Concordance between Gleason scores of needle biopsies and radical prostatectomy specimens: a population-based study

Ultrasound radiomics model based on grayscale transrectal ultrasound-guided biopsy for diagnosing prostate cancer and predicting distant metastasis

OBJECTIVE

A prostate ultrasound (US) imaging omics model was established to assess its effectiveness in diagnosing prostate cancer (PCa), predicting Gleason score (GS), and determining the likelihood of distant metastasis.

METHODS

US images of patients with prostate pathology confirmed by biopsy or surgery at our hospital were retrospectively analyzed. Regions of interest (ROI) segmentation, feature extraction, feature screening, and the construction and training of the radiomics model were performed.

RESULTS

Area under the curve (AUC) for the magnetic resonance imaging Prostate Imaging Reporting and Data System (MRI PI-RADS) classification, radiomics alone, and radiomics combined with prostate-specific antigen (PSA) in diagnosing PCa were 70.74%, 71.13%, and 90.47%, respectively. AUCs for the MRI PI-RADS classification, radiomics alone, and radiomics combined with PSA in predicting the GS of PCa were 75.6%, 74.7%, and 88.9%, respectively. Furthermore, AUCs for MRI PI-RADS classification and radiomics alone in predicting distant metastasis of PCa were 66.7% and 90.8%, respectively.

CONCLUSION

The combination of ultrasonic imaging omics and serum PSA significantly improves the efficiency of PCa diagnosis, GS prediction, and distant metastasis prediction. This method is an important tool for PCa screening and follow-up.

Ten-Year Outcomes of a Phase 3, Multicenter, Randomized Controlled Trial (SHIP0804) With 3-Month Neoadjuvant Androgen Deprivation Prior to 125I-Seed Transperineal Prostate Brachytherapy Followed by Nil Versus 9-Month Adjuvant Hormonal Therapy in Patients With Intermediate-Risk Prostate Cancer

PURPOSE

To analyze the effects of adjuvant hormonal therapy (AHT) on time to event after neoadjuvant androgen deprivation therapy (ADT) and 125I-transperineal prostate brachytherapy (TPPB), compared with neoadjuvant ADT and TPPB only, in patients with intermediate-risk prostate cancer (IRPC).

METHODS AND MATERIALS

In this multicenter, open-label, phase 3 randomized controlled trial (SHIP0804), 421 patients with IRPC were randomly assigned to either 9-month AHT (AHT arm) or no AHT (non-AHT arm) after 3 months of neoadjuvant ADT and TPPB. The primary endpoint was biochemical progression-free survival, and secondary endpoints included overall survival and clinical progression-free survival. Prostatic biopsy results 36 months after treatment were evaluated in a correlative investigation (SHIP36B).

RESULTS

With a median follow-up of over 11 years, the 10-year biochemical progression-free survival rates were comparable: 82.9% in the AHT group and 78.4% in the non-AHT group (P = .51). Results were consistent across key prognostic indicators such as age at randomization, baseline prostate-specific antigen level, clinical stage, Gleason grade group, number of National Comprehensive Cancer Network intermediate-risk factors, and prostatic volume. The secondary endpoints, including overall survival and clinical progression-free survival, were also comparable between the 2 arms. Grade 3 or higher adverse events occurred in 5.4% and 1.4% of patients in the AHT and non-AHT arms, respectively. At 36-month post-TPPB prostate biopsy, only 3.1% of biopsied patients tested positive for residual tumors. There were no deaths due to prostate cancer in either group.

CONCLUSIONS

Adding 9-month AHT to TPPB after 3-month neoadjuvant ADT did not improve long-term outcomes in patients with IRPC. These findings suggest that moderate-term AHT may not offer substantial benefits and thus should not be considered a standard treatment in this population with IRPC.

Prioritizing precision: detection of prostate cancer using mri guided fusion needle biopsy across the pennsylvania urologic regional collaborative

PURPOSE

Targeted prostate biopsies are increasingly being performed by urologists in the United States including those in the Pennsylvania Urologic Regional Collaborative, a physician-led data-sharing and quality improvement collaborative. To evaluate the performance of MRI guided fusion needle prostate biopsies in the collaborative, we analyzed the variability by practice in rates of detection of clinically significant prostate cancer and patient characteristics associated with detection of clinically significant prostate cancer.

METHODS

We analyzed 857 first-time MRI fusion biopsy procedures performed at five practices (minimum 20 procedures) between 2015 and 2019. We used chi-square analysis for baseline patient characteristics and Grade Group (GG) ≥ 3 tumor detection rates by practice. Multivariable logistic regression was used to estimate the odds of clinically significant cancer detection when adjusting for baseline patient characteristics.

RESULTS

Approximately 15% of men undergoing targeted MRI guided biopsy were ≤ 59 years old. Median prostate specific antigen (PSA) was 6.8 ng/ml. Detection rates for GG ≥ 3 tumors ranged from 14.3% to 28.3% (P = 0.02) across practices. However, the odds of GG ≥ 3 tumor detection did not differ significantly between practices after adjusting for clinical and radiographic factors. Overall, increased likelihood of detecting a GG ≥ 3 tumor was associated with increased age, DRE abnormalities, higher PSA, smaller gland volume and PI-RADS ≥ 4 MRI lesions. There was an 81% concordance rate between PI-RADS ≥ 4 and Gleason grade ≥ 3 prostate cancer.

CONCLUSION

We demonstrate the value of obtaining pre-biopsy MRI given high concordance between presence of suspicious lesions and MRI-targeted biopsy detection of clinically significant prostate cancer. Variability of baseline patient characteristics among practices may account for the observed differences in clinically significant cancer detection rates. These findings can aid standardization and quality improvement efforts within the collaborative.

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models

OBJECTIVE

To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.

METHODS

Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.

RESULTS

The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.

CONCLUSIONS

Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.

CLINICAL RELEVANCE STATEMENT

The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.

KEY POINTS

• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Early experience with targeted and combination biopsies in prostate cancer work-up in Denmark from 2012 to 2016

PURPOSE

To investigate the early implementation of combined systematic and targeted (cBx) primary biopsy in prostate cancer diagnosis and define the concordance in Gleason grading (GG) of different biopsy techniques with radical prostatectomy (RP) pathology.

METHODS

This population-based analysis includes data on all men in Denmark who underwent primary cBx or standalone systematic (sBx) prostate biopsy between 2012 and 2016. Biopsy results were compared to RP pathology if performed within a year. Concordance measurement was estimated using Cohen's Kappa, and the cumulative incidence of cancer-specific death was estimated at 6 years with the Aalen-Johansen estimator.

RESULTS

Concordance between biopsy and RP pathology was 0.53 (95CI: 0.43-0.63), 0.38 (95CI: 0.29-0.48), and 0.16 (95CI: 0.11-0.21) for cBx, targeted biopsy (tBx), and sBx, respectively. For standalone sBx and RP, concordance was 0.29 (95CI: 0.27-0.32). Interrelated GG concordance between tBx and sBx was 0.67 (95CI: 0.62-0.71) in cBx. The proportion of correctly assessed GG based on RP pathology was 54% in both cBx and standalone sBx. Incidence of prostate cancer-specific death was 0% regardless of biopsy technique in GG 1, and 22% (95CI: 11-32), 30% (95CI: 15-44), and 19% (95CI: 7.0-30) in GG 5 for cBx, tBx, or sBx, respectively.

CONCLUSION

Overall, the cBx strategy demonstrates higher concordance to RP pathology than the standalone sBx. However, cBx exhibits more overgrading of the GG of RP pathology compared to sBx. Ultimately, the classic grading system does not take change in the diagnostic pathway into account, and grading should be altered accordingly to ensure appropriate treatment.

Design, synthesis and evaluation of novel prostate-specific membrane antigen-targeted aryl [18F]fluorosulfate PET tracers

The expression of prostate-specific membrane antigen (PSMA) in prostate cancer is 100-1000 times higher than that in normal tissues, and it has shown great advantages in the diagnosis and treatment of prostate cancer. The combination of PSMA and PET imaging technology based on the principle of metabolic imaging can achieve high sensitivity and high specificity for diagnosis. Due to its suitable half-life (109 min) and good positron abundance (97%), as well as its cyclotron accelerated generation, 18F has the potential to be commercialize, which has attracted much attention. In this article, we synthesized a series of fluorosulfate PET tracers targeting PSMA. All four analogues have shown high affinity to PSMA (IC50 = 1.85-5.15 nM). After the radioisotope exchange labeling, [18F]L9 and [18F]L10 have PSMA specific cellular uptake (0.65 ± 0.04% AD and 1.19 ± 0.03% AD) and effectively accumulated in 22Rv1 xenograft mice model. This study demonstrates that PSMA-1007-based PSMA-targeted aryl [18F]fluorosulfate novel tracers have the potential for PET imaging in tumor tissues.

MRI-Ultrasound Fused Approach for Prostate Biopsy-How It Is Performed

The use of MRI-ultrasound image fusion targeted biopsy of the prostate in the face of an elevated serum PSA is now recommended by multiple societies, and results in improved detection of clinically significant cancer and, potentially, decreased detection of indolent disease. This combines the excellent sensitivity of MRI for clinically significant prostate cancer and the real-time biopsy guidance and confirmation of ultrasound. Both transperineal and transrectal approaches can be implemented using cognitive fusion, mechanical fusion with an articulated arm and electromagnetic registration, or pure software registration. The performance has been shown comparable to in-bore MRI biopsy performance. However, a number of factors influence the performance of this technique, including the quality and interpretation of the MRI, the approach used for biopsy, and experience of the practitioner, with most studies showing comparable performance of MRI-ultrasound fusion to in-bore targeted biopsy. Future improvements including artificial intelligence promise to refine the performance of all approaches.

Could Prognostic Nutritional Index be a new criteria for active surveillance of prostate cancer?

OBJECTIVE

To evaluate the importance of the Prognotic Nutritional Index(PNI) value for patient selection of active surveillance(AS) in prostate cancer.

METHODS

Between September 2020 and June 2022, the data of 125-patients who underwent Robot-Assisted-Laparoscopic-Prostatectomy(RALP) were retrospectively analyzed. All patients were suitable for AS preoperatively. Using the pathological results of RALP, patients have been divided two groups. Patients who met the criteria for AS were defined as the first group, others were defined second. Demographic datas, PNI values and hematological parameters of the groups were compared.

RESULTS

38% (n:48) patients were found suitable for the group1, and 62%(n:77) were found suitable for the group 2. Upgrading and upstaging were found at 76 patients (61%) and 26(21%), respectively. There is no significant difference between groups on age, BMI, PSA, PSA-density, prostate volume, and PIRADS. PNI value was found higher at first group. The value of 49.45 was calculated by ROC analysis as the ideal PNI cut-off value for predicting upgrading and upstaging of prostate cancer (P < ,001). According to the both univariate and multivariate regression analysis, PNI was found a predictor for exclusion from AS (P < ,001).

CONCLUSION

Upgrading and upstaging are detected at a higher rate in patients with low PNI values. The use of PNI value in the selection of patients to AS will increase the success rate of ideal patient selection.

ACR Appropriateness Criteria® Pretreatment Detection, Surveillance, and Staging of Prostate Cancer: 2022 Update

Prostate cancer is second leading cause of death from malignancy after lung cancer in American men. The primary goal during pretreatment evaluation of prostate cancer is disease detection, localization, establishing disease extent (both local and distant), and evaluating aggressiveness, which are the driving factors of patient outcomes such as recurrence and survival. Prostate cancer is typically diagnosed after the recognizing elevated serum prostate-specific antigen level or abnormal digital rectal examination. Tissue diagnosis is obtained by transrectal ultrasound-guided biopsy or MRI-targeted biopsy, commonly with multiparametric MRI without or with intravenous contrast, which has recently been established as standard of care for detecting, localizing, and assessing local extent of prostate cancer. Although bone scintigraphy and CT are still typically used to detect bone and nodal metastases in patients with intermediate- or high-risk prostate cancer, novel advanced imaging modalities including prostatespecific membrane antigen PET/CT and whole-body MRI are being more frequently utilized for this purpose with improved detection rates. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Initial Outcomes of Freehand Transperineal Biopsies Regarding Diagnostic Value and Safety: An Early Experience at King Fahad Specialist Hospital, Buraydah, Saudi Arabia

BACKGROUND

Prostate cancer is a common type of cancer in Saudi Arabia with a high incidence rate. Trans-rectal ultrasound guided prostatic biopsy (TRUSBx) has been the standard diagnostic study for prostate cancer since a landmark study in 1989 which showed that it is better than digitally directed biopsy sampling of the prostate. As an alternative to TRUSBx, transperineal biopsies (TPBx) have gained popularity as they give a higher accuracy rate and avoid many complications. A new study has been conducted in Riyadh, Saudi Arabia to compare TRUSBx and TPBx showed that TPBx has a significantly higher detection rate of prostate cancer cases compared to TRUSBx (45.1% vs. 29.1%, p=0.003). The aim of this study is to determine the diagnostic value and safety of freehand transperineal prostate biopsy in patients with an elevated prostatic specific antigen (PSA) and/or abnormal digital rectal exam in King Fahad Specialist Hospital KFSH in Buraydah, Qassim region, Saudi Arabia.

METHODS

This is an observational retrospective study of all patients (n=39) who underwent transperineal biopsies at KFSH to assess the diagnostic value and safety of the procedure.

RESULTS

The mean age of the patients was 70.3 (SD 10.1) years. The most commonly found diagnosis was adenocarcinoma (61.5%), and incidence of complications was detected in (5.1%) of the patients.

CONCLUSION

We concluded that the freehand technique TPBx has a high accuracy rate in detecting prostatic cancer. However, the learning curve could be a limiting factor in implementing it. Increasing the number of biopsies could positively affect diagnostic accuracy, especially with our low complication rate in this procedure. A low number of biopsies in the older age group can give an accurate result with a low risk of complications. Although template-guided TPBx and robot-guided TPBx are better options, the freehand technique represents a cost-effective and time-saving alternative. However, more studies are needed to compare the outcome of such a technique.

Evaluation of Matrix Metalloproteases by Artificial Intelligence Techniques in Negative Biopsies as New Diagnostic Strategy in Prostate Cancer

Usually, after an abnormal level of serum prostate-specific antigen (PSA) or digital rectal exam, men undergo a prostate needle biopsy. However, the traditional sextant technique misses 15-46% of cancers. At present, there are problems regarding disease diagnosis/prognosis, especially in patients' classification, because the information to be handled is complex and challenging to process. Matrix metalloproteases (MMPs) have high expression by prostate cancer (PCa) compared with benign prostate tissues. To assess the possible contribution to the diagnosis of PCa, we evaluated the expression of several MMPs in prostate tissues before and after PCa diagnosis using machine learning, classifiers, and supervised algorithms. A retrospective study was conducted on 29 patients diagnosed with PCa with previous benign needle biopsies, 45 patients with benign prostatic hyperplasia (BHP), and 18 patients with high-grade prostatic intraepithelial neoplasia (HGPIN). An immunohistochemical study was performed on tissue samples from tumor and non-tumor areas using specific antibodies against MMP -2, 9, 11, and 13, and the tissue inhibitor of MMPs -3 (TIMP-3), and the protein expression by different cell types was analyzed to which several automatic learning techniques have been applied. Compared with BHP or HGPIN specimens, epithelial cells (ECs) and fibroblasts from benign prostate biopsies before the diagnosis of PCa showed a significantly higher expression of MMPs and TIMP-3. Machine learning techniques provide a differentiable classification between these patients, with greater than 95% accuracy, considering ECs, being slightly lower when considering fibroblasts. In addition, evolutionary changes were found in paired tissues from benign biopsy to prostatectomy specimens in the same patient. Thus, ECs from the tumor zone from prostatectomy showed higher expressions of MMPs and TIMP-3 compared to ECs of the corresponding zone from the benign biopsy. Similar differences were found for expressions of MMP-9 and TIMP-3, between fibroblasts from these zones. The classifiers have determined that patients with benign prostate biopsies before the diagnosis of PCa showed a high MMPs/TIMP-3 expression by ECs, so in the zone without future cancer development as in the zone with future tumor, compared with biopsy samples from patients with BPH or HGPIN. Expression of MMP -2, 9, 11, and 13, and TIMP-3 phenotypically define ECs associated with future tumor development. Also, the results suggest that MMPs/TIMPs expression in biopsy tissues may reflect evolutionary changes from prostate benign tissues to PCa. Thus, these findings in combination with other parameters might contribute to improving the suspicion of PCa diagnosis.

P-score in preoperative biopsies accurately predicts P-score in final pathology at radical prostatectomy in patients with localized prostate cancer

BACKGROUND

Prostate cancer (PCa) is a highly heterogeneous, multifocal disease, and identification of clinically significant lesions is challenging, which complicates the choice of adequate treatment. The Prostatype® score (P-score) is intended to guide treatment decisions for newly diagnosed PCa patients based on a three-gene signature (IGFBP3, F3, and VGLL3) and clinicopathological information obtained at diagnosis. This study evaluated association of the P-score measured in preoperative magnetic resonance imaging/transrectal ultrasound fusion-guided core needle biopsies (CNBs) and the P-score measured in radical prostatectomy (RP) specimens of PCa patients. We also evaluated the P-score association with the pathology of RP specimens. Furthermore, concordance of the P-score in paired CNB and RP specimens, as well as in index versus concomitant nonindex tumor foci from the same RP was investigated.

METHODS

The study included 100 patients with localized PCa. All patients were diagnosed by CNB and underwent RP between 2015 and 2018. Gene expression was assessed with the Prostatype® real-time quantitative polymerase chain reaction kit and the P-score was calculated. Patients were categorized into three P-score risk groups according to previously defined cutoffs.

RESULTS

For 71 patients, sufficient CNB tumor material was available for comparison with the RP specimens. The CNB-based P-score was associated with the pathological T-stage in RP specimens (p = 0.02). Moreover, the CNB-based P-score groups were in substantial agreement with the RP-based P-score groups (weighted κ score: 0.76 [95% confidence interval, 95% CI: 0.60-0.92]; Spearman's rank correlation coefficient r = 0.83 [95% CI: 0.74-0.89]; p < 0.0001). Similarly, the P-score groups based on paired index tumor and concomitant nonindex tumor foci (n = 64) were also in substantial agreement (weighted κ score: 0.74 [95% CI: 0.57-0.91]; r = 0.83 [95% CI: 0.73-0.89], p < 0.0001).

CONCLUSIONS

Our findings suggest that the P-score based on preoperative CNB accurately reflects the pathology of the whole tumor, highlighting its value as a decision support tool for newly diagnosed PCa patients.

Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images

In this work, we introduced an automated diagnostic system for Gleason system grading and grade groups (GG) classification using whole slide images (WSIs) of digitized prostate biopsy specimens (PBSs). Our system first classifies the Gleason pattern (GP) from PBSs and then identifies the Gleason score (GS) and GG. We developed a comprehensive DL-based approach to develop a grading pipeline system for the digitized PBSs and consider GP as a classification problem (not segmentation) compared to current research studies (deals with as a segmentation problem). A multilevel binary classification was implemented to enhance the segmentation accuracy for GP. Also, we created three levels of analysis (pyramidal levels) to extract different types of features. Each level has four shallow binary CNN to classify five GP labels. A majority fusion is applied for each pixel that has a total of 39 labeled images to create the final output for GP. The proposed framework is trained, validated, and tested on 3080 WSIs of PBS. The overall diagnostic accuracy for each CNN is evaluated using several metrics: precision (PR), recall (RE), and accuracy, which are documented by the confusion matrices.The results proved our system's potential for classifying all five GP and, thus, GG. The overall accuracy for the GG is evaluated using two metrics, PR and RE. The grade GG results are between 50% to 92% for RE and 50% to 92% for PR. Also, a comparison between our CNN architecture and the standard CNN (ResNet50) highlights our system's advantage. Finally, our deep-learning system achieved an agreement with the consensus grade groups.

Magnetic resonance imaging target fusion biopsy vs. transrectal ultrasound-guided biopsy - A comparative study of ISUP score upgrading risk in the final radical prostatectomy specimen

Objectives: The aim of this study was to com-pare the risk of International Society of Urological Pathology (ISUP) score upgrading between magnetic resonance imaging targeted fusion biopsy (MRI-TB) and tran-srectal ultrasound-guided biopsy (TRUS-B) in the final radical prostatectomy (RP) specimen pathological report.Materials and methods: This retrospective single center study included 51 patients with prostate cancer (PCa) diagnosed with MRI-TB and 83 patients diagnosed with TRUS-B between October/2019 and July/2021. We compared the rates of ISUP score upgrading between both groups after robotic-assisted radi-cal prostatectomy (RARP) and the specific transition of each ISUP score based on biopsy modality. The rate of ISUP score concordance and downgrading were also assessed. To define the intra and interobserver concordance for each ISUP score in biopsy and RP specimen for each biopsy modality, the Cohen’s Kappa coefficient was calculated. ISUP scores and biopsy modal-ity were selected for multivariate analysis and a logistic regres-sion model was built to provide independent risk factors of ISUP score upgrading.Results: The difference of the rate of upgrading between MRI-TB group and TRUS-B group was statistically significant (p = 0.007) with 42.2% of patients of TRUS-B group experiencing an upgrade in their ISUP score while only 19.6% in MRI-TB group. Concordance and downgrading rates did not statistically differ between the two groups. Strength of concordance using Cohen’s Kappa coefficient was fair in both groups but higher in MRI-TB group (TRUS-B group k = 0.230; p < 0.001; concordance: 47%vs. MRI/TB group k = 0.438; p < 0.001; concordance: 62.7%). Biopsy modality and ISUP 1 on biopsy were independent predic-tors of ISUP upgrading after RP.Conclusions: MRI-TB is highly accurate with lower risk of PCa upgrading after RP than TRUS-B. Patients with ISUP 1 on biopsy have greater susceptibility to upgrading their ISUP score.

Targeted Multiparametric Magnetic Resonance Imaging/Ultrasound Fusion Biopsy for Quantitative Gleason 4 Grading Prediction in Radical Prostatectomy Specimens: Implications for Active Surveillance Candidate Selection

BACKGROUND

Quantitative Gleason grading appears to be a reliable prognostic parameter and provides broader risk stratification then the traditional Gleason grading in patients with prostate cancer (PCa) treated with radical prostatectomy (RP).

OBJECTIVE

To determine if quantification of Gleason pattern (GP) 4 for targeted and systematic biopsy (TBx + SBx) cores together with further clinical variables can identify the lowest quantitative GP 4 fraction on RP.

DESIGN, SETTING, AND PARTICIPANTS

A total of 548 patients underwent TBx + SBx of the prostate and then RP, with pathology revealing Gleason score 3 + 4, 4 + 3, or 4 + 4 disease.

INTERVENTION

TBx + SBx of the prostate followed by RP.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

GP 4 fraction thresholds of ≤5%, ≤10%, ≤15%, ≤20%, and ≤25% were compared between the TBx + SBx and RP specimens. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy for predicting the GP 4 fraction in the RP specimen were determined. Logistic regression models were used to establish a probabilistic relationship between various combinations of clinical and biopsy variables and the GP 4 fraction in the RP specimen.

RESULTS AND LIMITATIONS

GP 4 fractions of ≤5%, ≤10%, ≤15%, ≤20%, and ≤25% was observed in 33%, 49%, 58%, 65%, and 70% of patients on TBx, and 18%, 41%, 53%, 63%, and 70% of patients on RP, respectively. The sensitivity, specificity, NPV, PPV, and accuracy were 75%, 67%, 91%, 39%, and 74% for a TBx GP 4 fraction of ≤5%, and 65%, 85%, 65%, 85%, and 79% for a TBx GP 4 fraction of ≤25%, respectively. A model combining quantified TBx + SBx GP 4 with clinical parameters demonstrated the highest diagnostic accuracy. Limitations include the retrospective study design.

CONCLUSIONS

Our results demonstrate that the combination of MRI-TBx + SBx and GP 4 quantification allowed precise detection of a low fraction of GP 4 when using RP specimens as the reference standard. Moreover, we found that clinical variables including Prostate Imaging-Reporting and Data System score without biopsy are limited in detection of low GP 4 fractions.

PATIENT SUMMARY

Combination of targeted biopsy alone as well as combined with systematic biopsy and quantitative Gleason grading of biopsy specimen showed high agreement with pathology findings after surgical removal of the prostate. This could help in identifying patients who are suitable for active surveillance.

Concordance between Gleason score of prostate biopsies and radical prostatectomy specimens and its predictive factors

Objective:

The Gleason score is an essential factor for making decisions about prostate cancer management and its prognosis. Thus, we conducted this research to discover the histologic-grading accuracy of needle biopsy specimens, and to identify preoperative clinical and pathological factors that predict upgrading and downgrading from biopsy to radical prostatectomy specimen.

Patients and methods:

This study was performed on 570 patients who were referred to the medical centers affiliated with Shiraz University of Medical Sciences and underwent radical prostatectomy from 2013 to 2017. Concordance was evaluated between the Gleason score of needle biopsy and radical prostatectomy specimens. Predictors of upgrades and downgrades were assessed in univariate and multivariate logistic regression analyses.

Results:

Scores were the same in 50% of cases, downgraded in 26%, and upgraded in 24%. The variables predicting a Gleason score upgrade were higher Prostate specific antigen level, larger tumors, and older age. Lower tumor volume, lower Prostate specific antigen, and low maximum percentage of cancer in cores were predictors of downgrading from Gleason score>6 to ⩽6. Also, Body mass index>30, smaller tumor size, and negative lymph nodes were predictors of downgrading from Gleason score>7 to 7.

Conclusion:

The correlation between biopsy and Radical prostatectomy Gleason scores was only 50%. After dividing them into the new grading groups, this coordination increased by only 5.6%. Physicians need to consider possible limitations of the Gleason score of biopsy and factors that can be predictive of upgrading to high-risk prostate cancer before making treatment decisions.

An enhanced Genetic Folding algorithm for prostate and breast cancer detection

Cancer's genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model's area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.

Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods

One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of the pathologist and tends to suffer from the lack of reproducibility of biopsy outcomes across pathologists. As a result, computational approaches are being sought and machine learning has been gaining momentum in the prediction of the Gleason grade group. To date, machine learning literature has addressed this problem by using features from magnetic resonance imaging images, whole slide images, tissue microarrays, gene expression data, and clinical features. However, there is a gap with regards to predicting the Gleason grade group using DNA sequences as the only input source to the machine learning models. In this work, using whole genome sequence data from South African prostate cancer patients, an application of machine learning and biological experiments were combined to understand the challenges that are associated with the prediction of the Gleason grade group. A series of machine learning binary classifiers (XGBoost, LSTM, GRU, LR, RF) were created only relying on DNA sequences input features. All the models were not able to adequately discriminate between the DNA sequences of the studied Gleason grade groups (Gleason grade group 1 and 5). However, the models were further evaluated in the prediction of tumor DNA sequences from matched-normal DNA sequences, given DNA sequences as the only input source. In this new problem, the models performed acceptably better than before with the XGBoost model achieving the highest accuracy of 74 ± 01, F1 score of 79 ± 01, recall of 99 ± 0.0, and precision of 66 ± 0.1.

Correlation Between Quantitative PSMA PET Parameters and Clinical Risk Factors in Non-Metastatic Primary Prostate Cancer Patients

Background

PSMA PET is frequently used for staging of prostate cancer patients. Furthermore, there is increasing interest to use PET information for personalized local treatment approaches in surgery and radiotherapy, especially for focal treatment strategies. However, it is not well established which quantitative imaging parameters show highest correlation with clinical and histological tumor aggressiveness.

Methods

This is a retrospective analysis of 135 consecutive patients with non-metastatic prostate cancer and PSMA PET before any treatment. Clinical risk parameters (PSA values, Gleason score and D'Amico risk group) were correlated with quantitative PET parameters maximum standardized uptake value (SUVmax), mean SUV (SUVmean), tumor asphericity (ASP) and PSMA tumor volume (PSMA-TV).

Results

Most of the investigated imaging parameters were highly correlated with each other (correlation coefficients between 0.20 and 0.95). A low to moderate, however significant, correlation of imaging parameters with PSA values (0.19 to 0.45) and with Gleason scores (0.17 to 0.31) was observed for all parameters except ASP which did not show a significant correlation with Gleason score. Receiver operating characteristics for the detection of D'Amico high-risk patients showed poor to fair sensitivity and specificity for all investigated quantitative PSMA PET parameters (Areas under the curve (AUC) between 0.63 and 0.73). Comparison of AUC between quantitative PET parameters by DeLong test showed significant superiority of SUVmax compared to SUVmean for the detection of high-risk patients. None of the investigated imaging parameters significantly outperformed SUVmax.

Conclusion

Our data confirm prior publications with lower number of patients that reported moderate correlations of PSMA PET parameters with clinical risk factors. With the important limitation that Gleason scores were only biopsy-derived in this study, there is no indication that the investigated additional parameters deliver superior information compared to SUVmax.

Radiomics Analysis on [68Ga]Ga-PSMA-11 PET and MRI-ADC for the Prediction of Prostate Cancer ISUP Grades: Preliminary Results of the BIOPSTAGE Trial

Simple Summary

Radiomics analysis is used on magnetic resonance imaging – apparent diffusion coefficient (MRI-ADC) maps and [68Ga]Ga-PSMA-11 PET uptake maps to assess unique tumor traits not visible to the naked eye and predict histology-proven ISUP grades in a cohort of 28 patients. Our study’s main goal is to report imaging features that can distinguish patients with low ISUP grades from those with higher grades (ISUP one+) by employing logistic regression statistical models based on MRI-ADC and 68Ga-PSMA data, as well as assess the features’ stability under small contouring variations. Our findings reveal that MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET imaging features-based models are equivalent and complementary for predicting low ISUP grade patients. These models can be employed in broader studies to confirm their ISUP grade prediction ability and eventually impact clinical workflow by reducing overdiagnosis of indolent, early-stage PCa.

Abstract

Prostate cancer (PCa) risk categorization based on clinical/PSA testing results in a substantial number of men being overdiagnosed with indolent, early-stage PCa. Clinically non-significant PCa is characterized as the presence of ISUP grade one, where PCa is found in no more than two prostate biopsy cores.MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET have been proposed as tools to predict ISUP grade one patients and consequently reduce overdiagnosis. In this study, Radiomics analysis is applied to MRI-ADC and [⁶⁸Ga]Ga-PSMA-11 PET maps to quantify tumor characteristics and predict histology-proven ISUP grades. ICC was applied with a threshold of 0.6 to assess the features’ stability with variations in contouring. Logistic regression predictive models based on imaging features were trained on 31 lesions to differentiate ISUP grade one patients from ISUP two+ patients. The best model based on [⁶⁸Ga]Ga-PSMA-11 PET returned a prediction efficiency of 95% in the training phase and 100% in the test phase whereas the best model based on MRI-ADC had an efficiency of 100% in both phases. Employing both imaging modalities, prediction efficiency was 100% in the training phase and 93% in the test phase. Although our patient cohort was small, it was possible to assess that both imaging modalities add information to the prediction models and show promising results for further investigations.

Diagnostic significance of reassessment of prostate biopsy specimens by experienced urological pathologists at a high-volume institution

In prostate cancer, accurate diagnosis and grade group (GG) decision based on biopsy findings are essential for determining treatment strategies. Diagnosis by experienced urological pathologists is recommended; however, their contribution to patient benefits remains unknown. Therefore, we analyzed clinicopathological information to determine the significance of reassessment by experienced urological pathologists at a high-volume institution to identify factors involved in the agreement or disagreement of biopsy and surgical GGs. In total, 1325 prostate adenocarcinomas were analyzed, and the GG was changed in 452/1325 (34.1%) cases (359 cases were upgraded, and 93 cases were downgraded). We compared the highest GG based on biopsy specimens, with the final GG based on surgical specimens of 210 cases. The agreement rate between the surgical GG performed and assessed in our institute and the highest biopsy GG assessed by an outside pathologist was 34.8% (73/210); the agreement rate increased significantly to 50% (105/210) when biopsy specimens were reevaluated in our institute (chi-square test, P < 0.01). Multivariate logistic regression analysis showed that only the length of the lesion in the positive core with the highest GG in the biopsy was a significant factor for determining the agreement between biopsy GG and surgical GG, with an odds ratio of 1.136 (95% confidence interval: 1.057-1.221; P < 0.01). Thus, reassessment by experienced urological pathologists at high-volume institutions improved the agreement rate. However, it should be noted there is a high probability of discordance between a small number of lesions or short lesions and surgical GG.

Radiomics in prostate cancer: an up-to-date review

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting

Summary

Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes (WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31) for IBC and eight genes (NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM) for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base.

Availability and implementation

www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git.

Supplementary information

Supplementary data are available at https://github.com/skr1/Imagene.git.

Prostate Cancer Gleason Score From Biopsy to Radical Surgery: Can Ultrasound Shear Wave Elastography and Multiparametric Magnetic Resonance Imaging Narrow the Gap?

Objectives

To investigate the impact of ultrasound shear wave elastography (USWE) and multiparametric magnetic resonance imaging (mpMRI) in predicting a change in biopsy-assigned Gleason Score (GS) after radical surgery for localised prostate cancer (PCa).

Method

A total of 212 men opting for laparoscopic radical prostatectomy (LRP) between September 2013 and June 2017 were recruited into this study. All the participants had 12-core transrectal ultrasound (TRUS) biopsies and imaging using USWE and mpMRI before radical surgery. The predictive accuracy for imaging modalities was assessed in relation to upgrading and downgrading of PCa GS between the biopsies and radical prostatectomy using Student's t-test and multivariable logistic regression analyses. A decision analysis curve was constructed assessing the impact of nomogram on clinical situations using different thresholds of upgrading probabilities.

Results

Most GS 6 diseases on biopsies were upgraded on radical surgery (37/42, 88.1%). Major downgrading was seen in GS 8 category of disease (14/35; 37.1%), whereas no alteration was observed in GS 7 on biopsies in most men (55/75; 73.3%). In univariate analysis, higher preoperative prostate-specific antigen (PSA) (p = 0.001), higher prostate-specific antigen density (PSAD) (p = 0.002), stiffer USWE lesions (p = 0.009), and higher prostate imaging-reporting and data system (PIRADS) (p = 0.002) on mpMRI were significant predictors of upgrading. In multivariate logistic regression analyses, only PSA (p = 0.016) and USWE-measured tissue stiffness (p = 0.029) showed statistical significance in predicting upgrading.

Conclusions

Measurement of tissue stiffness using USWE in clinically localised PCa can predict upgrading of GS and has the potential to improve patient management options.

A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens

Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG. The GP classification pipeline is based on a pyramidal deep learning system that utilizes three convolution neural networks (CNN) to produce both patch- and pixel-wise classifications. The analysis starts with sequential preprocessing steps that include a histogram equalization step to adjust intensity values, followed by a PBSs' edge enhancement. The digitized PBSs are then divided into overlapping patches with the three sizes: 100 × 100 (CNNS), 150 × 150 (CNNM), and 200 × 200 (CNNL), pixels, and 75% overlap. Those three sizes of patches represent the three pyramidal levels. This pyramidal technique allows us to extract rich information, such as that the larger patches give more global information, while the small patches provide local details. After that, the patch-wise technique assigns each overlapped patch a label as GP categories (1 to 5). Then, the majority voting is the core approach for getting the pixel-wise classification that is used to get a single label for each overlapped pixel. The results after applying those techniques are three images of the same size as the original, and each pixel has a single label. We utilized the majority voting technique again on those three images to obtain only one. The proposed framework is trained, validated, and tested on 608 whole slide images (WSIs) of the digitized PBSs. The overall diagnostic accuracy is evaluated using several metrics: precision, recall, F1-score, accuracy, macro-averaged, and weighted-averaged. The (CNNL) has the best accuracy results for patch classification among the three CNNs, and its classification accuracy is 0.76. The macro-averaged and weighted-average metrics are found to be around 0.70-0.77. For GG, our CAD results are about 80% for precision, and between 60% to 80% for recall and F1-score, respectively. Also, it is around 94% for accuracy and NPV. To highlight our CAD systems' results, we used the standard ResNet50 and VGG-16 to compare our CNN's patch-wise classification results. As well, we compared the GG's results with that of the previous work.

Three-dimensional nuclear magnetic resonance spectroscopy: a complementary tool to multiparametric magnetic resonance imaging in the identification of aggressive prostate cancer at 3.0T

Background

The limitations of the assessment of tumor aggressiveness by Prostate Imaging Reporting and Data System (PI-RADS) and biopsies suggest that the diagnostic algorithm could be improved by quantitative measurements in some chosen indications. We assessed the tumor high-risk predictive performance of 3.0 Tesla (3.0T) multiparametric magnetic resonance imaging (mp-MRI) combined with nuclear magnetic resonance spectroscopic sequences (NMR-S) in order to show that the metabolic analysis could bring out an evocative result for the aggressive form of prostate cancer.

Methods

We conducted a retrospective study of 26 patients (mean age, 62.4 years) who had surgery for prostate cancer between 2009 and 2016 after pre-therapeutic assessment with 3.0T mp-MRI and NMR-S. Groups within the intermediate range of the D'Amico risk classification were divided into two categories, low risk (n=20) and high risk (n=6), according to the International Society of Urological Pathology (ISUP) 2-3 limit. Histoprognostic discordances within various risk groups were compared with the corresponding predictive MRI values. The performance of predictive models was assessed based on sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves.

Results

After prostatectomy, histological analysis reclassified 18 patients as high-risk, including 16 who were T3 MRI grade, of whom 13 (81.3%) were found to be pT3. Among the patients who had cT1 or cT2 digital rectal examinations, the T3 MRI factor multiplied by 8.7 [odds ratio (OR), 8.7; 95% confidence interval (CI), 1.3-56.2; P=0.024] the relative risk of being pT3 and by 5.8 (OR, 5.8; 95% CI, 0.95-35.7; P=0.05) the relative risk of being pGleason (pGS) > GS-prostate biopsy. Spectroscopic data showed that the choline concentration was significantly higher (P=0.001) in aggressive disease.

Conclusions

The predictive model of tumor aggressiveness combining mp-MRI plus NMR-S was better than the mp-MRI model alone (AUC, 0.95 vs. 0.86). Information obtained by mp-MRI coupled with spectroscopy may improve the detection of occult aggressive disease, helping in the discrimination of intermediate risks.

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

Background

Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication.

Methods

In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17).

Results

Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively.

Conclusions

Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

Gleason grading is the process by which pathologists assess the morphology of prostate tumors. The assigned Grade Group tells us about the likely clinical course of people with prostate cancer and helps doctors to make decisions on treatment. The process is complex and subjective, with frequent disagreement amongst pathologists. In this study, we develop and evaluate an approach to Gleason grading based on artificial intelligence, rather than pathologists’ assessment, to predict risk of dying of prostate cancer. Looking back at tumors and data from 2,807 people diagnosed with prostate cancer, we find that our approach is better at predicting outcomes compared to grading by pathologists alone. These findings suggest that artificial intelligence might help doctors to accurately determine the probable clinical course of people with prostate cancer, which, in turn, will guide treatment.

Wulczyn et al. utilise a deep learning-based Gleason grading model to predict prostate cancer-specific mortality in a retrospective cohort of radical prostatectomy patients. Their model enables improved risk stratification compared to pathologists’ grading and demonstrates the potential for computational pathology in the management of prostate cancer.

Prognostic Impact of Different Gleason Patterns on Biopsy Within Grade Group 4 Prostate Cancer

Background

Grade group (GG) 4 prostate cancer (PC) is considered a single entity; however, there are questions regarding prognostic heterogeneity. This study assessed the prognostic differences among various Gleason scores (GSs) classified as GG 4 PC on biopsy before radical prostatectomy (RP).

Methods

We conducted a multicenter retrospective study, and a total of 1791 patients (GS 3 + 5: 190; GS 4 + 4: 1557; and GS 5 + 3: 44) with biopsy GG 4 were included for analysis. Biochemical recurrence (BCR)-free survival, cancer-specific survival, and overall survival were analyzed using the Kaplan–Meier method and the log-rank test. Logistic regression analysis was performed to identify factors associated with high-risk surgical pathologic features. Cox regression models were used to analyze time-dependent oncologic endpoints.

Results

Over a median follow-up of 75 months, 750 patients (41.9%) experienced BCR, 146 (8.2%) died of any causes, and 57 (3.2%) died of PC. Biopsy GS 5 + 3 was associated with significantly higher rates of GS upgrading in RP specimens than GS 3 + 5 and GS 4 + 4. On multivariable analysis adjusted for clinicopathologic features, different GSs within GG 4 were significantly associated with BCR (p = 0.03) but not PC-specific or all-cause mortality. Study limitations include the lack of central pathological specimen evaluation.

Conclusions

Patients with GG 4 at biopsy exhibited some limited biological and clinical heterogeneity. Specifically, GS 5 + 3 had an increased risk of GS upgrading. This can help individualize patients’ counseling and encourage further study to refine biopsy specimen-based GG classification.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-021-10257-x.

PI-RADS v2.1: What has changed and how to report

Multiparametric magnetic resonance imaging (MRI) of the prostate has become a vital imaging tool in daily radiological practice for the stratification of the risk of prostate cancer. There has been a recent update to the Prostate Imaging-Reporting and Data System (PI-RADS). The updated changes in PI-RADS, which is version 2.1, have been described with information pertaining to the recommended imaging protocols, the techniques on how to perform prostate MRI and a simplified approach to interpreting and reporting MRI of the prostate. Explanatory tables, schematic diagrams and key representative images have been used to provide the reader with a useful approach to interpreting and then stratifying lesions in the four anatomical zones of the prostate gland. The intention of this article is to address challenges of interpretation and reporting of prostate lesions in daily practice.

Correlation of the Grade Group of Prostate Cancer according to the International Society of Urological Pathology (Isup) 2014 Classification between Prostate Biopsy and Radical Prostatectomy Specimens

ABTRACTWe aimed to assess the correlation between ISUP 2014 grades of needle biopsy (NB) and radical prostatectomy (RP) specimens and the parameters effecting this correlation. A total of 353 patients, who underwent a radical prostatectomy with diagnose of prostate cancer, were included in the study. Especially, the maximum percentage of core involved by cancer (MPCI) of upgraded group was significantly higher than those of correlated group and downgraded group. MPCI might be used as a preoperative value to determine risk classification and to help counsel patients with regard to treatment decision and prognosis of disease.

Detection of clinically significant prostate cancer with 18F-DCFPyL PET/multiparametric MR

PURPOSE

To assess whether ¹⁸F-DCFPyL PET/multiparametric (mp)MR contributes to the diagnosis of clinically significant (cs) prostate cancer (PCa) compared to mpMR in patients with suspicion of PCa, or patients being considered for focal ablative therapies (FT).

PATIENTS AND METHODS

This ethics review board-approved, prospective study included 55 men with suspicion of PCa and negative systematic biopsies or clinically discordant low-risk PCa (n = 21) or those being considered for FT (n = 34) who received ¹⁸F-DCFPyL PET/mpMR. Each modality, PET, mpMR, and PET/MR (using the PROMISE classification), was assessed independently. All suspicious lesions underwent PET/MR-ultrasound fusion biopsies.

RESULTS

There were 45/55 patients (81.8%) that had histologically proven PCa and 41/55 (74.5%) were diagnosed with csPCa. Overall, 61/114 lesions (53.5%) identified on any modality were malignant; 49/61 lesions (80.3%) were csPCa. On lesion-level analysis, for detection of csPCa, the sensitivity of PET was higher than that of mpMR and PET/MR (86% vs 67% and 69% [p = 0.027 and 0.041, respectively]), but at a lower specificity (32% vs 85% and 86%, respectively [p < 0.001]). The performance of MR and PET/MR was comparable. For identification of csPCa in PI-RADS ≥ 3 lesions, the AUC (95% CI) for PET, mpMR, and PET/MR was 0.75 (0.65-0.86), 0.69 (0.56-0.82), and 0.78 (0.67-0.89), respectively. The AUC for PET/MR was significantly larger than that of mpMR (p = 0.04).

CONCLUSION

PSMA PET detects more csPCa than mpMR, but at low specificity. The performance PET/MR is better than mpMR for detection of csPCa in PI-RADS ≥ 3 lesions.

CLINICAL REGISTRATION

NCT03149861.

Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

Question

How does a deep learning system for assessing prostate biopsy specimens compare with interpretations determined by specialists in urologic pathology and by general pathologists?

Findings

In a validation data set of 752 biopsy specimens obtained from 2 independent medical laboratories and a tertiary teaching hospital, this study found that rate of agreement with subspecialists was significantly higher for the deep learning system than it was for a cohort of general pathologists.

Meaning

The deep learning system warrants evaluation as an assistive tool for improving prostate cancer diagnosis and treatment decisions, especially where subspecialist expertise is unavailable.

Importance

For prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role in determining case management. However, Gleason grading is associated with substantial interobserver variability, resulting in a need for decision support tools to improve the reproducibility of Gleason grading in routine clinical practice.

Objective

To evaluate the ability of a deep learning system (DLS) to grade diagnostic prostate biopsy specimens.

Design, Setting, and Participants

The DLS was evaluated using 752 deidentified digitized images of formalin-fixed paraffin-embedded prostate needle core biopsy specimens obtained from 3 institutions in the United States, including 1 institution not used for DLS development. To obtain the Gleason grade group (GG), each specimen was first reviewed by 2 expert urologic subspecialists from a multi-institutional panel of 6 individuals (years of experience: mean, 25 years; range, 18-34 years). A third subspecialist reviewed discordant cases to arrive at a majority opinion. To reduce diagnostic uncertainty, all subspecialists had access to an immunohistochemical-stained section and 3 histologic sections for every biopsied specimen. Their review was conducted from December 2018 to June 2019.

Main Outcomes and Measures

The frequency of the exact agreement of the DLS with the majority opinion of the subspecialists in categorizing each tumor-containing specimen as 1 of 5 categories: nontumor, GG1, GG2, GG3, or GG4-5. For comparison, the rate of agreement of 19 general pathologists’ opinions with the subspecialists’ majority opinions was also evaluated.

Results

For grading tumor-containing biopsy specimens in the validation set (n = 498), the rate of agreement with subspecialists was significantly higher for the DLS (71.7%; 95% CI, 67.9%-75.3%) than for general pathologists (58.0%; 95% CI, 54.5%-61.4%) (P < .001). In subanalyses of biopsy specimens from an external validation set (n = 322), the Gleason grading performance of the DLS remained similar. For distinguishing nontumor from tumor-containing biopsy specimens (n = 752), the rate of agreement with subspecialists was 94.3% (95% CI, 92.4%-95.9%) for the DLS and similar at 94.7% (95% CI, 92.8%-96.3%) for general pathologists (P = .58).

Conclusions and Relevance

In this study, the DLS showed higher proficiency than general pathologists at Gleason grading prostate needle core biopsy specimens and generalized to an independent institution. Future research is necessary to evaluate the potential utility of using the DLS as a decision support tool in clinical workflows and to improve the quality of prostate cancer grading for therapy decisions.

Correlation between Prostate Imaging Reporting and Data System Version 2, Prostate-Specific Antigen Levels, and Local Staging in Biopsy-Proven Carcinoma Prostate: A Retrospective Study

Background:

Multi-parametric magnetic resonance imaging (mp-MRI) is a promising tool in the diagnosis of clinically significant prostate cancer. Morphologic assessment using T2-weighted (T2W) images and functional assessment with diffusion-weighted imaging is the cornerstone for the diagnosis of prostate cancer on mp-MRI.

Aim/Objectives:

The aim of this study is to evaluate the role of mp-MRI based prostate imaging reporting and data system version 2 (PI-RADS v2) for the assessment of prostate cancer and its correlation with serum prostate specific antigen (S.PSA) levels, local (T) staging on MRI and histopathology.

Materials and Methods:

The study was carried out from June 2019 to February 2020. All patients with raised S.PSA levels and abnormal digital rectal examination who underwent mp-MRI of the prostate were included. MRI findings were characterized on the basis of PI-RADS v2 grading. All the patients underwent biopsy and histopathology. The score was correlated with S.PSA levels and the local stage of disease on MRI. Statistical analysis was performed, and results interpreted.

Results:

Carcinoma prostate was reported in 32/33 cases on biopsy. A significant correlation was observed between PI-RADS v2 score and S.PSA Levels and between PI-RADS v2 score and T stage of disease in our study. MRI was highly sensitive (93.75%) and specific (100%) in the diagnosis of prostate cancer in our study.

Conclusions:

Significant correlation between lesion score on PI-RADS v2 with the local stage and S.PSA levels was seen, thus signifying the importance of mp-MRI in detecting clinically significant prostate cancer. Diffusion-weighted and T2W sequences were the primary diagnostic sequence for the prostate cancer with no additional role of dynamic contrast enhanced sequences.

The expression of YWHAZ and NDRG1 predicts aggressive outcome in human prostate cancer

Some prostate cancers (PCas) are histo-pathologically grouped within the same Gleason Grade (GG), but can differ significantly in outcome. Herein, we aimed at identifying molecular biomarkers that could improve risk prediction in PCa. LC ESI-MS/MS was performed on human PCa and benign prostatic hyperplasia (BPH) tissues and peptide data was integrated with omic analyses. We identified high YWHAZ and NDRG1 expression to be associated with poor PCa prognosis considering all Gleason scores (GS). YWHAZ and NDRG1 defined two subpopulations of PCa patients with high and intermediate risk of death. Multivariable analyses confirmed their independence from GS. ROC analysis unveiled that YWHAZ outperformed GS beyond 60 months post-diagnosis. The genomic analysis of PCa patients with YWHAZ amplification, or increased mRNA or protein levels, revealed significant alterations in key DNA repair genes. We hereby state the relevance of YWHAZ in PCa, showcasing its role as an independent strong predictor of aggressiveness.

Predicting Gleason sum upgrading from biopsy to radical prostatectomy pathology: a new nomogram and its internal validation

Background

To explore the rate of Gleason sum upgrading (GSU) from biopsy to radical prostatectomy pathology and to develop a nomogram for predicting the probability of GSU in a Chinese cohort.

Methods

We retrospectively reviewed our prospectively maintained prostate cancer (PCa) database from October 2012 to April 2020. 198 patients who met the criteria were enrolled. Multivariable logistic regression analysis was performed to determine the predictors. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Calibration curve was used to assess the concordance between predictive probabilities and true risks.

Results

The rate of GSU was 41.4%, whilst GS concordance rate was 44.4%. The independent predictors are prostate specific antigen (PSA), greatest percentage of cancer (GPC), clinical T-stage and Prostate Imaging Reporting and Data System (PI-RADS) score. Our model showed good discrimination (AUC of 0.735). Our model was validated internally with good calibration with bias-corrected C-index of 0.726.

Conclusions

Utilization of basic clinical variables (PSA and T-stage) combined with imaging variable (PI-RADS) and pathological variable (GPC) could improve performance in predicting actual probabilities of GSU in the 24-core biopsy scheme. Our nomogram could help to assess the true risk and make optimal treatment decisions for PCa patients.

Periprostatic fat thickness quantified by preoperative magnetic resonance imaging is an independent risk factor for upstaging from cT1/2 to pT3 in robot-assisted radical prostatectomy

OBJECTIVES

To analyze the correlation between periprostatic fat thickness on multiparametric magnetic resonance imaging and upstaging from cT1/2 to pT3 in robot-assisted radical prostatectomy.

METHODS

We retrospectively evaluated data from men with cT1/2 prostate cancer treated with robot-assisted radical prostatectomy at Nara Prefecture General Medical Center, Nara, Japan, between March 2013 and December 2017. We calculated the periprostatic fat thickness and subcutaneous thickness from preoperative multiparametric magnetic resonance imaging. We divided the cohort into two groups for analysis. Group 1 included patients upstaged from clinical to pathological stage, whereas group 2 included those without upstaging.

RESULTS

Data on 220 patients meeting the inclusion criteria were included in the analysis. A total of 36 patients were upstaged from clinical T1 or T2 to pathological T3, whereas 184 patients were not upstaged. The upstaging was associated with prostate volume, Gleason score, prostate-specific antigen density, periprostatic fat thickness, Prostate Imaging Reporting and Data System score based on univariate analysis. Multivariate analysis showed prostate volume (P = 0.03, odds ratio 0.958, 95% confidence interval 0.921-0.996), Gleason score (P = 0.022, odds ratio 2.676, 95% confidence interval 1.153-6.213) and periprostatic fat thickness (P = 0.004, odds ratio 1.26, 95% confidence interval 1.079-1.471) as independent risk factors of upstaging.

CONCLUSIONS

Prostate volume, Gleason score and periprostatic fat thickness on multiparametric magnetic resonance imaging are significantly associated with and independent risk factors for upstaging from cT1/2 to pT3 in patients undergoing robot-assisted radical prostatectomy.

Development of Mobile Application for Dynamically Monitoring the Risk of Prostate Cancer and Clinicopathology

OBJECTIVE

To develop an application dynamically monitoring the prostate cancer (PCa) risk for patients to assess their own progression of PCa risk at home.

METHODS

Between January 2010 and December 2019, all of the 1697 patients underwent transrectal ultrasound prostate biopsy at the cancer center, which is one of the Chinese Prostate Cancer Consortium. Patients' clinical parameters from January 2010 to May 2018 were used to establish models that consisted of several risk factors with P value <0.1 in univariate analysis and with P value <0.05 in multivariate analysis (n=1113), including model 1 (predicting PCa), model 2 (predicting PCa with high Gleason scores (7 or higher)) and model 3 (predicting PCa with the high clinical stage (T2b or higher)). Other patients from June 2018 to December 2019 were used to validate models (n=440). Patients with a lack of sufficient data were eventually excluded (n=144).

RESULTS

A total of 1553 patients were involved in this study, and an application was used to perform the models. The predictive cut-off value and area under the curves (AUCs) of model 1, 2 and 3 were, respectively, calculated (cut-off: 0.53, 0.38 and 0.40, AUCs: 0.88, 0.89 and 0.89). Using a cut-off value of 10%, three models obtained a high sensitivity (>95%). Besides, more patients can be correctly reclassified via our models (42.9 to 55.5%). Decision curve analyses revealed a decent net benefit in any probability for models. These results were well verified in the validation cohort.

CONCLUSION

This application showed decent performance in predicting the risk of PCa and clinicopathology, which was available and convenient for patients to self-assess the progress of PCa risks so that being better to participate in disease management.

The prevalence and locations of bone metastases using whole-body MRI in treatment-naïve intermediate- and high-risk prostate cancer

Objective

The aim of this study was to assess the prevalence and distribution of bone metastases in treatment-naïve prostate cancer patients eligible for a metastatic workup using whole-body MRI, and to evaluate the results in light of current guidelines.

Methods

This single-institution, retrospective study included all patients with treatment-naïve prostate cancer referred to whole-body MRI during 2016 and 2017. All were eligible for a metastatic workup according to the guidelines: PSA > 20 ng/ml and/or Gleason grade group ≥ 3 and/or cT ≥ 2c and/or bone symptoms. The definition of a metastasis was descriptive and based on the original MRI reports. The anatomical location of metastases was registered.

Results

We included 161 patients with newly diagnosed prostate cancer of which 36 (22%) were intermediate-risk and 125 (78%) were high-risk. The median age and PSA were 71 years (IQR 64–76) and 13 ng/ml (IQR 8–28), respectively. Bone metastases were found in 12 patients (7%, 95% CI: 4–13), and all were high-risk with Gleason grade group ≥ 4. The pelvis was affected in 4 patients, and the spine + pelvis in the remaining 8. No patients demonstrated metastases to the spine without concomitant metastases in the pelvis. Limitations are the small number of metastases and retrospective design.

Conclusion

This study suggests that the overall prevalence of bone metastases using the current guidelines for screening is quite low. No metastases were seen in the case of Gleason grade group ≤ 3, and further studies should investigate if it necessary to screen non-high-risk patients.

Key Points

• The overall prevalence of bone metastases was 7% in the case of newly diagnosed intermediate- and high-risk prostate cancer.

• The prevalence in high-risk patients was 10%, and no metastases were seen in patients with Gleason grade group ≤ 3.

• The pelvic skeleton is the main site, and no metastases occurred in the spine without concomitant pelvic metastases.

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

IMPORTANCE

Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored.

OBJECTIVE

To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists.

EXPOSURE

An AI-based assistive tool for Gleason grading of prostate biopsies.

MAIN OUTCOMES AND MEASURES

Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies.

RESULTS

Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement.

CONCLUSIONS AND RELEVANCE

In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.

Malignancy Detection in Prostate Multi-Parametric MR Images Using U-net with Attention

Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.

Concordance Between Biopsy and Radical Prostatectomy Gleason Scores: Evaluation of Determinants in a Large-Scale Study of Patients Undergoing RARP in Belgium

To determine whether Gleason scores were concordant between prostate biopsies (bGS) and the definitive resection specimen (pGS) excised with robot-assisted radical prostatectomy (RARP); to identify clinical and pathological factors that might predict upgrading; and to evaluate how upgrading affected outcome. Between 2009 and 2016, 25 Belgian centers participated in collecting prospective data for patients that underwent RARP. We analyzed the concordance rate between the bGS and the pGS in 8021 patients with kappa statistics, and we compared concordance rates from different centers. We assessed the effect of several clinical and pathological factors on the concordance rate with logistic regression analysis. The concordance rate for the entire population was 62.9%. Upgrading from bGS to pGS occurred in 27.3% of patients. The number of biopsies was significantly associated with concordance. Older age (>60 y), a higher clinical T stage (≥cT2), a higher PSA value at the time of biopsy (>10 ng/ml), and more time between the biopsy and the radical prostatectomy were significantly associated with a higher risk of upgrading. Positive margins and PSA relapse occurred more frequently in upgraded patients. Center size did not significantly affect the concordance rate (p = 0.40).This prospective, nationwide analysis demonstrated a Gleason score concordance rate of 62.9%. Upgrading was most frequently observed in the non-concordant group. We identified clinical and pathological factors associated with (non)-concordance. Upgrading was associated with a worse oncological outcome. Center volume was not associated with pathological accuracy.

Prostate MRI-TRUS fusion biopsy: a review of the state of the art procedure

Prostate cancer is the fourth most common cancer and population-based screening programmes are being increasingly adopted worldwide. Screening-positive patients undergo routine transrectal ultrasound (TRUS)-guided systematic biopsy, which is the current diagnostic standard for prostate cancer. However, systematic biopsies suffer from poor sensitivity, especially for the tumors of the anterior prostate and apex as well as in large volume glands. In the past decade, MRI-guided targeted biopsies have come up, which utilize the multiparametric capability of MRI to target lesions for sampling. MRI/TRUS fusion biopsies combine the advantages of MRI-targeting with that of real-time guidance made possible by TRUS. MRI-TRUS fusion biopsies are being increasingly used in men with high clinical suspicion of prostate cancer who have had prior negative systematic biopsies. A large number of fusion biopsy platforms are currently available commercially. Although the basic workflow is similar, there are differences in the operational software, biopsy routes offered, TRUS acquisition technique, type of correction applied at the time of fusion and in the probe tracking hardware. The article describes the current role and indications of MRI-TRUS fusion biopsy followed by a discussion on the workflow, patient preparation, biopsy procedure and complications.

Quantifying the effect of biopsy lateral decubitus patient positioning compared to supine prostate magnetic resonance image scanning on prostate translocation and distortion

INTRODUCTION

More than a quarter of tumors are missed by magnetic resonance imaging/ultrasound (MRI/US) fusion-guided biopsy, the majority due to software-based misregistration. Transrectal approaches to biopsy are typically performed in the lateral decubitus position; conversely, diagnostic MRI is performed with the patient lying supine. Any position-related difference in prostate location or gland deformation could potentially exacerbate misregistration at subsequent biopsy.

METHODS

Fifteen healthy male volunteers (mean age 35.9 years, range 27-53) were included in this prospective, institutional review board-approved study. Each volunteer had an MRI performed in the supine position, followed by the second in the lateral decubitus position (mimicking a typical biopsy position). MRI images were co-registered and analyzed in order to assess prostate translocation and distortion.

RESULTS

Whole prostate translocation of ≥5 mm was observed in 20% of patients and of ≥3 mm in 60% of patients. When dividing the prostate into prostatic sectors, the prostatic base demonstrated the largest positional difference. When plotting the translocation directions with relative volume difference, there was a moderate negative correlation trend in the latero-lateral direction. Only minimal distortion was observed, with similar distortion among all prostatic sectors.

CONCLUSIONS

Positional change affects the prostate translocation, however, the effect on prostate distortion appears to be negligible. Prostate translocation in latero-lateral direction can be minimized with larger bladder volumes. Prostate translocation needs to be considered alongside software misregistration error; however, positional change should not affect software registration of MRI/US fusion-guided prostate biopsy.

Volume of Gleason pattern 4 stratifies risk of metastasis and death in patients with Gleason score 3+5=8/5+3=8 positive prostate core biopsies

Implementation of Grade Groups (GrGrs) has been widely accepted for reporting prostate cancer grade since the 2014 International Society of Urological Pathology consensus meeting. Despite their undisputed value for risk stratification, some GrGr are, a priori, quite heterogeneous in that they contain multiple Gleason patterns (GPs). In this regard, the prognostic significance of GP5 in biopsies with highest GrGr4 is uncertain and evaluated in this study. A search of all core biopsies positive for prostate cancer reviewed after 2005 was performed, and 71 cases with highest GrGr4 containing GP5 (i.e., 3 + 5 = 8 or 5 + 3 = 8; referred to as GrGr4/GP5pos) eligible for inclusion were identified. In addition, 95 core biopsy cases with highest GrGr4 and no GP5 (i.e, 4 + 4 = 8; referred to as GrGr4/GP5neg) were selected for comparison. Multiple pathologic parameters, including volume and amount of GP4, and clinical variables were collected to evaluate the influence of GP5 on disease recurrence, development of metastases, and disease-specific death. GrGr4/GP5pos cases did not show, as a group, statistically significant differences in prostatectomy findings, disease recurrence, metastases, and disease-specific mortality when compared with GrGr4/GP5neg cases. In addition, the risk of all outcomes evaluated in the study did not differ between the whole GrGr4/GP5pos and GrGr4/GP5neg groups. However, Kaplan-Meier analysis found that GrGr4/GP5pos cases with a significant amount of GP4 did show a higher risk of prostate cancer-specific death as well as bone and visceral metastases. Univariate Cox regression demonstrated that preoperative prostate specific antigen (PSA), total number of positive cores, and global GrGr5 were also associated with a higher chance of disease-specific death. In a multivariate model, only global GrGr5 and PSA >20 ng/dL remained statistically significant. This study suggests that the mere presence of GP5 in core biopsies with highest GrGr4 disease may not portend a worse prognosis. In these cases, accounting for the case-wide volume of GP4 by reporting a global GrGr appears to be more relevant as it identifies a subset of GrGr4/GP5pos patients with global GrGr5 who have a higher risk of metastases and prostate cancer-specific mortality.

Institutional Adoption and Apprenticeship of Fusion Targeted Prostate Biopsy: Does Experience Affect the Cancer Detection Rate?

INTRODUCTION

There are limited data on the learning curve of magnetic resonance imaging/transrectal ultrasound (MRI/TRUS)-fusion targeted prostate biopsies (tBx).

OBJECTIVE

The aim of this study was to investigate the difference in prostate cancer (PCa) detection rate between an experienced urologist and novice resident performing tBx.

METHODS

A total of 183 patients underwent tBx from 2012 to 2016 for a total of 518 tBx cores. Biopsies in this study were performed by an experienced urologist (investigator A) or a novice resident (investigator B). The outcome was the detection of PCa on tBx. Using a multivariable logistic regression model, we estimated odds ratios for the detection of PCa. Inverse probability treatment weighting (IPTW) was used to balance patients' baseline characteristics and compare detection rates of PCa. Before performance of tBx, all patients underwent MRI.

RESULTS

On multivariable logistic regression analysis, investigator experience was associated with a higher odds of detection of PCa (OR = 1.003; 95% confidence interval 1.002-1.006, p = 0.037). After IPTW adjustment, there was no significant difference between the detection rate of investigator A (23%) and investigator B (32%; p = 0.457).

CONCLUSIONS

Data revealed a positive association between investigator experience and the odds of PCa detection, although there was no difference in the detection rates of the investigators.