technique and to investigate the degree of concordance between RTB and surgical pathology reports. Materials and methodsWithin a prospectively maintained database, patients with indeterminate renal masses who underwent RTB at a single tertiary center were identified. We recorded and analyzed the approach (US vs. CT), the number of biopsy cores (NoC), and total core tissue length (LoC) to evaluate their impact on diagnostic outcomes. The K-Nearest Neighbors (KNN), a non-parametric supervised machine learning model, predicted the probability of obtaining pathological characterization and grading. In surgical patients, final pathology reports were compared with RTB results. ResultsOverall, 197 patients underwent RTB. Overall, 89.8% (n=177) and 44.7% (n=88) of biopsies were informative in terms of histology and grading, respectively. The discrepancy rate between the pathology results from renal tissue biopsy (RTB) and the final pathology report following surgery was 3.6% (n=7) for histology and 5.0% (n=10) for grading. According to the machine learning model, a minimum of 2 cores providing at least 0.8 cm of total tissue should be obtained to achieve the best accuracy in characterizing the cancer. Alternatively, in cases of RTB with more than two cores, no specific minimum tissue threshold is required. ConclusionsThe discordance rates between RTB pathology and final surgical pathology are notably minimal. We defined an optimal renal biopsy strategy based on at least 2 cores and at least 0.8 cm of tissue or at least 3 cores and no minimum tissue threshold. Patients summaryRTB is a useful test for kidney cancer, but it's not always perfect. Our study shows that it usually matches up well with what doctors find during surgery. Using machine learning can make RTB even better by helping doctors know how many samples to take. This helps doctors treat kidney cancer more accurately.

Prostate cancer lesions in transition zone exhibit a higher propensity for pathological upgrading in radical prostatectomy.

The varying malignancy and lethality of different grades of prostate cancer (PCa) highlight the importance of accurate diagnosis. This study aims to evaluate the upgrading of transition zone (TZ) prostate cancer biopsies and identify factors to improve TZ biopsy accuracy. This retrospective study included 217 patients who underwent laparoscopic radical prostatectomy after 12 + X cores transperineal transrectal ultrasound-magnetic resonance imaging (MRI)-guided targeted prostate biopsy from 2018 to 2021 in our center. Patients with TZ lesions showed a higher incidence of International Society of Urological Pathology (ISUP) grade upgrading from 1 to higher grade compared to peripheral zone lesions (16.9% vs. 5.0%, p = 0.005). Multivariate analysis confirmed TZ lesions as an independent risk factor (OR: 4.594, 97.5% CI: 1.569-15.238, p = 0.008) for upgrading from 1 to higher. Additionally, the number of positive biopsy cores (OR: 0.586, 97.5% CI: 0.336-0.891, p = 0.029) and anterior TZ lesion location (OR: 10.797, 97.5% CI: 1.503-248.727, p = 0.048) were independent factors for the upgrading in TZ patients. This study found that PCa lesions located in the TZ, particularly the anterior TZ, have a higher risk of ISUP grade upgrading. This elevated risk arises from the insufficient distribution of biopsy cores around the TZ lesion. The findings underscore the importance of having an adequate number of biopsy cores around the lesion area to improve the accuracy of ISUP grade assessments.

Factors influencing urinary retention following freehand transperineal prostate biopsy: Insights from a tertiary care center study

ABSTRACT Objectives: In this study, we evaluated the risk factors for urinary retention after freehand transrectal ultrasound (TRUS) guided transperineal prostate biopsy (TPB). Patients and Methods: Data from 102 cases of freehand TPB at a single institution were retrospectively collected and analyzed. All patients underwent magnetic resonance imaging (MRI)-TRUS cognitive fusion TPB using a transperineal needle guide, with systematic biopsies from 10 prostate sectors and additional MRI-guided targeted biopsies. Exclusions comprised patients with coagulation abnormalities, prior prostate surgeries including biopsy, active urinary tract infection, or a lack of pre-biopsy multiparametric MRI. Results: 14/102 (13.72%) had urinary retention and required urethral catheterization for voiding difficulty or discomfort along with a bladder volume of ≥500 ml. Patients with retention exhibited significantly larger prostate volumes (median 75 cc vs. 40 cc; P < 0.05). Receiver operating curve analysis revealed a prostate volume threshold of 57.5 cc and a core number cutoff of 23 for predicting post-TPB urinary retention, with sensitivities of 78.57% and 85.71%, specificities of 75% and 82.95%, positive predictive values of 33.33% and 44.44%, and negative predictive values of 95.75% and 97.33%, respectively, whereas the number of biopsy cores correlated positively with the development of urinary retention (median 25 vs. 22; P < 0.05). Urinary retention was independent of the patient’s age, comorbidities, presenting prostate-specific antigen levels, prebiopsy severity of lower urinary tract symptoms, and use of alpha-blockers. Conclusion: Patients with larger prostates and higher number of biopsy cores are at a higher risk of postfreehand TPB urinary retention and should receive appropriate counselling. Targeted biopsies alone, rather than a full template, may help mitigate urinary retention in these high-risk groups.

Risk factors for Gleason score upgrade from prostate biopsy to radical prostatectomy.

Accurate identification of prostate cancer Gleason grade group remains an important component of the initial management of clinically localized disease. However, Gleason score upgrading (GSU) from biopsy to radical prostatectomy can occur in up to a third of patients treated with surgery. Concern for disease undergrading remains a source of diagnostic uncertainty, contributing to both over-treatment of low-risk disease as well as under-treatment of higher-risk prostate cancer. This review examines the published literature concerning risk factors for GSU from time of biopsy to prostatectomy final pathology. Risk factors identified for Gleason upgrading include patient demographic and clinical factors including age, body mass index, race, prostate volume, and biomarker based assays, including prostate-specific antigen (PSA) density, and testosterone values. In addition, prostate magnetic resonance imaging (MRI) findings have also been associated with GSU. Biopsy-specific characteristics associated with GSU include lower number of biopsy cores and lack of targeted methodology, and possibly increasing percent biopsy core positivity. Recognition of risk factors for disease undergrading may prompt confirmatory testing including repeat sampling or imaging. Continued refinements in imaging guided biopsy techniques may also reduce sampling error contributing to undergrading.

MRI-guided in-bore biopsy of the prostate – defining the optimal number of cores needed

BackgroundNumerous studies have shown that magnetic resonance imaging (MRI)-targeted biopsy approaches are superior to traditional systematic transrectal ultrasound guided biopsy (TRUS-Bx). The optimal number of biopsy cores to be obtained per lesion identified on multiparametric MRI (mpMRI) images, however, remains a matter of debate. The aim of this study was to evaluate the incremental value of additional biopsy cores in an MRI-targeted “in-bore”-biopsy (MRI-Bx) setting.Patients and methodsTwo hundred and forty-five patients, who underwent MRI-Bx between June 2014 and September 2021, were included in this retrospective single-center analysis. All lesions were biopsied with at least five biopsy cores and cumulative detection rates for any cancer (PCa) as well as detection rates of clinically significant cancers (csPCa) were calculated for each sequentially labeled biopsy core. The cumulative per-core detection rates are presented as whole numbers and as proportion of the maximum detection rate reached, when all biopsy cores were considered. CsPCa was defined as Gleason Score (GS) ≥ 7 (3 + 4).ResultsOne hundred and thirty-two of 245 Patients (53.9%) were diagnosed with prostate cancer and csPCa was found in 64 (26.1%) patients. The first biopsy core revealed csPCa/ PCa in 76.6% (49/64)/ 81.8% (108/132) of cases. The second, third and fourth core found csPCa/ PCa not detected by previous cores in 10.9% (7/64)/ 8.3% (11/132), 7.8% (5/64)/ 5.3% (7/132) and 3.1% (2/64)/ 3% (4/132) of cases, respectively. Obtaining one or more cores beyond the fourth biopsy core resulted in an increase in detection rate of 1.6% (1/64)/ 1.5% (2/132).ConclusionWe found that obtaining five cores per lesion maximized detection rates. If, however, future research should establish a clear link between the incidence of serious complications and the number of biopsy cores obtained, a three-core biopsy might suffice as our results suggest that about 95% of all csPCa are detected by the first three cores.

Prostate cancer detection rate with MRI-targeted biopsy alone using outpatient transperineal prostate biopsy.

We aimed to compare the detection rate of prostate cancer (PCa) and clinically significant (cs) PCa by magnetic resonance imaging-guided targeted biopsy (MTBx) alone and MTBx plus systematic biopsy (SBx) using an outpatient transperineal (TP) approach under local anesthesia. A retrospective study of patients who underwent outpatient TP prostate biopsy under local anesthesia at our tertiary institution between 2019 and 2022 was performed. To compare the proportions of PCa and csPCa in both pathways, McNemar's tests were used. Multivariable logistic regression model was fitted to determine the predictors of csPCa. Of 255 men included, 177 (69%) underwent MTBx alone. MTBx had similar detection rate for PCa (56%) and csPCa (47%) compared to the combination of MTBx and SBx (PCa 61%; csPCa 49%; p=0.1 and p=0.3, respectively). MTBx had lower median number of biopsy cores compared to the combination of MTBx and SBx (6 vs. 11, p<0.001). At multivariable logistic regression analysis, age (odds ratio [OR] 1.08, 95% confidence interval [CI] 1.04-1.13, p<0.001), prior negative biopsy (OR 0.19, 95% CI 0.09-0.44, p<0.001), prostate-specific antigen density cutoff ≥0.15 (OR 3.17, 95% CI 1.67-6.01, p<0.001), and prostate imaging reporting and data system ≥4 (OR 12.2, 95% CI 4.21-35.6, p<0.001) were independent predictors of csPCa. MTBx showed similar diagnostic performance to the combination of MTBx and SBx in patients undergoing outpatient TP prostate biopsy. Future studies are needed to evaluate the role of MTBx in avoiding unnecessary biopsies.

Transperineal 3-Core Magnetic Resonance Imaging Ultrasound Fusion Targeted Plus Laterally 6-Core Systematic Biopsy in Prostate Cancer Diagnosis

IntroductionIt is important to explore strategies reducing the number of SB cores taken to minimize biopsy-related morbidity and patient's discomfort during biopsy. This study aims to optimize prostate biopsy procedures by reducing the number of systematic biopsy (SB) cores while preserving cancer detection rates in the era of combined biopsy. Patients and MethodsWe prospectively recruited patients with ≥1 magnetic resonance imaging (MRI) lesions and they underwent transperineal combined 12-core SB+3-core targeted prostate biopsy (TB, reference standard). New strategy was defined as a laterally 6-core SB+3-core TB. Patients were served as their own control. Detection rates for overall prostate cancer (PCa) and clinically significant PCa (csPCa) were compared among the standard SB, MRI-TB, 6-core SB +3-core TB, and reference standard. Pathology consistency was assessed using the Kappa test. ResultsA total of 204 men were included, of which 111 (54.41%) and 92 (45.10%) harbored overall PCa and csPCa. Referenced combined biopsy detected significantly 6.86% (P = .0005) or 4.90% (P = .0044) more csPCa than performing only SB or 3-core TB, but was comparable to the new biopsy strategy. (45.10% vs. 43.14%, P = .1336) Similar results persisted when limiting patients in biopsy-naïve men or stratified by Prostate Imaging Reporting and Data System scores, PSAD, and index lesion parameters. Additionally, performing 6-core SB+3-core TB demonstrated high consistency with reference standard in grade group distribution (Kappa coefficient: 0.952 for all, 0.961 for biopsy-naïve men) and achieved superior sensitivity of 95.7% (All: 95% CI: 89.2%-99.8%) and 96.9% (Biopsy-naïve: 95% CI: 91.1%-99.7%), respectively. ConclusionsThe 6-core SB+3-core TB approach maintains expected detection rates while reducing the total core count, offering a promising alternative to the reference standard, which may help to tailor transperineal combined biopsy procedures.

The Added Value of Prostate Magnetic Resonance Imaging to Patient Selection.

There has been a rapid increase in the utilization of magnetic resonance imaging (MRI) for prostate cancer detection. The objective of this study was to measure the increase in utilization of MRI before prostate biopsy and the effects on the distribution of Prostate Imaging Reporting and Data System (PI-RAD) scores and Gleason grades over a 5-year interval in an integrated health system. The authors conducted a retrospective analysis of prostate MRI studies prior to biopsy in the calendar years of 2017 and 2022. Peak PI-RADS score, peak Gleason grade of suspected prostatic lesions, and the number of biopsy cores were collected from radiology reports and pathology reports from patients' electronic health records, respectively. All statistical tests were 2-tailed with a significance level set at p < 0.05. Categorical data analyses were performed using Mann-Whitney tests. Continuous data analyses were performed using t-tests. The total number of prostate MRIs and the number of MRIs with subsequent biopsy respectively increased by 178% and 215% over a 5-year interval (2017-2022). There was a higher proportion of MRI studies with an associated biopsy given a PI-RADS score of ≥ 3 (91%) and a Gleason grade of ≥ 7 (61%) in 2022 than in 2017 (PI-RADS: 75%; Gleason: 28%). Increased utilization of prostate MRI has been associated with a higher proportion of biopsies with high PI-RADS and Gleason scores consistent with improved patient selection in this integrated health system.

High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance.

Majority of men with low-risk prostate cancer can be managed with active surveillance (AS). This study evaluates a high-resolution diffusion-weighted imaging (HR-DWI) technique to predict adverse biopsy histology (AH), defined as Gleason score ≥7 on any biopsy or ≥3 increase in number of positive biopsy cores on systematic biopsies. We test the hypothesis that high-grade disease and progressing disease undergo subtle changes during even short intervals that can be detected by HR-DWI. In a prospective clinical trial, serial multiparametric MRIs, incorporating HR-DWI and standard DWI (S-DWI) were performed approximately 12 months apart prior to prostate biopsy (n = 59). HR-DWI, which uses reduced field-of-view and motion compensation techniques, was compared with S-DWI. HR-DWI had a 3-fold improvement in spacial resolution compared with S-DWI as confirmed using imaging phantoms. For detecting AH, multiparametric MRI using HR-DWI had a sensitivity of 75% and specificity of 83.9%, and MRI using S-DWI had a sensitivity of 71.4% and specificity of 54.8%. The AUC for HR-DWI was significantly higher (0.794 vs. 0.631, P = 0.014). Secondary analyses of univariable predictors of AH showed tumor size increase [OR 16.8; 95% confidence interval (CI): 4.06-69.48; P < 0.001] and apparent diffusion coefficient (ADC) decrease (OR 5.06; 95% CI: 1.39-18.38; P = 0.014) on HR-DWI were significant predictors of AH. HR-DWI outperforms S-DWI in predicting AH. Patient with AH have tumors that change in size and ADC that could be detected using HR-DWI. Future studies with longer follow-up should assess HR-DWI for predicting disease progression during AS. We report on a prospective clinical trial using a MRI that has three times the resolution of standard MRI. During AS for prostate cancer, two high-resolution MRIs performed approximately a year apart can detect tumor changes that predict the presence of aggressive cancers that should be considered for curative therapy such as prostatectomy or radiation.

Antimicrobial prophylaxis protocol based on rectal swab culture before prostate biopsy to prevent infectious complications: a prospective randomized comparative study.

To evaluate the benefit of targeted antibiotic prophylaxis (TAP) based on rectal swab culture in comparison with standard empiric antimicrobial prophylaxis in patients undergoing transrectal ultrasound-guided needle biopsy of the prostate (TRUS-BP), as well as to assess rate of fecal carriage of Fluoroquinolone-resistant Enterobacterales FQRE. We prospectively analyzed data that randomized 157 patients within two groups: (G1) TAP according to rectal swab performed 10days before PB; (G2): empirical antibiotic prophylaxis with ciprofloxacin. Prevalence of FQRE digestive carriage and risk factors were investigated. Incidence of infectious complications after (TRUS-BP) in each group was compared. G2 included 80 patients versus 77 in G1. There was no difference between the two groups regarding age, diabetes, prostate volume, PSA, number of biopsy cores, and risk factors for FQRE. In G2, the prevalence of FQRE digestive carriage was 56.3% all related to E. coli species. In the case of digestive carriage of FQRE, TAP according to the rectal swab culture with third-generation cephalosporins was performed in 73.3%. Patients with FQRE had history of FQ use within the last 6months in 17.8% (p = 0.03). Rate of febrile urinary tract infection after PB was 13% in G1 and 3.8% in G2 (p = 0.02). Incidence of FQ resistance in the intestinal flora of our local population was prevalent. Risk factor for resistance was the use of FQ within the last 6months. TAP adapted to rectal swab, mainly with third-generation cephalosporins, significantly reduced the rate of infectious complications after (TRUS-BP).

Predictive Factors for Extracapsular Extension of Prostate Cancer to Select the Candidates for Nerve-sparing Radical Prostatectomy.

Nerve-sparing radical prostatectomy (NSRP) for prostate cancer (PC) enables better postoperative recovery of continence and potency but may increase the risk of positive surgical margins. This study aimed to investigate preoperative predictive factors for extracapsular extension (ECE) of PC to select patients for NSRP. We retrospectively evaluated 288 patients with PC (576 lobes) diagnosed with 12-core transrectal ultrasound-guided biopsy and magnetic resonance imaging (MRI) who underwent laparoscopic or robot-assisted radical prostatectomy at our institution. Surgical specimens and preoperative parameters (prostate-specific antigen, prostate volume, biopsy and MRI findings, preoperative therapy) were analyzed. Of 576 prostate lobes, the incidence Ipsilateral ECE was identified in 97 (16.8%) lobes. The higher number of unilateral positive biopsy cores, the highest Gleason score 8 or more and positive unilateral findings on MRI are significant higher in prostate sides with ECE in univariate analysis. In multivariate analysis, positive unilateral MRI findings (odds ratio [OR], 2.86; p < 0.001) and unilateral biopsy positive core ≥ 3 (OR, 3.73; p < 0.001) were independent predictors of unilateral ECE. The detection rate of unilateral ECE in those cases with two factors (side-specific positive biopsy core 2 or less and side-specific MRI findings negative) was 7.1% (19/269). Patients with fewer unilateral positive biopsy cores and negative unilateral MRI findings might be good candidates for NSRP.

Analysis of biopsy pathology and risk factors of lymph node metastasis in prostate cancer.

To explore the relationship between biopsy pathology and lymph node metastasis in patients with prostate cancer (PCa), and to identify risk factors of lymph node metastasis (LNM). Patients diagnosed with prostate cancer were respective screened between Jan 2015 and May 2022. Patients diagnosed PCa via 13-core ultrasound-guided biopsies and underwent radical prostatectomy and lymph node dissection were identified. The clinicopathological characteristics of the patients were recorded. Relationships between LNM and non-LNM were analyzed using chi-square and independent samples t-test. Logistic regression model was fitted to analyze the risk factors of lymph node metastases. Two hundreds and fifteen patients were included, sixty-seven patients had lymph node metastasis. Gleason scores in LNM group were higher than that in non-LNM group (8.5 ± 0.9 VS 7.5 ± 1.5, p < 0.001), positive biopsy in non-LNM group was significantly lower than that in LNM group (p < 0.001), Binary logistic regression analysis indicated number of positive biopsy and number of removed lymph nodes increased the risks of LNM (odds ratio, OR = 1.28, 95% confidence interval, CI = 1.16-1.42, p < 0.001; OR = 1.11, 95% CI = 1.06-1.17, p < 0.001; respectively). Number of positive biopsy in internal gland but not external gland was significant associated with LNM (OR = 1.66, 95% CI = 1.34-2.06, p < 0.001; OR = 1.19, 95% CI = 0.88-1.61, p = 0.262; respectively). The patients with lymph nodes dissection more than 13 were about four times more likely to detect lymph node metastasis than those fewer than 13 (OR = 3.92, 95% CI = 2.10-7.33, p < 0.001). The risk of lymph node metastasis increased with the number of positive prostate biopsy cores, and tumors in the internal gland were more likely to cause lymph node metastasis. In addition, lymph node metastasis was more likely to be found when the number of lymph nodes dissection was greater than 13.

Accuracy, safety, and diagnostic prediction of percutaneous renal mass biopsy and subsequent changes in treatment.

The incidence of renal tumours is increasing annually, and imaging alone cannot meet the diagnostic needs. This single-centre study aimed to evaluate the predictors of diagnostic imaging-guided percutaneous renal mass biopsy (PRMB), its accuracy and safety, and subsequent changes to the treatment plan. We retrospectively collected the clinical data of patients who had undergone PRMB. The diagnosis rate, pathological data, and complications were analysed. Potential predictors of a diagnostic PRMB were evaluated using logistic regression analysis. Changes to the treatment plan due to PRMB results were also analysed. A total of 158 patients were included in this study. The univariate analysis showed that higher tumour diameter (OR = 1.223, 95% CI: 1.018-1.468, p = 0.031) and number of biopsy cores ≥ 2 (OR = 6.125, 95% CI: 2.006-18.703, p = 0.001) were significantly associated with diagnostic biopsy, and multivariate analysis results showed that higher tumour diameter (OR = 1.215, 95% CI: 1.008-1.463, p = 0.041) was an independent predictor of diagnostic biopsy. A nomogram including tumour diameter and number of biopsy cores was constructed to predict diagnostic biopsy. Compared with postoperative pathology, the concordance between biopsy and postoperative pathology at identifying malignancies, histologic type, and histologic grade were 100% (47/47), 85.1% (40/47), and 54.1% (20/37), respectively. The treatment plans of 15 patients (9.5%) changed based on the PRMB results. Fourteen patients (8.9%) had minor complications (Clavien-Dindo classification < 2). Our results suggest that tumour diameter was an independent predictor of diagnostic biopsy. Furthermore, PRMB can be accurately and safely performed and may guide clinical decision-making for patients with renal tumours.

STUDY OF THE TRANSRECTAL PROSTATE BIOPSY IN KANAZAWA MEDICAL UNIVERSITY HOSPITAL, SUMMARY OF 20 YEARS

(Background) Prostate biopsy is the most important examination for the diagnosis of prostate cancer and the most common test in urology. We evaluated the results of transrectal prostate biopsy performed at Kanazawa Medical University Hospital. (Materials and Methods) Prospectively collected cases of 1,935 patients was undergone transrectal prostate biopsy between January 2002 and December 2021. We looked at patients age, serum Prostate-specific antigen (PSA) level, PSA density (PSAD), total prostate volume, number of biopsy times, and number of biopsy core. We examined the positive cancer rate. (Results) The median age of patients was 70 years (range: 36-96 years), the median serum PSA level was 7.6 ng/mL (range: 0.19-15,823 ng/mL), the mean number of biopsy times was 1.3 (range: 1-6), and the mean number of total biopsy core was 8.6 (range: 2-14). There were 880 (45.5%) positive cases and 1,055 (54.5%) negative cases. Comparing the cancer-positive and cancer-negative groups, the median age was 72 years (range: 50-96 years), 68 years (range: 36-93 years), the median PSA level was 11.0 ng/mL (range: 0.19-15,823 ng/mL), 6.6 ng/mL (range: 0.24-86.0 ng/mL), median prostate volume was 29.5 mL (range: 7.4-206 mL), 39.1 mL (range: 9.6-178 mL), median PSAD 0.376 (range: 0.003-1,582), 0.177 (range: 0.016-7.513), mean number of biopsy times 1.2 (range: 1-6), 1.4 (range: 1-6), mean number of total biopsy core 8.9 (range: 2-14), 8.5 (range: 4-14), and there was a significant difference in all comparison.

Prediction model of gleason score upgrading after radical prostatectomy based on a bayesian network

ObjectiveTo explore the clinical value of the Gleason score upgrading (GSU) prediction model after radical prostatectomy (RP) based on a Bayesian network.MethodsThe data of 356 patients who underwent prostate biopsy and RP in our hospital from January 2018 to May 2021 were retrospectively analysed. Fourteen risk factors, including age, body mass index (BMI), total prostate-specific antigen (tPSA), prostate volume, total prostate-specific antigen density (PSAD), the number and proportion of positive biopsy cores, PI-RADS score, clinical stage and postoperative pathological characteristics, were included in the analysis. Data were used to establish a prediction model for Gleason score elevation based on the tree augmented naive (TAN) Bayesian algorithm. Moreover, the Bayesia Lab validation function was used to calculate the importance of polymorphic Birnbaum according to the results of the posterior analysis and to obtain the importance of each risk factor.ResultsIn the overall cohort, 110 patients (30.89%) had GSU. Based on all of the risk factors that were included in this study, the AUC of the model was 81.06%, and the accuracy was 76.64%. The importance ranking results showed that lymphatic metastasis, the number of positive biopsy cores, ISUP stage and PI-RADS score were the top four influencing factors for GSU after RP.ConclusionsThe prediction model of GSU after RP based on a Bayesian network has high accuracy and can more accurately evaluate the Gleason score of prostate biopsy specimens and guide treatment decisions.

Machine Learning and Explainable Artificial Intelligence to Predict Occult Pelvic Nodal Metastases in Prostate Cancer.

Determination of risk of occult pelvic lymph node involvement (LNI) in patients with cN0 prostate cancer is critical for determination of optimal treatment options. Though several nomograms exist, machine learning (ML) approaches might enable physicians to better assess individual risk by incorporating multiple clinical risk factors. Herein, we developed a ML model to predict occult LNI, and explained its composition using an explainable artificial intelligence (XAI) framework. Patients with cN0 prostate adenocarcinoma diagnosed from 2018-2020 were identified in the National Cancer Database. The query was limited to patients with known clinical staging and biopsy results who did not receive neoadjuvant therapy prior to pelvic nodal examination. Occult LNI was defined as pN1 disease based on surgical evaluation, with a minimum of 10 nodes examined. Five ML models were trained to predict LNI. Variables incorporated into the model were age, core biopsy results, Gleason scores, preoperative prostate specific antigen (PSA), and clinical T-stage. Model performance, measured using area under the receiver operator characteristic curve (AUC) on a holdout testing dataset, was compared to multivariable logistic regression. The best-performing model was explained using SHapley Additive exPlanation (SHAP) values. To permit more clinically-meaningful statistical interpretation, using a novel approach SHAP values were converted into odds ratios (OR), confidence intervals (CI), and p-values. A total of 23,131 patients met inclusion criteria; 2,676 (11.6%) had occult LNI. The Extreme Gradient Boosting model outperformed all other models with an AUC of 0.82 (95% CI: 0.78-0.86) compared to 0.80 (95% CI: 0.76-0.84) for logistic regression. Increasing PSA (OR: 1.031; p<0.001), number of positive biopsy cores (OR: 1.055; p<0.001), and percent positive biopsy cores (OR: 1.01; p<0.001) were all associated with increased risk of LNI. Based on observation of SHAP dependence plots, risk of LNI plateaued at PSA>20 ng/dL and >11 positive cores, while no plateau was observed for percent positive biopsy cores. Relative to T1c disease, patients with T3b were at highest risk of LNI (OR: 1.461; p = 0.003). Gleason score of 9 was associated with significant risk of LNI (Ref: Gleason 6; OR: 1.891; p<0.001). This was primarily driven by the primary Gleason score; primary Gleason 5 disease was associated with significant risk of LNI (Ref: Gleason 3; OR: 1.915; p<0.001) while a secondary Gleason score of 5 was the only grade with significant increased risk of LNI (Ref: Gleason 3; OR: 1.185; p = 0.004). Age and number of cores examined were not significant predictors of LNI. Our ML achieved improved performance relative to logistic regression at predicting occult LNI. XAI provided insight into the inner-working of the ML model. ML can be used to identify patients at risk for occult LNI and therefore inform clinical decision-making.

Outcomes of a Diagnostic Pathway for Prostate Cancer Based on Biparametric MRI and MRI-Targeted Biopsy Only in a Large Teaching Hospital.

Diagnostic pathways for prostate cancer (PCa) balance detection rates and burden. MRI impacts biopsy indication and strategy. A prospectively collected cohort database (N = 496) of men referred for elevated PSA and/or abnormal DRE was analyzed. All underwent biparametric MRI (3 Tesla scanner) and ERSPC prostate risk-calculator. Indication for biopsy was PIRADS ≥ 3 or risk-calculator ≥ 20%. Both targeted (cognitive-fusion) and systematic cores were combined. A hypothetical full-MRI-based pathway was retrospectively studied, omitting systematic biopsies in: (1) PIRADS 1-2 but risk-calculator ≥ 20%, (2) PIRADS ≥ 3, receiving targeted biopsy-cores only. Significant PCa (GG ≥ 2) was detected in 120 (24%) men. Omission of systematic cores in cases with PIRADS 1-2 but risk-calculator ≥ 20%, would result in 34% less biopsy indication, not-detecting 7% significant tumors. Omission of systematic cores in PIRADS ≥ 3, only performing targeted biopsies, would result in a decrease of 75% cores per procedure, not detecting 9% significant tumors. Diagnosis of insignificant PCa dropped by 52%. PCa undetected by targeted cores only, were ipsilateral to MRI-index lesions in 67%. A biparametric MRI-guided PCa diagnostic pathway would have missed one out of six cases with significant PCa, but would have considerably reduced the number of biopsy procedures, cores, and insignificant PCa. Further refinement or follow-up may identify initially undetected cases. Center-specific data on the performance of the diagnostic pathway is required.

Hypofractionated Radiotherapy for Prostate Cancer (HRT20) Trial: Results

Radiotherapy is a definitive standard treatment for men with localized prostate cancer. Improvements in technology allow higher doses of radiation delivered to the prostate in less days, with lower doses to surrounding healthy tissues trying to reduce side effects. Several studies demonstrated that hypofractionated radiotherapy of 60 Gy is non-inferior to conventional radiotherapy fractionation. To report the first results in toxicity and biochemical relapse free survival of HRT20 trial after 5 years of recruitment. The HRT20 trial is registered (NCT03851926). A prospective observational study enrolled 298 men with localized prostate cancer (pT1b-T3aN0M0) started in October 2016. Median age was 75 (53-91). Median PSA was 14.5ng/ml. Using NCCN classification: 12,6% were low risk, 50.5% were intermediate and 36.9 % were high risk patients. The median duration of the treatment was 30 days (23-41) The clinical target volume (CTV) consisted of the prostate and seminal vesicles when affected. The CTV-planning target volume (PTV) margin was 0.8 cm. The primary end point evaluated assessed by CTCAE 4.0 was related later adverse events and the second end point was biochemical relapse free survival by Phoenix definition. WMAT plans calculations were carried out using the Monaco TPS version 5.10 based on a single arc arrangement. Univariate and multivariate logistic analyses were carried out. After median follow up of 56 months. The 5-year incidence rates of grade 2 and 3 late gastrointestinal toxicities were 6.3% and 3.1%, respectively, and those of grade 2 and 3 late genitourinary toxicities were 7.9% and 1%, respectively. PTV volume was found to be correlated with late rectal bleeding (p = 0.02). Also, urinary tract obstruction was correlated with PTV volume. The proportion of patients who were biochemical failure free at 5 years was 88·3% (95% CI 86·0-90·2) Multivariate analysis indicated that risk stage (p = 0.03) and number of positive needles biopsy cores (p = 0.02) were prognostic factor for biochemical relapse-free survival rates. The findings of this study indicate that hypofractionated is well tolerated and it is associated with good 5-years tumor-control outcomes in patients with localized prostate cancer. Long-term follow-up continues.

The impact of a second MRI and re-biopsy in patients with initial negative mpMRI-targeted and systematic biopsy for PIRADS ≥ 3 lesions

ObjectiveTo evaluate the proportions of detected prostate cancer (PCa) and clinically significant PCa (csPCa), as well as identify clinical predictors of PCa, in patients with PI-RADS > = 3 lesion at mpMRI and initial negative targeted and systematic biopsy (initial biopsy) who underwent a second MRI and a re-biopsy.MethodsA total of 290 patients from 10 tertiary referral centers were included. The primary outcome measures were the presence of PCa and csPCa at re-biopsy. Logistic regression analyses were performed to evaluate predictors of PCa and csPCa, adjusting for relevant covariates.ResultsForty-two percentage of patients exhibited the presence of a new lesion. Furthermore, at the second MRI, patients showed stable, upgrading, and downgrading PI-RADS lesions in 42%, 39%, and 19%, respectively. The interval from the initial to repeated mpMRI and from the initial to repeated biopsy was 16 mo (IQR 12–20) and 18 mo (IQR 12–21), respectively. One hundred and eight patients (37.2%) were diagnosed with PCa and 74 (25.5%) with csPCa at re-biopsy. The presence of ASAP on the initial biopsy strongly predicted the presence of PCa and csPCa at re-biopsy. Furthermore, PI-RADS scores at the first and second MRI and a higher number of systematic biopsy cores at first and second biopsy were independent predictors of the presence of PCa and csPCa. Selection bias cannot be ruled out.ConclusionsPersistent PI-RADS ≥ 3 at the second MRI is suggestive of the presence of a not negligible proportion of csPca. These findings contribute to the refinement of risk stratification for men with initial negative MRI-TBx.

Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms.

Innovative strategies are necessary to enhance prostate cancer diagnosis whilst reducing unnecessary and invasive repeat biopsies. This study aimed to determine the significant parameters affecting repeat prostate biopsy outcomes and develop an optimal machine learning algorithm for predicting positive repeat prostate biopsy results. We analysed data from 174 men who underwent repeated prostate biopsies between January 2008 and December 2022. Systematic multiple-core, ultrasound-targeted prostate biopsies were performed, each two samples from prostatic transitional zone and peripheral zone were obtained bilaterally. Clinical characteristics were collected, including patients' age, initial prostate volume, prostate-specific antigen (PSA) level, free PSA (fPSA)/PSA ratio, biopsy core numbers, pathological result; The time interval between first and latest prostate biopsy; Latest PSA level, fPSA/PSA ratio, biopsy core numbers; And final pathological diagnosis. Six feature selection methods, namely, variable ranking, correlation matrix, random forest regression, recursive feature elimination, cross-validation and forward selection, were employed to identify key influencing factors for repeat biopsy outcomes. Subsequently, the performance of seven machine learning algorithms, namely, multivariable logistic regression (LR), K-nearest neighbour search (KNN), support vector classification (SVC), decision tree (DT), random forest classifier (RF), naïve Bayes classifier (NBC) and gradient booster tree (GB), was assessed based on accuracy, misclassification, recall, specificity, precision and receiver operating characteristic (ROC) area under the curve (AUC). About 70% of patients were used as the training dataset, meanwhile remaining 30% as validation dataset. 52 were ultimately diagnosed with prostate cancer following the final pathological examination. The remaining 122 patients were negative. Amongst six feature selection methods, the variable ranking emerged as the most effective method for identifying the essential factors influencing repeat biopsy results. Amongst the machine learning algorithms, SVC demonstrated superior accuracy (0.7365), low recall rate (0.2500) and low misclassification rate (0.2093) for both patients with cancer and healthy individuals. Meanwhile, the ROC curve of SVC showed a relatively high AUC (0.6871). We developed an SVC-based machine learning algorithm for predicting positive repeat prostate biopsy results. Our analysis revealed that initial and latest prostate volumes, initial and latest PSA levels, latest fPSA/PSA ratio and age are significant factors for this model.