Validation of the Kattan Nomogram for Prostate Cancer Recurrence After Radical Prostatectomy

MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer

Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.

Prostate-derived circulating microRNAs add prognostic value to prostate cancer risk calculators

Prostate cancer is the second leading cause of malignancy-related deaths among American men. Active surveillance is a safe option for many men with less aggressive disease, yet definitively determining low-risk cancer is challenging with biopsy alone. Herein, we sought to identify prostate-derived microRNAs in patient sera and serum extracellular vesicles, and determine if those microRNAs improve upon the current clinical risk calculators for prostate cancer prognosis before and after biopsy. Prostate-derived intracellular and extracellular vesicle-contained microRNAs were identified by small RNA sequencing of prostate cancer patient explants and primary cells. Abundant microRNAs were included in a custom microRNA PCR panel that was queried in whole serum and serum extracellular vesicles from a diverse cohort of men diagnosed with prostate cancer. The levels of these circulating microRNAs significantly differed between indolent and aggressive disease and improved the area under the curve for pretreatment nomograms of prostate cancer disease risk. The microRNAs within the extracellular vesicles had improved prognostic value compared to the microRNAs in the whole serum. In summary, quantifying microRNAs circulating in extracellular vesicles is a clinically feasible assay that may provide additional information for assessing prostate cancer risk stratification.

Establishment and Validation of a Novel Prediction Model for Early Natural Biochemical Recurrence After Radical Prostatectomy Based on Post-Operative PSA at Sixth Week

Objective

Based on post-operative PSA at 6th week (PSA_6w) after radical prostatectomy to establish an optimal model for predicting natural biochemical recurrence (BCR).

Methods

A total of 742 patients with post-operative PSA_6w from PC-follow database, between January 2003 and October 2022, were included. All the patients had not received any hormone therapy and radiotherapy before operation and BCR. Of these patients, 588 cases operated by one surgeon were enrolled for modelling and another 154 cases operated by other surgeons were for external validation. After screened by Cox regression, the post-operative PSA_6w, pathological stage, Gleason Grade and positive surgical margins were adopted for modelling. The R software was used to plot the nomogram of the prediction model for BCR. C-index and calibration curve were calculated to evaluate the new model. Finally, integrated discrimination improvement was adopted to evaluate the prediction performances of the new nomogram model and the classical Kattan nomogram.

Results

The C-index of the new model was 0.871 (95% CI: 0.830-0.912). The calibration curve of the new model demonstrated superior consistency between the predicted and actual value. The C-index of the external validation group was 0.850 (95% CI: 0.742-0.958), which demonstrated perfect universality. The integrated discrimination improvement showed a 12.61% improvement in prediction performance over that of the classical Kattan nomogram (P < 0.01). Based on the new nomogram, patients were divided to high and low BCR group with a 3 year BCR-free cutoff probability as 74.72%. Low-risk patients, accounting for 77.89% of the patients, have no need to follow up frequently with a false-negative rate only 5.24%, which will save medical resources to a large extent.

Conclusion

Post-operative PSA6w is a sensitive risk biomarker for early natural BCR. The new nomogram model could predict BCR probability with a higher accuracy and will further simplify the clinical follow-up strategies.

m6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

Simple Summary

Recurrence and metastatic progression always lead to dismal outcomes in prostate cancer (PCa). There is no reliable biomarker for the prediction of recurrence and metastasis other than the Prostate Cancer Antigen (PCA). N6-methyladenosine (m⁶A) is the most common post-transcriptional mRNA modification and is regulated by m⁶A regulators dynamically. Since m⁶A modification is associated with cancer development and outgrowth, we performed a consensus clustering on PCa with regard to the gene expression of all m⁶A regulators. We identified three subtypes of Pca with distinct m⁶A expression patterns and enriched biological pathways. We also established an m⁶A score for metastasis prediction based on our clustering, which is potentially a predictive biomarker for Pca metastasis.

Abstract

Prostate cancer (PCa) is one of the most common cancers in men. Usually, most PCas at initial diagnosis are localized and hormone-dependent, and grow slowly. Patients with localized PCas have a nearly 100% 5-year survival rate; however, the 5-year survival rate of metastatic or progressive PCa is still dismal. N6-methyladenosine (m⁶A) is the most common post-transcriptional mRNA modification and is dynamically regulated by m⁶A regulators. A few studies have shown that the abnormal expression of m⁶A regulators is significantly associated with cancer progression and immune cell infiltration, but the roles of these regulators in PCa remain unclear. Here, we examined the expression profiles and methylation levels of 21 m⁶A regulators across the Cancer Genome Atlas (TCGA), 495 PCas by consensus clustering, and correlated the expression of m⁶A regulators with PCa progression and immune cell infiltration. Consensus clustering was applied for subtyping Pca samples into clusters based on the expression profiles of m⁶A regulators. Each subtype’s signature genes were obtained by a pairwise differential expression analysis. Featured pathways of m⁶A subtypes were predicted by Gene Ontology. The m⁶A score was developed to predict m⁶A activation. The association of the m⁶A score with patients’ survival, metastasis and immune cell infiltration was also investigated. We identified three distinct clusters in PCa based on the expression profiles of 21 m⁶A regulators by consensus clustering. The differential expression and pathway analyses on the three clusters uncovered the m⁶A regulators involved in metabolic processes and immune responses in PCa. Moreover, we developed an m⁶A score to evaluate the m⁶A regulator activation for PCa. The m⁶A score is significantly associated with Gleason scores and metastasis in PCa. The predictive capacity of the m⁶A score on PCa metastasis was also validated in another independent cohort with an area under the curve of 89.5%. Hence, our study revealed the critical role of m⁶A regulators in PCa progression and the m⁶A score is a promising predictive biomarker for PCa metastasis.

Incorporating artificial intelligence in urology: Supervised machine learning algorithms demonstrate comparative advantage over nomograms in predicting biochemical recurrence after prostatectomy

OBJECTIVE

After radical prostatectomy (RP), one-third of patients will experience biochemical recurrence (BCR), which is associated with subsequent metastasis and cancer-specific mortality. We employed machine learning (ML) algorithms to predict BCR after RP, and compare them with traditional regression models and nomograms.

METHODS

Utilizing a prospective Uro-oncology registry, 18 clinicopathological parameters of 1130 consecutive patients who underwent RP (2009-2018) were recorded, yielding over 20,000 data points for analysis. The data set was split into a 70:30 ratio for training and validation. Three ML models: Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) were studied, and compared with traditional regression models and nomograms (Kattan, CAPSURE, John Hopkins [JHH]) to predict BCR at 1, 3, and 5 years.

RESULTS

Over a median follow-up of 70.0 months, 176 (15.6%) developed BCR, at a median time of 16.0 months (interquartile range [IQR]: 11.0-26.0). Multivariate analyses demonstrated strongest association of BCR with prostate-specific antigen (PSA) (p: 0.015), positive surgical margins (p < 0.001), extraprostatic extension (p: 0.002), seminal vesicle invasion (p: 0.004), and grade group (p < 0.001). The 3 ML models demonstrated good prediction of BCR at 1, 3, and 5 years, with the area under curves (AUC) of NB at 0.894, 0.876, and 0.894, RF at 0.846, 0.875, and 0.888, and SVM at 0.835, 0.850, and 0.855, respectively. All models demonstrated (1) robust accuracy (>0.82), (2) good calibration with minimal overfitting, (3) longitudinal consistency across the three time points, and (4) inter-model validity. The ML models were comparable to traditional regression analyses (AUC: 0.797, 0.848, and 0.862) and outperformed the three nomograms: Kattan (AUC: 0.815, 0.798, and 0.799), JHH (AUC: 0.820, 0.757, and 0.750) and CAPSURE nomograms (AUC: 0.706, 0.720, and 0.749) (p < 0.001).

CONCLUSION

Supervised ML algorithms can deliver accurate performances and outperform nomograms in predicting BCR after RP. This may facilitate tailored care provisions by identifying high-risk patients who will benefit from multimodal therapy.

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.

Validation of an MRI-based prostate cancer prebiopsy Gleason score predictive nomogram

Background:

Gleason score grading is a cornerstone of risk stratification and management of patients with prostate cancer (PCa). In this work, we derive and validate a nomogram that uses prostate multiparametric magnetic resonance imaging (MP-MRI) and clinical patient characteristics to predict biopsy Gleason scores (bGS).

Materials and methods:

A predictive nomogram was derived from 143 men who underwent MP-MRI prior to any prostate biopsy and then validated on an independent cohort of 235 men from a different institution who underwent MP-MRI for PCa workup. Screen positive lesions were defined as lesions positive on T2W and DWI sequences on MP-MRI. Prostate specific antigen (PSA) density, number of screen positive lesions, and MRI suspicion were associated with PCa Gleason score on biopsy and were used to generate a predictive nomogram. The independent cohort was tested on the nomogram and the most likely bGS was noted.

Results:

The mean PSA in the validation cohort was 9.25ng/mL versus 6.8ng/mL in the original cohort (p = 0.001). The distribution of Gleason scores between the 2 cohorts were not significantly different (p = 0.7). In the original cohort of men, the most probable nomogram generated Gleason score agreed with actual pathologic bGS findings in 61% of the men. In the validation cohort, the most likely nomogram predicted bGS agreed with actual pathologic bGS 51% of the time. The nomogram correctly identified any PCa versus non-PCa 63% of the time and clinically significant (Gleason score ≥ 7) PCa 69% of the time. The negative predictive value for clinically significant PCa using this prebiopsy nomogram was 74% in the validation group.

Conclusions:

A preintervention nomogram based on PSA and MRI findings can help narrow down the likely pathologic finding on biopsy. Validation of the nomogram demonstrated a significant ability to correctly identify the most likely bGS. This feasibility study demonstrates the potential of a prebiopsy prediction of bGS and based on the high negative predictive value, identification of men who may not need biopsies, which could impact future risk stratification for PCa.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Advancement and properties of circular RNAs in prostate cancer: An emerging and compelling frontier for discovering

Prostate cancer (PC) is the most common carcinoma among men worldwide which results in 26% of leading causes of cancer-related death. However, the ideal and effective molecular marker remains elusive. CircRNA, initially observed in plant-infected viruses and Sendai virus in 1979, is generated from pre-mRNA back-splicing and comes in to play by adequate expression. The differential expression in prostate tissues compared with the control reveals the promising capacity in modulating processes including carcinogenesis and metastasis. However, the biological mechanisms of regulatory network in PC needs to systemically concluded. In this review, we enlightened the comprehensive studies on the definite mechanisms of circRNAs affecting tumor progression and metastasis. What's more, we validated the potential clinical application of circRNAs serving as diagnostic and prognostic biomarker. The discussion and analysis in circRNAs will broaden our knowledge of the pathogenesis of PC and further optimize the current therapies against different condition.

Prostate Cancer, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology

The NCCN Guidelines for Prostate Cancer include recommendations regarding diagnosis, risk stratification and workup, treatment options for localized disease, and management of recurrent and advanced disease for clinicians who treat patients with prostate cancer. The portions of the guidelines included herein focus on the roles of germline and somatic genetic testing, risk stratification with nomograms and tumor multigene molecular testing, androgen deprivation therapy, secondary hormonal therapy, chemotherapy, and immunotherapy in patients with prostate cancer.

Performance of prostate multiparametric MRI for prediction of prostate cancer extra-prostatic extension according to NCCN risk categories: implication for surgical planning

BACKGROUND

Prediction of extra-prostatic extension (EPE) in men undergoing radical prostatectomy (RP) is of utmost importance. Great variability in the performance of multiparametric magnetic resonance imaging (mpMRI) has been reported for prediction of EPE. The present study aimed to determine the diagnostic performance of mpMRI for predicting EPE in different National Comprehensive Cancer Network (NCCN) risk categories.

METHODS

Overall 664 patients who underwent radical prostatectomy with a staging mpMRI were enrolled in this single-center, retrospective study. Patients with mpMRI report non-compliant with PI-RADSv2.0, were excluded. Patients were stratified according to NCCN criteria: very low/low (VLR-LR) to High Risk (HR) in order to assess final pathology EPE rates (focal and established). Sensitivity, specificity, positive and negative predictive values of staging mpMRI were computed in each group. Univariable and multivariable analysis were used to evaluate predictors of positive surgical margins.

RESULTS

Pathological evaluation demonstrated established and focal EPE in 60 (9%) and 106 (16%) patients, respectively, while mpMRI suspicion for EPE was present in 180 (27%) patients. Age, preoperative PSA, PSA density, number of positive cores, NCCN groups, prostate volume, mpMRI suspicion for EPE, PIRADSv2.0 and lesion size differed significantly between the patients with any EPE and without EPE (all P≤0.05). The sensitivity of mpMRI in detecting any EPE varied from 12% (95% CI: 0.6-53%) in VLR-LR to 83% (66-93%) in HR while the corresponding values for the specificity were 92% (85-96%) and 63% (45-78%), respectively. Patients with false-negative mpMRI EPE prediction were more likely to have positive surgical margins in univariable (OR: 2.14; CI: 1.18, 3.87) as well as multivariable analysis adjusting for NCCN risk categories (OR: 1.97; CI: 1.08, 3.60).

CONCLUSIONS

The performance of mpMRI for prediction of EPE varies greatly between different NCCN risk categories with a low positive predicting value in patients at low to favorable intermediate risk and a low negative predictive value in patients at Unfavorable intermediate to high risk PCa. Given that mpMRI EPE misdiagnosis could have a negative impact on oncological and functional outcomes, NCCN risk categories should be considered when interpreting mpMRI findings in PCa patients.

Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score

BACKGROUND

Biochemical recurrence (BCR) affects a significant proportion of patients who undergo robotic-assisted laparoscopic prostatectomy (RALP).

PURPOSE

To evaluate the performance of a routine clinical prostate multiparametric magnetic resonance imaging (mpMRI) and Decipher genomic classifier score for prediction of biochemical recurrence in patients who underwent RALP.

STUDY TYPE

Retrospective cohort study.

SUBJECTS

Ninety-one patients who underwent RALP performed by a single surgeon, had mpMRI before RALP, Decipher taken from RALP samples, and prostate specific antigen (PSA) follow-up for >3 years or BCR within 3 years, defined as PSA >0.2 mg/ml. FIELD STRENGTH/SEQUENCE: mpMRI was performed at 27 different institutions using 1.5T (n = 10) or 3T scanners and included T₂ w, diffusion-weighted imaging (DWI), or dynamic contrast-enhanced (DCE) MRI.

ASSESSMENT

All mpMRI studies were reported by one reader using Prostate Imaging Reporting and Data System v. 2.1 (PI-RADsv2.1) without knowledge of other findings. Eighteen (20%) randomly selected cases were re-reported by reader B to evaluate interreader variability.

STATISTICAL TESTS

Univariate and multivariate analysis using greedy feature selection and tournament leave-pair-out cross-validation (TLPOCV) were used to evaluate the performance of various variables for prediction of BCR, which included clinical (three), systematic biopsy (three), surgical (six: RALP Gleason Grade Group [GGG], extracapsular extension, seminal vesicle invasion, intraoperative surgical margins [PSM], final PSM, pTNM), Decipher (two: Decipher score, Decipher risk category), and mpMRI (eight: prostate volume, PSA density, PI-RADv2.1 score, MRI largest lesion size, summed MRI lesions' volume and relative volume [MRI-lesion-percentage], mpMRI ECE, mpMRI seminal vesicle invasion [SVI]) variables. The evaluation metric was the area under the curve (AUC).

RESULTS

Forty-eight (53%) patients developed BCR. The best-performing individual features with TLPOCV AUC of 0.73 (95% confidence interval [CI] 0.64-0.82) were RALP GGG, MRI-lesion-percentage followed by biopsy GGG (0.72, 0.62-0.82), and Decipher score (0.71, 0.60-0.82). The best performance was achieved by feature selection of Decipher+Surgery and MRI + Surgery variables with TLPOCV AUC of 0.82 and 0.81, respectively DATA CONCLUSION: Relative lesion volume measured on a routine clinical mpMRI failed to outperform Decipher score in BCR prediction.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1075-1085.

RNAs as Candidate Diagnostic and Prognostic Markers of Prostate Cancer-From Cell Line Models to Liquid Biopsies

The treatment landscape of prostate cancer has evolved rapidly over the past five years. The explosion in treatment advances has been witnessed in parallel with significant progress in the field of molecular biomarkers. The advent of next-generation sequencing has enabled the molecular profiling of the genomic and transcriptomic architecture of prostate and other cancers. Coupled with this, is a renewed interest in the role of non-coding RNA (ncRNA) in prostate cancer biology. ncRNA consists of several different classes including small non-coding RNA (sncRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). These families are under active investigation, given their essential roles in cancer initiation, development and progression. This review focuses on the evidence for the role of RNAs in prostate cancer, and their use as diagnostic and prognostic markers, and targets for treatment in this disease.

Prediction models for prostate cancer outcomes: what is the state of the art in 2017?

PURPOSE OF REVIEW

Prostate cancer (PCa) remains a significant public health burden, with multiple points for decision-making at all stages of the disease. Given the amount and variety of data that may influence disease management, prediction models have been published to assist clinicians and patients in making decisions about the best next course of action at many disease states. We sought to review the most important studies related to PCa prediction models since 2016 and evaluate their impact upon the evolving field of risk modeling in PCa.

RECENT FINDINGS

There has been a significant amount of work published in the past year concerning risk modeling in PCa at all stages of disease, ranging from screening to predicting survival with metastatic disease. The majority of recent publications focus upon the addition of a new biomarker to prediction models or upon validating previously published prediction models. In particular, MRI has been the topic of a number of more recent studies.

SUMMARY

Prediction modeling in PCa currently compares the area under the receiver operating curve between models with and without the biomarker of interest to predict the outcome of interest in multiple disease states, ranging from diagnosis to prediction of survival with metastatic disease. Future work should provide additional information regarding clinical impact and measures of prediction confidence.

High expression of SLCO2B1 is associated with prostate cancer recurrence after radical prostatectomy

Solute carrier organic anion (SLCO) gene families encode organic anion transport proteins, which are transporters that up-take a number of substrates including androgens. Among them, high expression of SLCO2B1 is known to associate with the resistance to androgen deprivation therapy in prostate cancer (PCa). We hypothesized that high expression of SLCO genes enhances PCa progression by promoting the influx of androgen. Here, we demonstrated the impact of the expression levels of SLCO2B1 on prognosis in localized PCa after radical prostatectomy (RP) utilizing 494 PCa cases in The Cancer Genome Atlas (TCGA). SLCO2B1 high expression group showed significantly worse Disease-free survival (DFS) after RP (p = 0.001). The expression level of SLCO2B1 was significantly higher in advanced characteristics including Gleason Score (GS ≤ 6 vs GS = 7; p = 0.047, GS = 7 vs GS ≥ 8; p = 0.002), pathological primary tumor (pT2 vs pT3/4; p < 0.001), and surgical margin status (positive vs negative; p = 0.013), respectively. There was a significant difference in DFS between these two groups only in GS ≥ 8 patients (p = 0.006). Multivariate analysis demonstrated that only SLCO2B1 expression level was an independent predictor for DFS after RP in GS ≥ 8. SLCO2B1 high expressed tumors in GS ≥ 8 not only enriched epithelial mesenchymal transition (EMT) related gene set, (p = 0.027), as well as Hedgehog (p < 0.001), IL-6/JAK/STAT3 (p < 0.001), and K-ras signaling gene sets (p < 0.001), which are known to promote EMT, but also showed higher expression of EMT related genes, including N-cadherin (p = 0.024), SNAIL (p = 0.001), SLUG (p = 0.001), ZEB-1 (p < 0.001) and Vimentin (p < 0.001). In conclusion, PCa with high expression of SLCO2B1 demonstrated worse DFS, which might be due to accelerated EMT.

Molecular imaging of prostate cancer

Prostate cancer is a common malignancy with various treatments from surveillance, surgery, radiation and chemotherapy. The institution of appropriate, effective treatment relies in part on accurate imaging. Molecular imaging techniques offer an opportunity for increased timely detection of prostate cancer, its recurrence, as well as metastatic disease. Advancements within the field of molecular imaging have been complex with some agents targeting receptors and others acting as metabolic intermediaries. In this article, we provide an overview of the most clinically relevant radiotracers to date based on a combination of the five states model and the National Comprehensive Cancer Network Guidelines.

Whom to Treat: Postdiagnostic Risk Assessment with Gleason Score, Risk Models, and Genomic Classifier

Management of prostate cancer presents unique challenges because of the disease's variable natural history. Accurate risk stratification at the time of diagnosis in clinically localized disease is crucial in providing optimal counseling about management options. To accurately distinguish pathologically indolent tumors from aggressive disease, risk groups are no longer sufficient. Rather, multivariable prognostic models reflecting the complete information known at time of diagnosis offer improved accuracy and interpretability. After diagnosis, further testing with genomic assays or other biomarkers improves risk classification. These postdiagnostic risk assessment tools should not supplant shared decision making, but rather facilitate risk classification and enable more individualized care.

Validation of American Joint Committee on Cancer eighth staging system among prostate cancer patients treated with radical prostatectomy

Background

The objective in this paper was to validate the prognostic performance of the American Joint Committee on Cancer (AJCC) 7th and 8th systems among prostate cancer patients treated with radical prostatectomy.

Methods

The surveillance, epidemiology and end results (SEER) database (2006-2014) was accessed through the SEER*Stat program and AJCC 7th and 8th editions were calculated utilizing T, N and M stages, histological grade group, as well as baseline prostatic-specific antigen (PSA). Cancer-specific and overall survival analyses according to 7th and 8th editions were conducted. Moreover, multivariate analysis was conducted through a Cox proportional hazard model.

Results

A total of 72,999 patients with prostate cancer were identified in the period from 2006 to 2014. Overall survival was assessed according to AJCC 7th and 8th staging systems. The test for trend for overall survival was significant (p < 0.0001) for both staging systems. Concordance index for AJCC 7th system was: 0.791 [standard error of the mean (SE): 0.017; 95% CI: 0.758-0.825]; while concordance index for AJCC 8th system was: 0.840 (SE: 0.015; 95% CI: 0.811-0.869). In a multivariate analysis among patients with M0 disease, lower grade group, N0 stage and pT2 stage were associated with better cancer-specific survival (p < 0.01); while PSA level did not predict cancer-specific survival.

Conclusion

There is a clear improvement in the discriminatory ability for AJCC 8th versus AJCC 7th staging system in the postprostatectomy setting. This may be related to better integration of biological factors into the staging system.