Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system

Utility of positive core number on MRI-ultrasound fusion targeted biopsy in combination with PI-RADS scores for predicting unexpected extracapsular extension of clinically localized prostate cancer

OBJECTIVES

To evaluate the utility of magnetic resonance imaging (MRI) and MRI-ultrasound fusion targeted biopsy (TB) for predicting unexpected extracapsular extension (ECE) in clinically localized prostate cancer (CLPC).

METHODS

This study enrolled 89 prostate cancer patients with one or more lesions showing a Prostate Imaging-Reporting and Data System (PI-RADS) score ≥3 but without morphological abnormality in the prostatic capsule on pre-biopsy MRI. All patients underwent TB and systematic biopsy followed by radical prostatectomy (RP). Each lesion was examined by 3-core TB, taking cores from each third of the lesion. The preoperative variables predictive of ECE were explored by referring to RP specimens in the lesion-based analysis.

RESULTS

Overall, 186 lesions, including 81 (43.5%), 73 (39.2%), and 32 (17.2%) with PI-RADS 3, 4, and 5, respectively, were analyzed. One hundred and twenty-two lesions (65.6%) were diagnosed as cancer on TB, and ECE was identified in 33 (17.7%) on the RP specimens. The positive TB core number was ≤2 in 129 lesions (69.4%) and three in 57 lesions (30.6%). On the multivariate analysis, PI-RADS ≥4 (p = 0.049, odds ratio [OR] = 2.39) and three positive cores on TB (p = 0.005, OR = 3.07) were independent predictors of ECE. Lesions with PI-RADS ≥4 and a positive TB core number of 3 had a significantly higher rate of ECE than those with PI-RADS 3 and a positive TB core number ≤2 (37.5% vs. 7.8%, p < 0.001).

CONCLUSIONS

Positive TB core number in combination with PI-RADS scores is helpful to predict unexpected ECE in CLPC.

Can a nomogram predict apical prostate cancer pathology upgrade from fusion biopsy to final pathology? A multicenter study

BACKGROUND

This study evaluates the efficacy of a nomogram for predicting the pathology upgrade of apical prostate cancer (PCa).

METHODS

A total of 754 eligible patients were diagnosed with apical PCa through combined systematic and magnetic resonance imaging (MRI)-targeted prostate biopsy followed by radical prostatectomy (RP) were retrospectively identified from two hospitals (training: 754, internal validation: 182, internal-external validation: 148). A nomogram for the identification of apical tumors in high-risk pathology upgrades through comparing the results of biopsy and RP was established incorporating statistically significant risk factors based on univariable and multivariable logistic regression. The nomogram's performance was assessed via the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA).

RESULTS

Univariable and multivariable analysis identified age, targeted biopsy, number of targeted cores, TNM stage, and the prostate imaging-reporting and data system score as significant predictors of apical tumor pathological progression. Our nomogram, based on these variables, demonstrated ROC curves for pathology upgrade with values of 0.883 (95% CI, 0.847-0.929), 0.865 (95% CI, 0.790-0.945), and 0.840 (95% CI, 0.742-0.904) for the training, internal validation and internal-external validation cohorts respectively. Calibration curves showed good consistency between the predicted and actual outcomes. The validation groups also showed great generalizability with the calibration curves. DCA results also demonstrated excellent performance for our nomogram with positive benefit across a threshold probability range of 0-0.9 for the training and internal validation group, and 0-0.6 for the internal-external validation group.

CONCLUSION

The nomogram, integrating clinical, radiological, and pathological data, effectively predicts the risk of pathology upgrade in apical PCa tumors. It holds significant potential to guide clinicians in optimizing the surgical management of these patients.

Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.

Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management

BACKGROUND

The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications.

METHODS

A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas.

RESULTS

A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively.

CONCLUSION

DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.

Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology

This study aimed to enhance the accuracy of Gleason grade group (GG) upgrade prediction in prostate cancer (PCa) patients who underwent MRI-guided in-bore biopsy (MRGB) and radical prostatectomy (RP) through a combined analysis of prebiopsy and MRGB clinical data. A retrospective analysis of 95 patients with prostate cancer diagnosed by MRGB was conducted where all patients had undergone RP. Among the patients, 64.2% had consistent GG results between in-bore biopsies and RP, whereas 28.4% had upgraded and 7.4% had downgraded results. GG1 biopsy results, lower biopsy core count, and fewer positive cores were correlated with upgrades in the entire patient group. In patients with GG > 1 , larger tumor sizes and fewer biopsy cores were associated with upgrades. By integrating MRGB data with prebiopsy clinical data, machine learning (ML) models achieved 85.6% accuracy in predicting upgrades, surpassing the 64.2% baseline from MRGB alone. ML analysis also highlighted the value of the minimum apparent diffusion coefficient ( ADC min ) for GG > 1 patients. Incorporation of MRGB results with tumor size, ADC min value, number of biopsy cores, positive core count, and Gleason grade can be useful to predict GG upgrade at final pathology and guide patient selection for active surveillance.

Prediction of Gleason score in prostate cancer patients based on radiomic features of transrectal ultrasound images

OBJECTIVES

The aim of this study was to develop a model for predicting the Gleason score of patients with prostate cancer based on ultrasound images.

METHODS

Transrectal ultrasound images of 838 prostate cancer patients from The Cancer Imaging Archive database were included in this cross-section study. Data were randomly divided into the training set and testing set (ratio 7:3). A total of 103 radiomic features were extracted from the ultrasound image. Lasso regression was used to select radiomic features. Random forest and broad learning system (BLS) methods were utilized to develop the model. The area under the curve (AUC) was calculated to evaluate the model performance.

RESULTS

After the screening, 10 radiomic features were selected. The AUC and accuracy of the radiomic feature variables random forest model in the testing set were 0.727 (95% CI, 0.694-0.760) and 0.646 (95% CI, 0.620-0.673), respectively. When PSA and radiomic feature variables were included in the random forest model, the AUC and accuracy of the model were 0.770 (95% CI, 0.740-0.800) and 0.713 (95% CI, 0.688-0.738), respectively. While the BLS method was utilized to construct the model, the AUC and accuracy of the model were 0.726 (95% CI, 0.693-0.759) and 0.698 (95% CI, 0.673-0.723), respectively. In predictions for different Gleason grades, the highest AUC of 0.847 (95% CI, 0.749-0.945) was found to predict Gleason grade 5 (Gleason score ≥9).

CONCLUSIONS

A model based on transrectal ultrasound image features showed a good ability to predict Gleason scores in prostate cancer patients.

ADVANCES IN KNOWLEDGE

This study used ultrasound-based radiomics to predict the Gleason score of patients with prostate cancer.

The role of PSMA PET/CT to predict upgrading in patients undergoing radical prostatectomy for ISUP grade group 1 prostate cancer

INTRODUCTION AND OBJECTIVES

To investigate the additive role of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) independent from multiparametric magnetic resonance imaging (mpMRI) and clinical-pathological parameters to predict pathological upgrading in patients with ISUP grade group (GG) 1 prostate cancer (PCa) at prostate biopsy.

MATERIALS AND METHODS

A total of 41 patients who underwent robotic radical prostatectomy (RP) for GG1 disease at prostate biopsy with preoperative PSMA PET/CT and mpMRI images available for central review were included in the study. Univariate and multivariate logistic regression analyses were performed to determine the independent predictors of pathological upgrading (GG ≥ 2).

RESULTS

Final RP pathology revealed upgrading in 26 patients (65.9%); to GG 2 disease in 25 cases and GG 4 disease in one case. International Society of Urological Pathology (ISUP) upgrading rates for prostate imaging-reporting and data system (PIRADS)-5, PIRADS-4, and PIRADS ≤ 3 lesions were 78%, 74%, and 38%, respectively. Fourteen out of 15 (93.3%) patients with an SUVmax ≥ 5.6 and all patients with an SUVmax ≥ 6.5 (n = 10) had pathological upgrading. The upgrading rate in patients with SUV < 5.6 was 46.2% (12/26). Intraprostatic SUVmax ≥ 5.6 was found as the only independent predictor of pathological upgrading in multivariate analysis.

CONCLUSION

High prostatic PSMA uptake was found to be a very reliable predictor of pathological upgrading, but low PSMA uptake cannot exclude pathological upgrading. Intraprostatic PSMA uptake along with previously known mpMRI and biopsy-related parameters should be considered when making a treatment decision in patients with GG1 PCa at prostate biopsy.

AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer

PURPOSE

To evaluate the feasibility of using mpMRI image features predicted by AI algorithms in the prediction of clinically significant prostate cancer (csPCa).

MATERIALS AND METHODS

This study analyzed patients who underwent prostate mpMRI and radical prostatectomy (RP) at the Affiliated Hospital of Jiaxing University between November 2017 and December 2022. The clinical data collected included age, serum prostate-specific antigen (PSA), and biopsy pathology. The reference standard was the prostatectomy pathology, and a Gleason Score (GS) of 3 + 3 = 6 was considered non-clinically significant prostate cancer (non-csPCa), while a GS ≥ 3 + 4 was considered csPCa. A pre-trained AI algorithm was used to extract the lesion on mpMRI, and the image features of the lesion and the prostate gland were analyzed. Two logistic regression models were developed to predict csPCa: an MR model and a combined model. The MR model used age, PSA, PSA density (PSAD), and the AI-predicted MR image features as predictor variables. The combined model used biopsy pathology and the aforementioned variables as predictor variables. The model's effectiveness was evaluated by comparing it to biopsy pathology using the area under the curve (AUC) of receiver operation characteristic (ROC) analysis.

RESULTS

A total of 315 eligible patients were enrolled with an average age of 70.8 ± 5.9. Based on RP pathology, 18 had non-csPCa, and 297 had csPCa. PSA, PSAD, biopsy pathology, and ADC value of the prostate outside the lesion (ADCprostate) varied significantly across different ISUP grade groups of RP pathology (P < 0.001). Other clinical variables and image features did not vary significantly across different ISUP grade groups (P > 0.05). The MR model included PSAD, the ratio of ADC value between the lesion and the prostate outside the lesion (ADClesion/prostate), the signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate), and the ratio of DWIlesion/prostate to ADClesion/prostate. The combined model included biopsy pathology, ADClesion/prostate, mean signal intensity of the lesion on DWI (DWIlesion), DWI signal intensity of the prostate outside the lesion (DWIprostate), and signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate). The AUC of the MR model (0.830, 95% CI 0.743, 0.916) was not significantly different from that of biopsy pathology (0.820, 95% CI 0.728, 0.912, P = 0.884). The AUC of the combined model (0.915, 95% CI 0.849, 0.980) was higher than that of the biopsy pathology (P = 0.042) and MR model (P = 0.031).

CONCLUSION

The aggressiveness of prostate cancer can be effectively predicted using AI-extracted image features from mpMRI images, similar to biopsy pathology. The prediction accuracy was improved by combining the AI-extracted mpMRI image features with biopsy pathology, surpassing the performance of biopsy pathology alone.

Perilesional Targeted Biopsy Combined with MRI-TRUS Image Fusion-Guided Targeted Prostate Biopsy: An Analysis According to PI-RADS Scores

A prostate-targeted biopsy (TB) core is usually collected from a site where magnetic resonance imaging (MRI) indicates possible cancer. However, the extent of the lesion is difficult to accurately predict using MRI or TB alone. Therefore, we performed several biopsies around the TB site (perilesional [p] TB) and analyzed the association between the positive cores obtained using TB and pTB and the Prostate Imaging Reporting and Data System (PI-RADS) scores. This retrospective study included patients who underwent prostate biopsies. The extent of pTB was defined as the area within 10 mm of a TB site. A total of 162 eligible patients were enrolled. Prostate cancer (PCa) was diagnosed in 75.2% of patients undergoing TB, with a positivity rate of 50.7% for a PI-RADS score of 3, 95.8% for a PI-RADS score of 4, and 100% for a PI-RADS score of 5. Patients diagnosed with PCa according to both TB and pTB had significantly higher positivity rates for PI-RADS scores of 4 and 5 than for a PI-RADS score of 3 (p < 0.0001 and p = 0.0009, respectively). Additional pTB may be performed in patients with PI-RADS ≥ 4 regions of interest for assessing PCa malignancy.

Evaluating the Utility of Prostate-Specific Antigen Density in Risk Stratification of PI-RADS 3 Peripheral Zone Lesions on Non-Contrast-Enhanced Prostate MRI: An Exploratory Single-Institution Study

Objective This study aimed to explore the potential of prostate-specific antigen density (PSAD) as a supplementary tool for defining high-risk Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions in the peripheral zone on non-contrast-enhanced MRI. This additional stratification tool could supplement the decision-making process for biopsy, potentially helping in identifying higher-risk patients more accurately, minimizing unnecessary procedures in lower-risk patients, and limiting the need for dynamic contrast-enhanced (DCE) scans. Materials and methods Between January 2019 and April 2023, 30 patients with PI-RADS 3 lesions underwent MRI-ultrasound fusion biopsies at our institution. Age and PSAD values were investigated using logistic regression and chi-square automatic interaction detection (CHAID) analysis to discern their predictive value for malignancy. Results The mean patient age was 64.7 years, and the mean PSAD was 0.13 ng/mL2. Logistic regression demonstrated PSAD to be a significant predictor of cancer (p=0.012), but not age (p=0.855). CHAID analysis further identified a PSAD cut-off value of 0.12, below which the cancer detection rate was 23.1% and above which the rate increased to 76.5%. Conclusions This exploratory study suggests that PSAD might be utilized to enhance the stratification of high-risk PI-RADS 3 lesions in the peripheral zone on non-contrast-enhanced MRI, aiding in decision-making for biopsy. While biopsy remains the gold standard for definitive diagnosis, a high PSAD value may suggest a greater need for biopsy in this specific group. Although further validation in larger cohorts is required, our findings contribute to the ongoing discourse on optimizing PI-RADS 3 lesion management. Limitations include a small sample size, the retrospective nature of the study, and the single-center setting, which may impact the generalizability of our results.

Intravoxel incoherent motion predicts positive surgical margins and Gleason score upgrading after radical prostatectomy for prostate cancer

BACKGROUND

Whether Intravoxel incoherent motion (IVIM) can be used as a predictive tool of positive surgical margins (PSMs) and Gleason score (GS) upgrading in prostate cancer (PCa) patients after radical prostatectomy (RP) still remains unclear. The aim of this study is to explore the ability of IVIM and clinical characteristics to predict PSMs and GS upgrading.

METHODS

A total of 106 PCa patients after RP who underwent pelvic mpMRI (multiparametric Magnetic Resonance Imaging) between January 2016 and December 2021 and met the requirements were retrospectively included in our study. IVIM parameters were obtained using GE Functool post-processing software. Logistic regression models were fitted to confirm the predictive risk factor of PSMs and GS upgrading. The area under the curve and fourfold contingency table were used to evaluate the diagnostic efficacy of IVIM and clinical parameters.

RESULTS

Multivariate logistic regression analyses revealed that percent of positive cores, apparent diffusion coefficient and molecular diffusion coefficient (D) were independent predictors of PSMs (Odds Ratio (OR) were 6.07, 3.62 and 3.16, respectively), Biopsy GS and pseudodiffusion coefficient (D*) were independent predictors of GS upgrading (OR were 0.563 and 7.15, respectively). The fourfold contingency table suggested that combined diagnosis increased the ability of predicting PSMs but had no advantage in predicting GS upgrading except the sensitivity from 57.14 to 91.43%.

CONCLUSIONS

IVIM showed good performance in predicting PSMs and GS upgrading. Combining IVIM and clinical factors enhanced the performance of predicting PSMs, which may contribute to clinical diagnosis and treatment.

The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer

PURPOSE

To evaluate the diagnostic accuracy of [18F]-DCFPyL PET/MRI radiomics for the prediction of pathological grade group in prostate cancer (PCa) in therapy-naïve patients.

METHODS

Patients with confirmed or suspected PCa, who underwent [18F]-DCFPyL PET/MRI (n = 105), were included in this retrospective analysis of two prospective clinical trials. Radiomic features were extracted from the segmented volumes following the image biomarker standardization initiative (IBSI) guidelines. Histopathology obtained from systematic and targeted biopsies of the PET/MRI-detected lesions was the reference standard. Histopathology patterns were dichotomized as ISUP GG 1-2 vs. ISUP GG ≥ 3 categories. Different single-modality models were defined for feature extraction, including PET- and MRI-derived radiomic features. The clinical model included age, PSA, and lesions' PROMISE classification. Single models, as well as different combinations of them, were generated to calculate their performances. A cross-validation approach was used to evaluate the internal validity of the models.

RESULTS

All radiomic models outperformed the clinical models. The best model for grade group prediction was the combination of PET + ADC + T2w radiomic features, showing sensitivity, specificity, accuracy, and AUC of 0.85, 0.83, 0.84, and 0.85, respectively. The MRI-derived (ADC + T2w) features showed sensitivity, specificity, accuracy, and AUC of 0.88, 0.78, 0.83, and 0.84, respectively. PET-derived features showed 0.83, 0.68, 0.76, and 0.79, respectively. The baseline clinical model showed 0.73, 0.44, 0.60, and 0.58, respectively. The addition of the clinical model to the best radiomic model did not improve the diagnostic performance. The performances of MRI and PET/MRI radiomic models as per the cross-validation scheme yielded an accuracy of 0.80 (AUC = 0.79), whereas clinical models presented an accuracy of 0.60 (AUC = 0.60).

CONCLUSION

The combined [18F]-DCFPyL PET/MRI radiomic model was the best-performing model and outperformed the clinical model for pathological grade group prediction, indicating a complementary value of the hybrid PET/MRI model for non-invasive risk stratification of PCa. Further prospective studies are required to confirm the reproducibility and clinical utility of this approach.

Transperineal Prostate Biopsy Targeted by Magnetic Resonance Imaging Cognitive Fusion

Prostate cancer is among the most frequently diagnosed cancers and a leading cause of cancer-related death in men. Currently, the most reliable and widely used imaging test for prostate cancer diagnosis is multiparametric pelvic magnetic resonance imaging (mpMRI). Modern biopsy techniques are based on the computerised merging of ultrasound and MRI images to provide better vision during the biopsy procedure (Fusion Biopsy). However, the method is expensive due to high equipment cost. Cognitive fusion of ultrasound and MRI images has recently emerged as a cheaper and easier alternative to computerised fusion. The aim of this prospective study is to perform an in-patient comparison of the systematic prostate biopsy procedure (SB) vs. cognitive fusion (CF) guided prostate biopsy method in terms of safety, ease of use, cancer detection rate and clinically significant cancer detection. We enrolled 103 patients with suspected prostate cancer that were biopsy naive, with PSA > 4 ng/dL and PIRADS score of 3, 4 or 5. All patients received a transperineal standard 12-18 cores systematic biopsy (SB) and a four-cores targeted cognitive fusion (CF) biopsy. Following the prostate biopsy, 68% of the patients were diagnosed with prostate cancer (70/103 patients). SB diagnosis rate was 62% while CF biopsy was slightly better with a 66% rate. There was a significant 20% increase in clinically significant prostate cancer detection rate for the CF compared to SB (p < 0.05) and a significant prostate cancer risk upgrade from the low to the intermediate risk category (13%, p = 0.041). Transperineal cognitive fusion targeted prostate biopsy is a straightforward biopsy method that is easy to perform and is a safe alternative to standard systematic biopsy with improved significant cancer detection accuracy. A combined targeted and systematic approach should be used for the best diagnostic results.

Incidence and Predicting Factors of Histopathological Features at Robot-Assisted Radical Prostatectomy in the mpMRI Era: Results of a Single Tertiary Referral Center

Background and Objectives: To describe the predictors of cribriform variant status and perineural invasion (PNI) in robot-assisted radical prostatectomy (RARP) histology. To define the rates of upgrading between biopsy specimens and final histology and their possible predictive factors in prostate cancer (PCa) patients undergoing RARP. Material and Methods: Within our institutional database, 265 PCa patients who underwent prostate biopsies and consecutive RARP at our center were enrolled (2018-2022). In the overall population, two independent multivariable logistic regression models (LRMs) predicting the presence of PNI or cribriform variant status at RARP were performed. In low- and intermediate-risk PCa patients according to D'Amico risk classification, three independent multivariable LRMs were fitted to predict upgrading. Results: Of all, 30.9% were low-risk, 18.9% were intermediate-risk and 50.2% were high-risk PCa patients. In the overall population, the rates of the cribriform variant and PNI at RARP were 55.8% and 71.1%, respectively. After multivariable LRMs predicting PNI, total tumor length in biopsy cores (>24 mm [OR: 2.37, p-value = 0.03], relative to <24 mm) was an independent predictor. After multivariable LRMs predicting cribriform variant status, PIRADS (3 [OR:15.37], 4 [OR: 13.57] or 5 [OR: 16.51] relative to PIRADS 2, all p = 0.01) and total tumor length in biopsy cores (>24 mm [OR: 2.47, p = 0.01], relative to <24 mm) were independent predicting factors. In low- and intermediate-risk PCa patients, the rate of upgrading was 74.4% and 78.0%, respectively. After multivariable LRMs predicting upgrading, PIRADS (PIRADS 3 [OR: 7.01], 4 [OR: 16.98] or 5 [OR: 20.96] relative to PIRADS 2, all p = 0.01) was an independent predicting factor. Conclusions: RARP represents a tailored and risk-adapted treatment strategy for PCa patients. The indication of RP progressively migrates to high-risk PCa after a pre-operative assessment. Specifically, the PIRADS score at mpMRI should guide the decision-making process of urologists for PCa patients.

Utility of dual read in the setting of prostate MRI interpretation

PURPOSE

The purpose of this study is to assess the utility of dual reader interpretation of prostate MRI in the evaluation/detection of prostate cancer, using the PI-RADS v2.1 scoring system.

METHODS

We performed a retrospective study to assess the utility of dual reader interpretation for prostate MRI. All MRI cases compiled for analysis were accompanied with prostate biopsy pathology reports that included Gleason scores to correlate to the MRI PI-RADS v2.1 score, tissue findings and location of pathology within the prostate gland. To assess for dual reader utility, two fellowship trained abdominal imagers (each with > 5 years of experience) provided independent and concurrent PI-RADS v2.1 scores on all included MRI examinations, which were then compared to the biopsy proven Gleason scores.

RESULTS

After application of inclusion criteria, 131 cases were used for analysis. The mean age of the cohort was 63.6 years. Sensitivity, specificity and positive/negative predictive values were calculated for each reader and concurrent scores. Reader 1 demonstrated 71.43% sensitivity, 85.39% specificity, 69.77% PPV and 86.36% NPV. Reader 2 demonstrated 83.33% sensitivity, 78.65% specificity, 64.81% PPV and 90.91% NPV. Concurrent reads demonstrated 78.57% sensitivity, 80.9% specificity, 66% PPV and 88.89% NPV. There was no statistically significant difference between the individual readers or concurrent reads (p = 0.79).

CONCLUSION

Our results highlight that dual reader interpretation in prostate MRI is not needed to detect clinically relevant tumor and that radiologists with experience and training in prostate MRI interpretation establish acceptable sensitivity and specificity marks on PI-RADS v2.1 assessment.

Quantification of prostate tumour diameter and volume from MR images using 3D ellipsoid model and its impact on PI-RADS v2.1 assessment

Maximum diameter and volume of the tumour provide important clinical information and are decision-making parameters for patients suspected with prostate cancer (PCa). The objectives of this study were to develop an automated method for 3D tumour measurement and compare it with the radiologist's manual assessment, as well as to investigate the impact of 3D tumour measurement on Prostate Imaging-Reporting and Data System version-2.1 (PI-RADS v2.1) scoring of prostate cancer. Tumour maximum diameter and volume were calculated using automated ellipsoid-fit method. For all PI-RADS scores, mean ± standard deviation range of tumour maximum diameter and volume measured using ellipsoid-fit method were 1.36 ± 0.28 to 1.97 ± 0.67 cm and 0.49 ± 0.31 to 1.05 ± 0.78 cc and manual assessment were in range of 0.73 ± 0.12 to 1.14 ± 0.25 cm and 0.36 ± 0.21 to 0.93 ± 0.39 cc, respectively. Ellipsoid-fit method showed significantly (p < 0.05) higher values for maximum diameter and volume than manual assessment. 3D measurement of tumour using ellipsoid-fit method was found to have higher maximum diameter and volume values (in 40-61% patients) compared to conventional assessment by radiologist, which may have an impact on PI-RADS v2.1 scoring system.

Clinical Factors Associated With Pathological Grade Group 1 Patients in D'Amico Intermediate-Risk Group Following Robot-Assisted Radical Prostatectomy: A Retrospective Multicenter Cohort Study in Japan (The MSUG94 Group)

INTRODUCTION

We aimed to examine the relationship between D'Amico intermediate-risk and pathological grade group 1 (pGG1) after robot-assisted radical prostatectomy (RARP).

PATIENTS AND METHODS

In this retrospective multicenter cohort study, D'Amico intermediate-risk prostate cancer patients who did not receive neoadjuvant therapy, and underwent RARP at 10 institutions in Japan were examined for preoperative factors associated with pGG1.

RESULTS

In total, we enrolled 1161 D'Amico intermediate-risk prostate cancer patients. The pGG1 and pGG ≥2 groups comprised 73 (6.3%), and 1088 (93.7%) cases, respectively. Biochemical recurrence-free survival (BCRFS) of the pGG1 group was equivalent to that of the D'Amico low-risk patients. Among the 3 D'Amico intermediate-risk factors (IRF), the pGG1-rate was 24% with prostate-specific antigen (PSA) of 10 to 20 ng/mL alone, and 30% with cT2b alone. Both groups had significantly higher pGG1-rates than other groups. Down-grading from biopsy GG ≥2 to pGG1 was relatively rare (3.9%). Patients with pGG1 were further stratified by prostate volume (PV) (cutoff, 40 cc) among patients with one IRF and PSA of 10 to 20 ng/mL. Patients with one IRF, PSA of 10 to 20 ng/mL, and PV >40 cc had a relatively good BCRFS similar to that of the D'Amico low-risk group.

CONCLUSION

Among intermediate-risk prostate cancer patients, those with pGG1 have a good prognosis. Downgrading from biopsy GG ≥2 is rare, and definitive treatment may be recommended for patients with biopsy GG ≥2. Patients with one IRF, PSA of 10 to 20 ng/mL, and PV >40 cc who are eligible for RARP may be candidates for active surveillance.

COMPARISON OF GRADING ACCURACY OF PROSTATE CANCER IN SAMPLES ACQUIRED BY A TARGETED AND SYSTEMIC PROSTATE BIOPSY

Introduction

All malignancies, including prostate cancer, require accurate diagnosing and staging before making a treatment decision. The introduction of targeted biopsies based on prostate MRI findings has raised prostate biopsy accuracy. Guided biopsies target the tumor itself during the biopsy instead of the most common tumor sites as is the case with a systemic biopsy. Some studies report that targeted biopsies should lower prostate cancer biopsy undergrading and overgrading.

Goals

To determine the incidence of prostate cancer biopsy undergrading in patients who underwent a classic systemic biopsy compared to patients who underwent a mpMRI cognitive targeted biopsy.

Materials and methods

We identified the patients from our database who underwent a radical prostatectomy at our institution from January 1st, 2021, to June 30th, 2021.There were 112 patients identified. Patients were stratified into two groups based on the type of biopsy that confirmed prostate cancer. The mpMRI (N=50) group had a mpMRI cognitive guided transrectal ultrasound (TRUS) prostate biopsy performed, and the non-mpMRI group (N=62) received a classic, systemic TRUS biopsy. We compared the biopsy results with the final pathological results, and searched for undergrading or overgrading in the biopsies compared to the final histological report.

Results

The undergrading was found in 17,7% (N=11) cases in the non-mpMRI group and in 12,0% (N=6) of cases in the mpMRI group (p=0,02, Mann-Whitney U test). No overgrading was found in our cohort. All cases of undergrading had Grade Group 1 in the biopsy report and Grade Group 2 in the final specimen report. The charasteristics of patients are listed in Table 1.

Discussion and conclusion

In our cohort, the patients who underwent a mpMRI targeted biopsy had a lower undergrading incidence. During a systemic TRUS biopsy, the urologist targets the areas of the prostate where cancer is most commonly located, which is usually the peripheral zone of the prostate. Since different areas of the tumor have different areas of differentiation, only a low-grade part of the tumor is sometimes biopsied, which results in a sampling error. Once the prostate is removed, the whole tumor is analyzed, so the obtained pathological results related to the removed prostate are far more accurate than the analysis of prostate cores obtained by biopsy.

From Cognitive MR-Targeted Fusion Prostate Biopsy to Radical Prostatectomy: Incidence and Predictors of Gleason Grade Group Upgrading in a Chinese Cohort

Purpose

To access the incidence and predictors of Gleason grade group upgrading from cognitive MR-targeted fusion prostate biopsy to radical prostatectomy in a Chinese cohort.

Materials and Methods

We included 199 patients in our institution between January 2016 and June 2021. Multivariable logistic regression model and nomograms were utilized to analyze the collected data.

Results

The concordance rate of biopsy Gleason grade group and radical prostatectomy was 50.3% (100 in 199). Upgrading occurred in 80 (40.2%) patients and 37 (68.5%) patients have an upgrading Gleason grade group when the biopsy Gleason grade group was 1. Multivariable logistic regression models were established to analyze the incidence and predictors of Gleason grade group upgrading from cognitive MR-targeted fusion prostate biopsy to radical prostatectomy. Biopsy Gleason grade group, prostate volume, and patient year were confirmed to be individual predictors of upgrading. Based on the logistic regression models, nomograms for predicting probability of prostate Gleason grade group upgrading were generated.

Conclusions

We established a logistic regression model to predict the accuracy of prostate biopsy GG and provide the probability of upgrading. Clinicians should be more cautious when deciding the treatment strategy especially for prostate cancer biopsy GG1 patients. Future studies should expand the sample size and include more variables to improve the accuracy of predicting upgrading and prostate cancer early screening program is urgently needed in our city in China.

Study on the Diagnostic Value of Contrast-Enhanced Ultrasound and Magnetic Resonance Imaging in Prostate Cancer

Objective

The aim is to study the different roles of single and joint application of magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) in prostate malignant tumors.

Methods

72 patients with prostate masses who underwent CEUS and MRI examination in our hospital from October 2021 and March 2022 were enrolled in this research. The differentially diagnostic roles of CEUS, MRI, and CEUS combined MRI for prostate cancer was assessed on basis of pathological findings as the reference standard. The specificity and sensitivity of the joint application for prostate malignant tumors with various prostate-specific antigen (PSA) levels were also evaluated.

Results

The sensitivity of CEUS, MRI, and the joint application for prostate cancer were 72.1%, 74.4%, and 90.7%, respectively. Compared with single application, the sensitivity of CEUS combined with MRI was significantly higher. The specificity of MRI, CEUS, and the combination of the two for prostate cancer were 82.8%, 79.3%, and 89.7%, respectively, and the statistical differences for specificity were not found. The area under ROC curve (AUC) of CEUS combined with MRI in prostate malignant tumor diagnosis was obviously more than that of CEUS and MRI (P < 0.05). CEUS combined with MRI has relative high sensitivity in these patients with different levels of PSA.

Conclusions

Contrast-enhanced ultrasound combined with MRI can significantly improve the sensitivity and specificity of prostate cancer diagnosis so that patients can be better diagnosed in advance.

Molecular Characterization of Cancer Associated Fibroblasts in Prostate Cancer

BACKGROUND

Stromal components surrounding epithelial cancer cells seem to play a pivotal role during epithelial-to-mesenchymal transition (EMT), tumor invasion, and metastases. To identify the molecular mechanisms underlying tumor-stroma interactions may yield novel therapeutic targets for prostate cancer.

METHODS

Gene expression profile of prostate-cancer associated fibroblast (PCAF) and prostate non-cancer associated fibroblast (PNAF) cells isolated from radical prostatectomy was performed by Illumina, analyzed, and further processed by Ingenuity^®: IPA^® software. qRT-PCR was performed on an independent set of 17 PCAF, 12 PNAF, and 12 fibroblast cell lines derived from patients with benign prostatic hyperplasia (BPHF).

RESULTS

Using microarray analysis, we found six upregulated genes and two downregulated genes in PCAFs compared to PNAFs. To validate microarray results, we performed qRT-PCR for the most significantly regulated genes involved in the modulation of proliferation and androgen resistance on an independent set of PNAF, PCAF, and BHPF samples. We confirmed the increased expression of SCARB1, MAPK3K1, and TGF-β as well as the decreased expression of S100A10 in PCAFs compared to PNAFs and BPHFs.

CONCLUSIONS

These results provide strong evidence that the observed changes in the gene expression profile of PCAFs can contribute to functional alteration of adjacent prostate cancer cells.

Machine Learning-Based Prediction of Pathological Upgrade From Combined Transperineal Systematic and MRI-Targeted Prostate Biopsy to Final Pathology: A Multicenter Retrospective Study

Objective

This study aimed to evaluate the pathological concordance from combined systematic and MRI-targeted prostate biopsy to final pathology and to verify the effectiveness of a machine learning-based model with targeted biopsy (TB) features in predicting pathological upgrade.

Materials and Methods

All patients in this study underwent prostate multiparametric MRI (mpMRI), transperineal systematic plus transperineal targeted prostate biopsy under local anesthesia, and robot-assisted laparoscopic radical prostatectomy (RARP) for prostate cancer (PCa) sequentially from October 2016 to February 2020 in two referral centers. For cores with cancer, grade group (GG) and Gleason score were determined by using the 2014 International Society of Urological Pathology (ISUP) guidelines. Four supervised machine learning methods were employed, including two base classifiers and two ensemble learning-based classifiers. In all classifiers, the training set was 395 of 565 (70%) patients, and the test set was the remaining 170 patients. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The Gini index was used to evaluate the importance of all features and to figure out the most contributed features. A nomogram was established to visually predict the risk of upgrading. Predicted probability was a prevalence rate calculated by a proposed nomogram.

Results

A total of 515 patients were included in our cohort. The combined biopsy had a better concordance of postoperative histopathology than a systematic biopsy (SB) only (48.15% vs. 40.19%, p = 0.012). The combined biopsy could significantly reduce the upgrading rate of postoperative pathology, in comparison to SB only (23.30% vs. 39.61%, p < 0.0001) or TB only (23.30% vs. 40.19%, p < 0.0001). The most common pathological upgrade occurred in ISUP GG1 and GG2, accounting for 53.28% and 20.42%, respectively. All machine learning methods had satisfactory predictive efficacy. The overall accuracy was 0.703, 0.768, 0.794, and 0.761 for logistic regression, random forest, eXtreme Gradient Boosting, and support vector machine, respectively. TB-related features were among the most contributed features of a prediction model for upgrade prediction.

Conclusion

The combined effect of SB plus TB led to a better pathological concordance rate and less upgrading from biopsy to RP. Machine learning models with features of TB to predict PCa GG upgrading have a satisfactory predictive efficacy.

CRMP4 CpG Hypermethylation Predicts Upgrading to Gleason Score ≥ 8 in Prostate Cancer

Background

This study determined the predictive value of CRMP4 promoter methylation in prostate tissues collected by core needle biopsies for a postoperative upgrade of Gleason Score (GS) to ≥8 in patients with low-risk PCa.

Method

A retrospective analysis of the clinical data was conducted from 631 patients diagnosed with low-risk PCa by core needle biopsy at multiple centers and then underwent Radical Prostatectomy (RP) from 2014-2019. Specimens were collected by core needle biopsy to detect CRMP4 promoter methylation. The pathologic factors correlated with the postoperative GS upgrade to ≥8 were analyzed by logistic regression. The cut-off value for CRMP4 promoter methylation in the prostate tissues collected by core needle biopsy was estimated from the ROC curve in patients with a postoperative GS upgrade to ≥8.

Result

Multivariate logistic regression showed that prostate volume, number of positive cores, and CRMP4 promoter methylation were predictive factors for a GS upgrade to ≥8 (OR: 0.94, 95% CI: 0.91-0.98, P=0.003; OR: 3.16, 95% CI: 1.81-5.53, P<0.001; and OR: 1.43, 95% CI: 1.32-1.55, P<0.001, respectively). The positive predictive rate was 85.2%, the negative predictive rate was 99.3%, and the overall predictive rate was 97.9%. When the CRMP4 promoter methylation rate was >18.00%, the low-risk PCa patients were more likely to escalate to high-risk patients. The predictive sensitivity and specificity were 86.9% and 98.8%, respectively. The area under the ROC curve (AUC) was 0.929 (95% CI: 0.883-0.976; P<0.001). The biochemical recurrence (BCR)-free survival, progression-free survival (PFS), and cancer-specific survival (CSS) were worse in patients with CRMP4 methylation >18.0% and postoperative GS upgrade to ≥8 than in patients without an upgrade (P ≤ 0.002).

Conclusion

A CRMP4 promoter methylation rate >18.00% in prostate cancer tissues indicated that patients were more likely to escalate from low-to-high risk after undergoing an RP. We recommend determining CRMP4 promoter methylation before RP for low-risk PCa patients.

Evaluation of selective bone scan staging in prostate cancer - external validation of current strategies and decision-curve analysis

Background

Recommendations for staging newly diagnosed prostate cancer patients vary between guidelines and literature.

Methods

Our objective was to validate and compare prediction models selecting newly diagnosed prostate cancer patients for bone scan staging. To achieve this, we validated eleven models in a population-based cohort of 10,721 patients diagnosed with prostate cancer between 2005 and 2019. The primary outcome was net-benefit. This was assessed at different balances of conservatism and tolerance, represented by preference ratio and number-willing-to-test (NWT). Secondary outcomes included calibration slope, calibration-in-the-large (intercept), and discrimination measured by Area-under-the-receiver-operator-characteristics curve (AUC).

Results

For preference ratios less than 1:39 (NWT greater than 40), scanning everyone provided greater net-benefit than selective staging. For preference ratios 1:39 to 3:97 (NWT 33–40), the European Association of Urology (EAU) 2020 guideline recommendation was the best approach. For preference ratios 3:97–7:93 (NWT 14–33), scanning EAU high-risk patients only was preferable. For preference ratios 7:93–1:9 (NWT 10–13), scanning only Gnanapragasam Group 5 patients was best. All models had similar fair discrimination (AUCs 0.68–0.80), but most had poor calibration.

Conclusions

We identified three selective staging strategies that outperformed all other approaches but did so over different ranges of conservatism and tolerance. Scanning only EAU high-risk patients provided the greatest net-benefit over the greatest range of preference ratios and scenarios, but other options may be preferable depending upon the local healthcare system’s degree of conservatism and tolerance.

Predicting the Performance of Concurrent Systematic Random Biopsies during Image Fusion Targeted Sampling of Multi-Parametric MRI Detected Prostate Cancer. A Prospective Study (PRESET Study)

Simple Summary

The study provides a predictive model by using clinical factors in selecting men who may benefit from the addition of systematic biopsies with an image fusion targeted approach. The approach is likely to improve the detection of csPCa and avoid unnecessary detection of indolent prostate cancers.

Abstract

The study was aimed to develop a predictive model to identify patients who may benefit from performing systematic random biopsies (SB) in addition to targeted biopsies (TB) in men suspected of having prostate cancer. A total of 198 patients with positive pre-biopsy MRI findings and who had undergone both TB and SB were prospectively recruited into this study. The primary outcome was detection rates of clinically significant prostate cancer (csPCa) in SB and TB approaches. The secondary outcome was net clinical benefits of SB in addition to TB. A logistic regression model and nomogram construction were used to perform a multivariate analysis. The detection rate of csPCa using SB was 51.0% (101/198) compared to a rate of 56.1% (111/198) for TB, using a patient-based biopsy approach. The detection rate of csPCa was higher using a combined biopsy (64.6%; 128/198) in comparison to TB (56.1%; 111/198) alone. This was statistically significant (p < 0.001). Age, PSA density and PIRADS score significantly predicted the detection of csPCa by SB in addition to TB. A nomogram based on the model showed good discriminative ability (C-index; 78%). The decision analysis curve confirmed a higher net clinical benefit at an acceptable threshold.

Race-insurance disparities in prostate patients' magnetic resonance imaging biopsies and their subsequent cancer care: a New York State cohort study

For organ-confined prostate cancer, socioeconomic factors influencing Magnetic Resonance Imaging (MRI)-guided biopsy utilization and downstream prostate cancer patients' care are unknown. This retrospective, observational cohort study used the New York Statewide Planning and Research Cooperative System (SPARCS) billing-code driven database to examine the impact of prostate patients' socioeconomic characteristics on prostate cancer care defined as initial biopsy, 2-month post-biopsy cancer diagnoses, and within 1-year cancer-related intervention, controlling for other risk factors. From 2011-2017, the population studied (n = 18,253) included all New York State-based, male, residents aged 18 to 75 without a prior prostatectomy receiving a first-time biopsy; 760 such patient records in 2016 were removed due to data quality concerns. Major exposures included patient age, race, ethnicity and insurance. The major outcome included receipt of MRI biopsy versus standard biopsy and for these sub-populations, subsequent 2-month post-biopsy metastatic versus non-metastatic prostate cancer diagnosis and within 1-year prostate cancer treatment (prostatectomy with or without radiation versus prostatectomy-only) were compared using dichotomous (primary) and time-to-event (secondary) endpoints. Of 17,493 patients with a first-time prostate biopsy, 3.89% had MRI guided biopsies; of the 17,128 patients with no pre-biopsy cancer diagnosis, the subsequent prostate cancer diagnosis rate was 42.59%. For 6,754 non-metastatic prostate cancer patients with 1-year follow-up, 1,674 (24.79%) received surgery (with or without radiation) and 495 (7.33%) received radiation-only. Holding other factors constant, multivariable regression models identified that race-insurance was a primary predictor of MRI-guided biopsy use. Compared to commercially insured White patients, Black patients across all insurance categories received MRI-guided biopsies less frequently; Commercially insured and self-pay Black patients also had increased chance of prostate cancer diagnosis. Across all insurers, Black patients had lower likelihood of prostatectomies. In contrast, Black and White patients with government insurance were more likely to have within 1-year radiation-only treatments versus commercially insured White patients. Thus, across the prostate cancer care continuum, race-insurance affected prostate cancer-related service utilization. Future research should evaluate the generalizability of these New York State findings.

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.

Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer

Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Quantitative ultrasound shear wave elastography (USWE)-measured tissue stiffness correlates with PIRADS scoring of MRI and Gleason score on whole-mount histopathology of prostate cancer: implications for ultrasound image-guided targeting approach

OBJECTIVE

To correlate quantitative tissue stiffness measurements obtained by transrectal ultrasound shear wave elastography (USWE) with PI-RADS scoring of multiparametric magnetic imaging resonance (mpMRI) using Gleason scores of radical prostatectomy as a reference standard.

PATIENTS AND METHODS

196 men with localised prostate cancer were prospectively recruited into the study and had quantitative prostate tissue stiffness measurements in kilopascals (kPa) using transrectal USWE prior to radical prostatectomy. PI-RADS scores of mpMRI were also obtained in all the men. Imaging and histopathology of radical prostatectomy specimen were oriented to each other using patient specific customised 3D moulds to guide histopathology grossing of radical prostatectomy specimens. All included patients had confirmed PCa on TRUS-guided biopsies, had both USWE and mpMRI imaging data, and underwent radical prostatectomy. Chi-square test with 95% confidence interval was used to assess the difference between Gleason score (GS) of radical prostatectomy and PI-RADS classification, as well as GS of radical prostatectomy and stiffness (in Kpa) using USWE. The correlation coefficient (r) was calculated in order to investigate relation between PI-RADS classification and tissue stiffness in kPa.

RESULTS

There was a statistically significant correlation between USWE-measured tissue stiffness and GS (χ2 (2, N = 196) = 23.577, p < 0.001). Also, there was a statistically significant correlation between Gleason score and PI-RADS score (χ2 (2, N = 196) = 12.838, p = 0.002). High PIRADS on MRI and high stiffness on USWE (> 100 kPa) detected more than 80% and 90% high risk prostate cancer disease. However, a weak correlation coefficient of 0.231 was observed between PI-RADS score and level of tissue stiffness measured in kPa.

CONCLUSION

Quantitative USWE and mpMRI using PI-RADS classification provide a good degree of prediction for Gleason score of clinically significant prostate cancer (csPCa). Stiffer lesions on ultrasound showed a weak correlation with PI-RADS scoring system. USWE could be used to target suspected prostate cancer.

Optimal Method for Reporting Prostate Cancer Grade in MRI-targeted Biopsies

When multiple cores are biopsied from a single magnetic resonance imaging (MRI)-targeted lesion, Gleason grade may be assigned for each core separately or for all cores of the lesion in aggregate. Because of the potential for disparate grades, an optimal method for pathology reporting MRI lesion grade awaits validation. We examined our institutional experience on the concordance of biopsy grade with subsequent radical prostatectomy (RP) grade of targeted lesions when grade is determined on individual versus aggregate core basis. For 317 patients (with 367 lesions) who underwent MRI-targeted biopsy followed by RP, targeted lesion grade was assigned as (1) global Grade Group (GG), aggregated positive cores; (2) highest GG (highest grade in single biopsy core); and (3) largest volume GG (grade in the core with longest cancer linear length). The 3 biopsy grades were compared (equivalence, upgrade, or downgrade) with the final grade of the lesion in the RP, using κ and weighted κ coefficients. The biopsy global, highest, and largest GGs were the same as the final RP GG in 73%, 68%, 62% cases, respectively (weighted κ: 0.77, 0.79, and 0.71). For cases where the targeted lesion biopsy grade scores differed from each other when assigned by global, highest, and largest GG, the concordance with the targeted lesion RP GG was 69%, 52%, 31% for biopsy global, highest, and largest GGs tumors (weighted κ: 0.65, 0.68, 0.59). Overall, global, highest, and largest GG of the targeted biopsy show substantial agreement with RP-targeted lesion GG, however targeted global GG yields slightly better agreement than either targeted highest or largest GG. This becomes more apparent in nearly one third of cases when each of the 3 targeted lesion level biopsy scores differ. These results support the use of global (aggregate) GG for reporting of MRI lesion-targeted biopsies, while further validations are awaited.

Age and gleason score upgrading between prostate biopsy and radical prostatectomy: Is this still true in the multiparametric resonance imaging era?

INTRODUCTION

Several studies have invariably shown that the risk of Grade Group (GG) upgrading between biopsy and radical prostatectomy (RP) is higher in elderly men. Whether this is due to a real biological effect or to a diagnostic bias is still unknown. We hypothesized that the introduction of multiparametric magnetic resonance imaging (MRI) has improved the diagnostic accuracy of PCa detection in older men thus reducing the risk of GG upgrading at RP reported in the pre-MRI era.

MATERIALS AND METHODS

We selected 424 men who received a systematic plus targeted biopsy for a positive MRI and subsequent RP at two referral centers between 2013 and 2019. Upgrading was defined as an increase in GG at final pathology as compared to biopsy. Multivariable logistic regressions tested the risk of upgrading over increasing age according to any upgrading definition and after stratifying definitions according to GG group and biopsy type. Non-parametric functions explored the relationship between age and upgrading rate.

RESULTS

Median rate of upgrading was 17%. In multivariable models, while age was not associated with increased risk of GG upgrading (p=0.4). At non-parametric analyses, probability of upgrading slightly decreased with age, without reaching statistical significance. In subgroup analyses according to different upgrading definition and to biopsy type, age did not predict higher risk of upgrading regardless of outcome definitions (GG 1 to 2 P = 0.1; GG 2 to 3 P = 0.2; GG 3 to 4-5 P = 0.2) and in GG detected at TBx (OR 0.998, P = 0.8).

CONCLUSIONS

We showed that use of MRI has obliterated the association between older age and increased risk of upgrading mainly due to improved diagnostic approaches in this group of men. Therefore, it is likely that the effect of age and GG upgrading reported in previous studies in elderly men was due to misdiagnosis and lead-time bias in the pre-MRI era.

Pitfalls in Prostate Cancer Magnetic Resonance Imaging

Image-guided prostate biopsies are changing the outlook of prostate cancer (PCa) diagnosis, with the degree of suspicion on multiparametric magnetic resonance imaging (mp-MRI) being a strong predictor of targeted biopsy outcome. It is important not only to detect these suspicious lesions but also to be aware of the potential pitfalls in mp-MRI prostate imaging. The aim of this pictorial essay is to show a wide spectrum of representative cases, which are frequently misdiagnosed as PIRADS ⅘ while reporting mp-MRI of the prostate. We provide some valuable recommendations to avoid these fallacies and improve mp-MRI of prostate evaluation.

The predictive factor for pathological downgrading after prostatectomy in patients with biopsy Gleason score 4+3 or 4+4 prostate cancer

The proportion of Gleason pattern (GP) 4 prostate cancers at prostate biopsy has a clinically significant impact on risk stratification for patients with prostate cancer. In pathological diagnosis including GP 4, a biopsy Gleason score (GS) of 3+4 has a more favorable prognosis than a GS of 4+3 and 4+4. However, the discrepancy between biopsy and prostatectomy specimens is well known. The current study investigated the clinical parameters and biopsy specimens associated with pathological downgrading after prostatectomy in biopsies with a GS of 4+3 or 4+4 prostate cancer. A total of 302 patients with prostate cancer who underwent robot-assisted radical prostatectomy between August 2013 and May 2019 were retrospectively reviewed. A total of 103 patients had biopsies with GSs of 4+3 and GS 4+4 (unfavorable pathology). The proportion of patients who were downgraded from unfavorable disease to GS ≤3+4 (favorable pathology) in prostatectomy specimens was investigated. Logistic regression analysis was used to explore the association between clinical parameters and downgrading in prostatectomy specimens. A total of 43 patients (41.7%) were downgraded from biopsy GS to prostatectomy GS. The proportions of downgrade in biopsy GS 4+4 and 4+3 were 14.6 and 27.1%, respectively. The percentage of highest GS out of positive biopsy cores and the maximum percentage of cancer involvement within a positive core with the highest GS were lower in the downgrade group than in the no downgrade group (45 vs. 66.7%, P=0.025; 20 vs. 30%, P=0.048, respectively). When performing multivariate logistic regression analysis, the only significant predictor for downgrade was lower percentage of highest GS cores out of positive biopsy cores (odds ratio, 2.469; 95% confidence interval, 1.029-5.925 P=0.043). In conclusion, patients with biopsy GS 4+4 and 4+3 often exhibit a downgrade to GS 3+4 or less in prostatectomy specimens. The lower percentage of highest GS cores out of positive biopsy cores was associated with downgrade.

Multiparametric MRI in the management of prostate cancer: an update-a narrative review

The growing interest in multiparametric MRI is leading to important changes in the diagnostic process of prostate cancer. MRI-targeted biopsy is likely to become a standard for the diagnosis of prostate cancer in the next years. Despite it is well known that MRI has no role as a staging technique, it is clear that multiparametric MRI may be of help in active surveillance protocols. Noteworthy, MRI in active surveillance is not recommended, but a proper understanding of its potential may be of help in achieving the goals of a delayed treatment strategy. Moreover, the development of minimally invasive techniques, like laparoscopic and robotic surgery, has led to greater expectations as regard to the functional outcomes of radical prostatectomy. Multiparametric MRI may play a role in planning surgical strategies, with the aim to provide the highest oncologic outcome with a minimal impact on the quality of life. We maintain that a proper anatomic knowledge of prostate lesions may allow the surgeon to achieve a better result in planning as well as in performing surgery and help the surgeon and the patient engage in a shared decision in planning a more effective strategy for prostate cancer control and treatment. This review highlights the advantages and the limitations of multiparametric MRI in prostate cancer diagnosis, in active surveillance and in planning surgery.