Prostate cancer reporting and staging: needle biopsy and radical prostatectomy specimens

Extra-prostatic extension grading system: correlation with MRI features and integration of capsular enhancement sign for "enhanced" detection of T3a lesions

PURPOSE

This study aims to confirm the diagnostic accuracy of extra-prostatic extension (EPE) grading system and to explore the predictive capabilities of the prostate MRI while considering various MRI features such as lesion location, apparent diffusion coefficient (ADC) values and capsular enhancement sign (CES).

METHODS

Our monocentric study is based on a retrospective analysis of 99 patients who underwent radical prostatectomy from January 2021 to January 2023. The observers reviewed for each lesion, including location (transitional or peripheral zone, anterior or posterior location), capsular contact length, irregular bulging of the capsule, asymmetry of the neurovascular bundle, obliteration of the recto-prostatic angle, macroscopic EPE, ADC value, and CES.

RESULTS

Among 99 patients, 31 patients had EPE. Lesions with EPE have broadercapsule contact (24 mm vs 12 mm) with contact ≥14 mm being the optimal cut-off for EPE discrimination. Among the morphological MRI criteria used to determine the EPE, the one with major sensitivity was shown to be bulging (sen 81%), while macroscopic extension had highest specificity (100%). Univariate analysis showed as significative risk factors for EPE: capsular contact ≥14 mm (P < .001), International Society of Urological Pathology score ≥3 (P = .005), CES (P < .001), bulging (P = .001), neurovascular bundle asymmetry (P < .001) and EPE score ≥2 (P < .001), and in multivariate analysis CES (P = .001) and EPE score ≥2 (P = .004) were significant. The AUC of the EPE score was 0.76, raised to 0.83 when combining it with CES (P = .11).

CONCLUSION

CES in the setting of multiparametric MRI can increase diagnostic accuracy for the prediction of extracapsular disease.

ADVANCES IN KNOWLEDGE

This study highlights the potential of contrast media in prostate cancer local staging.

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.

Value of three-dimensional visualization of preoperative prostatic magnetic resonance imaging based on measurements of anatomical structures in predicting positive surgical margin after radical prostatectomy

Background

Positive surgical margin (PSM) or apical positive surgical margin (APSM) is an established predictive factor of biochemical recurrence or disease progression in prostate cancer (PCa) patients after radical prostatectomy. Since there are limited usable magnetic resonance imaging (MRI)-based models, we sought to explore the role of three-dimensional (3D) visualization for preoperative MRI in the prediction of PSM or APSM.

Methods

From December 2016 to April 2022, 149 consecutive PCa patients who underwent radical prostatectomy were retrospectively selected from the Second Affiliated Hospital of Dalian Medical University. According to the presence of PSM or APSM, patients were divided into a PSM group (n=41) and a without PSM group (n=108) and into an APSM group (n=33) and a without APSM group (n=116). Twenty-one parameters, including prostate apical shape, PCa distance to the membranous urethra, and pubic angle, were measured on 3D visualization of MRI. The development of the nomogram models was built by the findings of multivariate logistic regression analysis for significant factors.

Results

To predict the probability of PSM, a longer PCa distance to the membranous urethra (OR=0.136, p=0.019) and the distance from the anterior peritoneum to the anterior border of the coccyx (work space AP, OR=0.240, p=0.030) were independent protective factors, while a type 3 prostate apical shape (OR=8.262, p=0.025) and larger pubic angle 2 (OR=5.303, p=0.029) were identified as independent risk factors. The nomogram model presented an area under the curve (AUC) of the receiver operating characteristic curve (ROC) of PSM of 0.777. In evaluating the incidence of APSM, we found that the distance to the membranous urethra (OR=0.135, p=0.014) was associated with a low risk of APSM, while larger pubic angle 1 (OR=4.666, p=0.043) was connected to a higher risk of APSM. The nomogram model showed that the AUC of APSM was 0.755.

Conclusion

As 3D visualization for preoperative MRI showed good performance in predicting PSM or APSM, the tool might be potentially valuable, which also needs to be validated by multicenter, large-scale, prospective studies.

Measuring cancer burden in prostatic needle core biopsies: simplified assessments outperform complex measurements in assessing outcome: evidence to assist pathologist efficiency and minimize datasets

AIMS

The optimal method of measuring cancer extent in prostate cancer (PCa) biopsies is unknown.

METHODS AND RESULTS

Nine hundred eighty-one men with clinically localised PCa managed conservatively were reviewed with follow up. The number of positive cores (NPC), the Maximum Cancer Length in a core (MCL), Total Cancer Length (TCL), and percentage of positive cores (%+cores) was calculated and univariate and multivariate analysis performed using prostate-specific antigen (PSA), T-stage, and Gleason score. The presence of stromal gaps (SG) was recorded. Univariate models were run where SG made a difference to the MCL. All variables showed significant association with PCa death in univariate models. In multivariate models, incorporating PSA, T-stage, and Gleason score, only %+cores was a significant predictor of outcome, with a 10% increase in %+cores resulting in a hazard ratio (HR) of 1.07 (likelihood-ratio test P > Χ2  = 0.01). There were 120 patients where SG made a difference to the MCL and a total of 20 events in this group. Including SG, on univariate analysis the median MCL was 10 mm and HR was 1.16 (P = 0.007), not including SG, the median MCL was 6 mm and HR was 1.23 (P = 6.3 × 10-4 ). Inclusion or exclusion of SG made no significant difference to TCL as a predictor of outcome.

CONCLUSION

Cancer extent is a strong predictor of PCa death but only %+cores added to the multivariate model. Expressed as a fraction of NPC/total number of cores, this is the simplest method of assessment, which we favour over more complicated methods in nontargeted biopsies.

A Nomogram for Predicting Prostate Cancer with Lymph Node Involvement in Robot-Assisted Radical Prostatectomy Era: A Retrospective Multicenter Cohort Study in Japan (The MSUG94 Group)

Background: To create a nomogram for predicting prostate cancer (PCa) with lymph node involvement (LNI) in the robot-assisted radical prostatectomy (RARP) era. Methods: A retrospective multicenter cohort study was conducted on 3195 patients with PCa who underwent RARP at nine institutions in Japan between September 2012 and August 2021. A multivariable logistic regression model was used to identify factors strongly associated with LNI. The Bootstrap-area under the curve (AUC) was calculated to assess the internal validity of the prediction model. Results: A total of 1855 patients were enrolled in this study. Overall, 93 patients (5.0%) had LNI. On multivariable analyses, initial prostate-specific antigen, number of cancer-positive and-negative biopsy cores, biopsy Gleason grade, and clinical T stage were independent predictors of PCa with LNI. The nomogram predicting PCa with LNI has been demonstrated (AUC 84%). Using a nomogram cut-off of 6%, 492 of 1855 patients (26.5%) would avoid unnecessary pelvic lymph node dissection, and PCa with LNI would be missed in two patients (0.1%). The sensitivity, specificity, and negative predictive values associated with a cutoff of 6% were 74%, 80%, and 99.6%, respectively. Conclusions: We developed a clinically applicable nomogram for predicting the probability of patients with PCa with LNI.

Head-to-head comparison of biparametric versus multiparametric MRI of the prostate before robot-assisted transperineal fusion prostate biopsy

Purpose

Prostate biparametric magnetic resonance imaging (bpMRI) including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) might be an alternative to multiparametric MRI (mpMRI, including dynamic contrast imaging, DCE) to detect and guide targeted biopsy in patients with suspected prostate cancer (PCa). However, there is no upgrading peripheral zone PI-RADS 3 to PI-RADS 4 without DCE in bpMRI. The aim of this study was to evaluate bpMRI against mpMRI in biopsy-naïve men with elevated prostate-specific antigen (PSA) scheduled for robot-assisted-transperineal fusion-prostate biopsy (RA-TB).

Methods

Retrospective single-center-study of 563 biopsy-naïve men (from 01/2015 to 09/2018, mean PSA 9.7 ± 6.5 ng/mL) with PI-RADSv2.1 conform mpMRI at 3 T before RA-TB. Clinically significant prostate cancer (csPCa) was defined as ISUP grade ≥ 2 in any core. Two experienced readers independently evaluated images according to PI-RADSv2.1 criteria (separate readings for bpMRI and mpMRI sequences, 6-month interval). Reference standard was histology from RA-TB.

Results

PI-RADS 2 was scored in 5.1% of cases (3.4% cancer/3.4% csPCa), PI-RADS 3 in 16.9% (32.6%/3.2%), PI-RADS 4 in 57.6% (66.1%/58.3%) and PI-RADS 5 in 20.4% of cases (79.1%/74.8%). For mpMRI/bpMRI test comparison, sensitivity was 99.0%/97.1% (p < 0.001), specificity 47.5%/61.2% (p < 0.001), PPV 69.5%/75.1% (p < 0.001) and NPV 97.6%/94.6% (n.s.). csPCa was considered gold standard. 35 cases without cancer were upgraded to PI-RADS 4 (mpMRI) and six PI-RADS 3 cases with csPCa were not upgraded (bpMRI).

Conclusion

In patients planned for RA-TB with elevated PSA and clinical suspicion for PCa, specificity was higher in bpMRI vs. mpMRI, which could solve constrains regarding time and contrast agent.

Detection of perineural invasion in prostate needle biopsies with deep neural networks

The presence of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI are, however, labor intensive. To aid pathologists in this task, we developed an artificial intelligence (AI) algorithm based on deep neural networks. We collected, digitized, and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7406 men who underwent biopsy in a screening trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n = 8318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build the AI algorithm, while 20% were used to evaluate its performance. For detecting PNI in prostate biopsy cores, the AI had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1652 PNI negative cores in the independent test set. For a pre-specified operating point, this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. The concordance of the AI with pathologists, measured by mean pairwise Cohen's kappa (0.74), was comparable to inter-pathologist concordance (0.68 to 0.75). The proposed algorithm detects PNI in prostate biopsies with acceptable performance. This could aid pathologists by reducing the number of biopsies that need to be assessed for PNI and by highlighting regions of diagnostic interest.

The Cooked Meat Carcinogen 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine Hair Dosimeter, DNA Adductomics Discovery, and Associations with Prostate Cancer Pathology Biomarkers

Well-done cooked red meat consumption is linked to aggressive prostate cancer (PC) risk. Identifying mutation-inducing DNA adducts in the prostate genome can advance our understanding of chemicals in meat that may contribute to PC. 2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), a heterocyclic aromatic amine (HAA) formed in cooked meat, is a potential human prostate carcinogen. PhIP was measured in the hair of PC patients undergoing prostatectomy, bladder cancer patients under treatment for cystoprostatectomy, and patients treated for benign prostatic hyperplasia (BPH). PhIP hair levels were above the quantification limit in 123 of 205 subjects. When dichotomizing prostate pathology biomarkers, the geometric mean PhIP hair levels were higher in patients with intermediate and elevated-risk prostate-specific antigen values than lower-risk values <4 ng/mL (p = 0.03). PhIP hair levels were also higher in patients with intermediate and high-risk Gleason scores ≥7 compared to lower-risk Gleason score 6 and BPH patients (p = 0.02). PC patients undergoing prostatectomy had higher PhIP hair levels than cystoprostatectomy or BPH patients (p = 0.02). PhIP-DNA adducts were detected in 9.4% of the patients assayed; however, DNA adducts of other carcinogenic HAAs, and benzo[a]pyrene formed in cooked meat, were not detected. Prostate specimens were also screened for 10 oxidative stress-associated lipid peroxidation (LPO) DNA adducts. Acrolein 1,N²-propano-2'-deoxyguanosine adducts were detected in 54.5% of the patients; other LPO adducts were infrequently detected. Acrolein adducts were not associated with prostate pathology biomarkers, although DNA adductomic profiles differed between PC patients with low and high-grade Gleason scores. Many DNA adducts are of unknown origin; however, dG adducts of formaldehyde and a series of purported 4-hydroxy-2-alkenals were detected at higher abundance in a subset of patients with elevated Gleason scores. The PhIP hair biomarker and DNA adductomics data support the paradigm of well-done cooked meat and oxidative stress in aggressive PC risk.

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).

Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.

Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.

Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.

Clinicopathological Analysis of the ISUP Grade Group And Other Parameters in Prostate Cancer: Elucidation of Mutual Impact of the Various Parameters

Background

Prostate cancer has become increasingly common worldwide. Although Grade group (GG) is widely accepted as an indicator of prostate cancer grade, there are malignancies that cannot be defined by GG alone. Moreover, the relationship between GG and other parameters remains unclear. Herein, we aimed to explore the biological characteristics of prostate cancer.

Methods

This study included 299 radical prostatectomy cases. The Chi-square test and analysis of variance were used to analyze the association of GG with binary and continuous variables. We then conducted morphological analyses. Multivariate analyses were performed to extract the data on risk factors for biochemical recurrence (BCR) and lymph node metastasis.

Results

The lymphatic, venous, perineural, and seminal vesicle invasion rates were 37/299 (12.4%), 25/299 (8.4%), 280/299 (93.6%), and 23/299 (7.7%), respectively. The extraprostatic extension (EPE), positive surgical margin, tertiary Gleason pattern 5, intraductal carcinoma of the prostate gland, and lymph node metastasis rates were 89/299 (29.8%), 106/299 (35.5%), 33/260 (12.7%), 56/299 (18.7%), and 23/299 (7.7%), respectively. As GG increased, various parameters became easier to visualize; however, there were differences between the parameters. Postoperative BCR was observed in 31/242 (12.8%) cases without preoperative hormone therapy; GG2, GG3, GG4, and GG5 accounted for 4, 7, 7, and 13 cases, respectively. Multivariate analyses revealed that GG and tumor diameter were significant risk factors for early BCR, whereas lymphatic invasion, EPE, and seminal vesicle invasion were significant risk factors for lymph node metastasis. For BCR, the odds ratios (ORs) for GG and tumor diameter were 2.253 (95% confidence interval (CI]): 1.297–3.912; P=0.004) and 1.074 (95% CI: 1.011–1.142; P=0.022), respectively. For lymph node metastasis, ORs for the presence of lymphatic invasion, EPE, and seminal vesicle invasion were 7.425 (95% CI: 1.688–22.583; P=0.004), 4.391 (95% CI: 1.037–18.589; P=0.044), and 5.755 (95% CI: 1.308–25.316; P=0.021), respectively.

Conclusions

We summarized various parameters correlating with each GG. Through multivariate analyses, we established the independent risk factors for early BCR and lymph node metastasis. In addition to GG, other important indices of malignancy were determined and weighted to provide a basis for future investigations.

In-bore MRI-guided prostate biopsy in a patient group with PI-RADS 4 and 5 targets: A single center experience

PURPOSE

To determine the diagnostic yield of magnetic resonance imaging (MRI) guided in-bore biopsy in patients with high likelihood multiparametric MRI (mpMRI) findings, regarding overall and clinically significant prostate cancer (csPCa) detection rates and concordance of biopsy and radical prostatectomy (RP) Gleason scores (GS).

METHODS

This retrospective study consisted of 277 Prostate Imaging Reporting and Data System (PI-RADS) assessment category 4 and 5 targets in 246 patients (mean age, 65.7 years; median prostate specific antigen value, 7.75 ng/mL) who had undergone in-bore biopsy at our institution between 2012 and 2020. Eighty-one patients who underwent RP were eligible for the concordance analysis of biopsy and RP specimen GS.

RESULTS

Overall PCa detection rates were 80.5 % per patient (198/246) and 78 % per target (216/277) and 83.5 % and 67.4 % in primary (biopsy naive) and secondary (at least one negative prior biopsy) settings. csPCa was found in 63 % overall, 66 % of patients (132/200) in the primary, and 50 % of patients (23/46) in the secondary biopsy settings (p < 0.001). The prostate cancer detection rate was 68 % and 92 % in PI-RADS 4 and 5, respectively (p < 0.001). In the radical prostatectomy subcohort, 27.2 % of patients were upgraded, 8.6 % of patients were downgraded from needle biopsy. Significant complications occurred in 1.2 % of patients.

CONCLUSIONS

MRI-guided in-bore prostate biopsy has a high detection rate of csPCa in primary and secondary biopsy cohorts. Biopsy results were satisfactory in terms of the number of positive cores, cancer percentage in positive cores, and concordance of GS in needle biopsy and RP specimen.

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer

OBJECTIVE

To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.

PATIENTS AND METHODS

Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status.

RESULTS

With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P < 0.001) proved to be independent predictors for LNM.

CONCLUSION

In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN)

BACKGROUND

Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application.

METHODS

PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN loss was quantified per cancer cell and per tissue microarray core. Thresholds for identifying clinically relevant PTEN loss were determined using log-rank statistics in the training cohort. Univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) analyses on various subpopulations were performed to assess biochemical recurrence-free survival (BRFS) and were independently validated. All statistical tests were two-sided.

RESULTS

PTEN loss in more than 65% cancer cells was most clinically relevant and had statistically significant association with reduced BRFS in training (hazard ratio [HR] = 2.48, 95% confidence interval [CI] = 1.59 to 3.87; P < .001) and validation cohorts (HR = 4.22, 95% CI = 2.01 to 8.83; P < .001). The qPTEN scoring method identified patients who recurred within 5.4 years after surgery (P < .001). In men with favorable risk of biochemical recurrence (Cancer of the Prostate Risk Assessment - Postsurgical scores <5 and no adverse pathological features), qPTEN identified a subset of patients with shorter BRFS (HR = 5.52, 95% CI = 2.36 to 12.90; P < .001) who may be considered for intensified monitoring and/or adjuvant therapy.

CONCLUSIONS

Compared with previous qualitative approaches, qPTEN improves risk stratification of postradical prostatectomy patients and may be considered as a complementary tool to guide disease management after surgery.

Apparent Diffusion Coefficient and Other Preoperative Magnetic Resonance Imaging Features for the Prediction of Positive Surgical Margins in Prostate Cancer Patients Undergoing Radical Prostatectomy

PURPOSE

To investigate the use of apparent diffusion coefficient (ADC) values and other MRI features for predicting positive surgical margins (PSMs) in patients undergoing radical prostatectomy.

MATERIALS AND METHODS

We retrospectively identified 400 consecutive patients who underwent surgery for prostate cancer between January 2015 and June 2016. ADC values of the index lesion and other preoperative magnetic resonance imaging features, including tumor site, laterality, level, Prostate Imaging Reporting and Data System category, European Society of Urogenital Radiology extracapsular extension score, and prostate volume, were assessed. Univariate and multivariable logistic regression were performed. Performance in predicting the occurrence of PSMs was measured using the area under the curve (AUC). AUC differences were evaluated with the DeLong method. The Youden index was calculated to identify the ADC threshold to best discriminate patients with PSMs.

RESULTS

Of the 400 patients, 105 (26.2%) had PSMs after radical prostatectomy. ADC values, Prostate Imaging Reporting and Data System category, extracapsular extension score, tumor site, and laterality were significantly associated with PSMs (P < .001) in univariate analysis. The AUC of the predictive model based on ADC alone was 68.2% (95% confidence interval, 62.2-74.2%) and did not significantly differ from the best multivariable predictive model which combined laterality, and site with ADC to attain an AUC of 70.0% (95% confidence interval, 64.2-75.8%; DeLong P = .318). The ADC threshold that maximized the Youden index was 960.3 µm²/s.

CONCLUSION

ADC values and preoperative magnetic resonance imaging features can help estimate the risk of PSMs after radical prostatectomy.

Oxidative stress and redox signaling in CRPC progression: therapeutic potential of clinically-tested Nrf2-activators

Androgen deprivation therapy (ADT) is the mainstay regimen in patients with androgen-dependent prostate cancer (PCa). However, the selection of androgen-independent cancer cells leads to castrate resistant prostate cancer (CRPC). The aggressive phenotype of CRPC cells underscores the need to elucidate mechanisms and therapeutic strategies to suppress CRPC outgrowth. Despite ADT, the activation of androgen receptor (AR) transcription factor continues via crosstalk with parallel signaling pathways. Understanding of how these signaling cascades are initiated and amplified post-ADT is lacking. Hormone deprivation can increase oxidative stress and the resultant reactive oxygen species (ROS) may activate both AR and non-AR signaling. Moreover, ROS-induced inflammatory cytokines may further amplify these redox signaling pathways to augment AR function. However, clinical trials using ROS quenching small molecule antioxidants have not suppressed CRPC progression, suggesting that more potent and persistent suppression of redox signaling in CRPC cells will be needed. The transcription factor Nrf2 increases the expression of numerous antioxidant enzymes and downregulates the function of inflammatory transcription factors, e.g., nuclear factor kappa B. We documented that Nrf2 overexpression can suppress AR-mediated transcription in CRPC cell lines. Furthermore, two Nrf2 activating agents, sulforaphane (a phytochemical) and bardoxolone-methyl (a drug in clinical trial) suppress AR levels and sensitize CRPC cells to anti-androgens. These observations implicate the benefits of potent Nrf2-activators to suppress the lethal signaling cascades that lead to CRPC outgrowth. This review article will address the redox signaling networks that augment AR signaling during PCa progression to CRPC, and the possible utility of Nrf2-activating agents as an adjunct to ADT.

Cohesive cancer invasion of the biophysical barrier of smooth muscle

Smooth muscle is found around organs in the digestive, respiratory, and reproductive tracts. Cancers arising in the bladder, prostate, stomach, colon, and other sites progress from low-risk disease to high-risk, lethal metastatic disease characterized by tumor invasion into, within, and through the biophysical barrier of smooth muscle. We consider here the unique biophysical properties of smooth muscle and how cohesive clusters of tumor use mechanosensing cell-cell and cell-ECM (extracellular matrix) adhesion receptors to move through a structured muscle and withstand the biophysical forces to reach distant sites. Understanding integrated mechanosensing features within tumor cluster and smooth muscle and potential triggers within adjacent adipose tissue, such as the unique damage-associated molecular pattern protein (DAMP), eNAMPT (extracellular nicotinamide phosphoribosyltransferase), or visfatin, offers an opportunity to prevent the first steps of invasion and metastasis through the structured muscle.

A Panel of Urinary Volatile Biomarkers for Differential Diagnosis of Prostate Cancer from Other Urological Cancers

Our group recently developed a urinary 6-biomarker panel for the diagnosis of prostate cancer (PCa) which has a higher level of accuracy compared to the serum prostate specific antigen (PSA) test. Herein, urine from an independent cohort of PCa patients and cancer-free controls was analyzed to further validate the discriminative power of that panel. Additionally, urine from patients diagnosed with bladder cancer (BC) and renal cancer (RC) were included to evaluate the site-specificity of the panel. Results confirmed the ability of the 6-biomarker panel to discriminate PCa patients from controls, but not from other urological cancers. To overcome this limitation, an untargeted approach was performed to unveil discriminant metabolites among the three cancer types. A 10-biomarker panel comprising the original panel plus four new metabolites was established to discriminate PCa from controls, BC, and RC, with 76% sensitivity, 90% specificity, and 92% accuracy. This improved panel also disclosed better accuracy than serum PSA test and provides the basis for a new non-invasive early detection tool for PCa.

Diagnostic Accuracy and Value of Magnetic Resonance Imaging-Ultrasound Fusion Transperineal Targeted and Template Systematic Prostate Biopsy Based on Bi-parametric Magnetic Resonance Imaging

Purpose

The purpose of this study was to investigate the diagnostic value of magnetic resonance imaging (MRI)–ultrasound (US) fusion transperineal targeted biopsy (FTB) and fusion template systematic biopsy (FSB) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) (intermediate/high grade [Gleason score ≥ 3+4]) based on bi-parametric MRI (bpMRI).

Materials and methods

Retrospectively, we analyzed 300 patients with elevated prostate-specific antigen (≥ 4.0 ng/mL) and/or abnormal findings in a digital rectal examination at the Korea University Hospital. All 300 men underwent bpMRI-US fusion transperineal FTB and FSB in the period from April 2017 to March 2019.

Results

PCas were detected in 158 of 300 men (52.7%), and the prevalence of csPCa was 34.0%. CsPCas were detected in 12 of 102 (11.8%) with Prostate Imaging-Reporting and Data System (PI-RADS) 3, 42 of 92 (45.7%) with PI-RADS 4, respectively; and 45 of 62 (72.6%) men with PI-RADS 5, respectively. BpMRI showed a sensitivity of 95.1% and negative predictive value of 89.6% for csPCa. FTB detected additional csPCa in 33 men (12.9%) compared to FSB. Compared to FTB, FSB detected additional csPCa in 10 men (3.9%).

Conclusion

BpMRI-US FTB and FSB improved detection of PCa and csPCa. The accuracy of bi-parametric MRI is comparable with that of multi-parametric MRI. Further, it is rapid, simpler, cheaper, and no side effects of contrast media. Therefore, it is expected that bpMRI-US transperineal FTB and FSB could be a good alternative to conventional US-guided transrectal biopsy, which is the current gold standard.

Prognostic value of perineural invasion in prostate needle biopsies: a population-based study of patients treated by radical prostatectomy

AIMS

Despite being one of the major pathways for the spread of malignant tumours, perineural invasion (PNI) has not conclusively been shown to have an independent prognostic value for prostate cancer. Prostatic biopsy constitutes the major pathology workload in prostate cancer and is the foundation for primary treatment decisions and for this reason we aimed to estimate the prognostic value of PNI in biopsies.

METHODS

We followed 918 men who underwent radical prostatectomy (RP) from the prospective and population based STHLM3 study until biochemical recurrence with a median follow-up of 4.1 years. To strengthen the evidence, we combined the estimates from the largest studies targeting the prognostic value of PNI in the biopsy. We also estimated the OR of advanced stage as radical prostatectomy for PNI positive and negative men.

RESULTS

The estimated prognostic value based on our data suggested an approximately 50% increased risk of biochemical recurrence if PNI was present in the biopsy (p=0.06). Even though not statistically significant on the 5% level, this estimate is consistent with similar studies, and by combining the estimates there is in fact strong evidence in support of an independent prognostic value of PNI in the biopsy (p<0.0001). There was also an independent increased risk of advanced stage at RP for positive men (OR 1.85, p=0.005).

CONCLUSIONS

The evidence supporting a clinically relevant and independent prognostic value of PNI is strong enough to be considered for pathology reporting guidelines.

Characterization and screening of anti-tumor activity of fucoidan from acid-processed hijiki (Hizikia fusiforme)

The aim of this study was to investigate the antiproliferative effects of fucoidan from three regional hijiki (Hizikia fusiforme) samples (Zhejiang-China, Jeju-Korea [JH], and Wando-Korea) in East Asia. Hijiki was processed using 1% citric acid to decrease heavy metal content. The JH sample was separated using diethylaminoethyl-cellulose-ion exchange chromatography to obtain four active fractions (JHCF1-JHCF4) and their monosaccharide composition was detected using high-performance liquid chromatography. The structure of the crude polysaccharides and four fucoidan fractions was analyzed using Fourier-transform infrared spectroscopy. JHCF4 showed the highest fucose and sulfate content and decreased Hep3B cell growth in 48 h with a half-maximal inhibitory concentration of 33.53 ± 2.50 μg/ml, which represented the strongest anticancer activity. Further, nuclear staining with Hoechst 33342 and acridine orange-ethidium bromide staining demonstrated that the anticancer activity of JHCF4 was mediated by apoptosis. Moreover, JHCF4 down-regulated B-cell lymphoma extra-large, while up-regulating Bcl-2-associated X protein, caspase-3, and apoptotic bodies to different degrees in Hep3B cells. JHCF4 induced apoptosis via the generation of reactive oxygen species along with the concurrent loss of mitochondrial membrane potential, indicating the potential role of the mitochondria-mediated pathway. Therefore, these results indicate that JHCF4 exhibited antiproliferative effects on the investigated cancer cell lines.

Essential Updates in Grading, Morphotyping, Reporting, and Staging of Prostate Carcinoma for General Surgical Pathologists

CONTEXT.—

Within this decade, several important updates in prostate cancer have been presented through expert international consensus conferences and influential publications of tumor classification and staging.

OBJECTIVE.—

To present key updates in prostate carcinoma.

DATA SOURCES.—

The study comprised a review of literature and our experience from routine and consultation practices.

CONCLUSIONS.—

Grade groups, a compression of the Gleason system into clinically meaningful groups relevant in this era of active surveillance and multidisciplinary care management for prostate cancer, have been introduced. Refinements in the Gleason patterns notably result in the contemporarily defined Gleason score 6 cancers having a virtually indolent behavior. Grading of tertiary and minor higher-grade patterns in radical prostatectomy has been clarified. A new classification for prostatic neuroendocrine tumors has been promulgated, and intraductal, microcystic, and pleomorphic giant cell carcinomas have been officially recognized. Reporting the percentage of Gleason pattern 4 in Gleason score 7 cancers has been recommended, and data on the enhanced risk for worse prognosis of cribriform pattern are emerging. In reporting biopsies for active surveillance criteria-based protocols, we outline approaches in special situations, including variances in sampling or submission. The 8th American Joint Commission on Cancer TNM staging for prostate cancer has eliminated pT2 subcategorization and stresses the importance of nonanatomic factors in stage groupings and outcome prediction. As the clinical and pathology practices for prostate cancer continue to evolve, it is of utmost importance that surgical pathologists become fully aware of the new changes and challenges that impact their evaluation of prostatic specimens.