An updated catalog of prostate cancer predictive tools

Sex hormone-binding globulin and its critical role in prostate cancer: A comprehensive review

Prostate cancer (PC) is a common and widespread cancer that affects men globally. A complicated interaction of hormonal variables influences its development. Sex hormone-binding globulin (SHBG) is a crucial element in controlling the availability of sex hormones, especially androgens, which have a notable impact on the development and progression of PC. SHBG controls the levels of free, active androgens in the body, which helps regulate androgen-dependent processes associated with PC. The equilibrium between SHBG and androgens plays a critical role in maintaining the stability of the prostate. When this balance is disrupted, it is associated with the development and advancement of PC. The processes responsible for SHBG's role in PC are complex and have multiple aspects. SHBG primarily binds to androgens, preventing them from interacting with androgen receptors (ARs) in prostate cells. It reduces the activation of androgen signaling pathways essential for tumor development and survival. In addition, SHBG can directly affect prostate cells by interacting with specific receptors on the cell surface. This review thoroughly examines the role of SHBG in PC, including its physiological activities, methods of action, and clinical consequences.

Model-agnostic explanations for survival prediction models

Advanced machine learning methods capable of capturing complex and nonlinear relationships can be used in biomedical research to accurately predict time-to-event outcomes. However, these methods have been criticized as "black boxes" that are not interpretable and thus are difficult to trust in making important clinical decisions. Explainable machine learning proposes the use of model-agnostic explainers that can be applied to predictions from any complex model. These explainers describe how a patient's characteristics are contributing to their prediction, and thus provide insight into how the model is arriving at that prediction. The specific application of these explainers to survival prediction models can be used to obtain explanations for (i) survival predictions at particular follow-up times, and (ii) a patient's overall predicted survival curve. Here, we present a model-agnostic approach for obtaining these explanations from any survival prediction model. We extend the local interpretable model-agnostic explainer framework for classification outcomes to survival prediction models. Using simulated data, we assess the performance of the proposed approaches under various settings. We illustrate application of the new methodology using prostate cancer data.

Autonomous Tumor Signature Extraction Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study

BACKGROUND

Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI.

METHODS

Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer.

RESULTS

The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob.

CONCLUSIONS

The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of Magnetic Resonance Imaging (MRI) sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement (DCE) in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material and prolonging scanning time. More accurate quantitative MRI without DCE and artificial intelligence approaches are needed. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and correlate with the International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasive management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx + SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade ≥ 2 (<2) was judged as CsPCa (CiPCa). Linear and logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R ~ 0.17). Patients assessed with combined MRBx + SysBx and from individual MRI scanners achieved higher R (DR = 0.207+/-0.118) than all patients used in the fits. Conclusions: In the first study, to date, spectral approaches for assessing tumor aggressiveness on SRBP-MRI have been applied and tested and achieved high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA, respectively.

MR-based simplified extraprostatic extension evaluation: comparison of performances of different predictive models

OBJECTIVES

To test reproducibility and predictive value of a simplified score for assessment of extraprostatic tumor extension (sEPE grade).

METHODS

Sixty-five patients (mean age ± SD, 67 years ± 6.3) treated with radical prostatectomy for prostate cancer who underwent 1.5-Tesla multiparametric magnetic resonance imaging (mpMRI) 6 months before surgery were enrolled. sEPE grade was derived from mpMRI metrics: curvilinear contact length > 15 mm (CCL) and capsular bulging/irregularity. The diameter of the index lesion (dIL) was also measured. Evaluations were independently performed by seven radiologists, and inter-reader agreement was tested by weighted Cohen K coefficient. A nested (two levels) Monte Carlo cross-validation was used. The best cut-off value for dIL was selected by means of the Youden J index to classify values into a binary variable termed dIL*. Logistic regression models based on sEPE grade, dIL, and clinical scores were developed to predict pathologic EPE. Results on validation set were assessed by the main metrics of the receiver operating characteristics curve (ROC) and by decision curve analysis (DCA). Based on our findings, we defined and tested an alternative sEPE grade formulation.

RESULTS

Pathologic EPE was found in 31/65 (48%) patients. Average κ_w was 0.65 (95% CI 0.51-0.79), 0.66 (95% CI 0.48-0.84), 0.67 (95% CI 0.50-0.84), and 0.43 (95% CI 0.22-0.63) for sEPE grading, CLL ≥ 15 mm, dIL*, and capsular bulging/irregularity, respectively. The highest diagnostic yield in predicting EPE was obtained by combining both sEPE grade and dIL*(ROC-AUC 0.81).

CONCLUSIONS

sEPE grade is reproducible and when combined with the dIL* accurately predicts extraprostatic tumor extension.

KEY POINTS

• Simple and reproducible mpMRI semi-quantitative scoring system for extraprostatic tumor extension. • sEPE grade accurately predicts extraprostatic tumor extension regardless of reader expertise. • Accurate pre-operative staging and risk stratification for optimized patient management.

Improving the Post-Operative Prediction of BCR-Free Survival Time with mRNA Variables and Machine Learning

Predicting the risk of, and time to biochemical recurrence (BCR) in prostate cancer patients post-operatively is critical in patient treatment decision pathways following surgical intervention. This study aimed to investigate the predictive potential of mRNA information to improve upon reference nomograms and clinical-only models, using a dataset of 187 patients that includes over 20,000 features. Several machine learning methodologies were implemented for the analysis of censored patient follow-up information with such high-dimensional genomic data. Our findings demonstrated the potential of inclusion of mRNA information for BCR-free survival prediction. A random survival forest pipeline was found to achieve high predictive performance with respect to discrimination, calibration, and net benefit. Two mRNA variables, namely ESM1 and DHAH8, were identified as consistently strong predictors with this dataset.

Assessing and testing anomaly detection for finding prostate cancer in spatially registered multi-parametric MRI

Background

Evaluating and displaying prostate cancer through non-invasive imagery such as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research quantitatively applied supervised target algorithms using vectoral tumor signatures to spatially registered T1, T2, Diffusion, and Dynamic Contrast Enhancement images. This is the first study to apply the Reed-Xiaoli (RX) multi-spectral anomaly detector (unsupervised target detector) to prostate cancer, which searches for voxels that depart from the background normal tissue, and detects aberrant voxels, presumably tumors.

Methods

MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images, or seven components) were prospectively collected from 26 patients and then resized, translated, and stitched to form spatially registered multi-parametric cubes. The covariance matrix (CM) and mean μ were computed from background normal tissue. For RX, noise was reduced for the CM by filtering out principal components (PC), regularization, and elliptical envelope minimization. The RX images were compared to images derived from the threshold Adaptive Cosine Estimator (ACE) and quantitative color analysis. Receiver Operator Characteristic (ROC) curves were used for RX and reference images. To quantitatively assess algorithm performance, the Area Under the Curve (AUC) and the Youden Index (YI) points for the ROC curves were computed.

Results

The patient average for the AUC and [YI] from ROC curves for RX from filtering 3 and 4 PC was 0.734[0.706] and 0.727[0.703], respectively, relative to the ACE images. The AUC[YI] for RX from modified Regularization was 0.638[0.639], Regularization 0.716[0.690], elliptical envelope minimization 0.544[0.597], and unprocessed CM 0.581[0.608] using the ACE images as Reference Image. The AUC[YI] for RX from filtering 3 and 4 PC was 0.742[0.711] and 0.740[0.708], respectively, relative to the quantitative color images. The AUC[YI] for RX from modified Regularization was 0.643[0.648], Regularization 0.722[0.695], elliptical envelope minimization 0.508[0.605], and unprocessed CM 0.569[0.615] using the color images as Reference Image. All standard errors were less than 0.020.

Conclusions

This first study of spatially registered MP-MRI applied anomaly detection using RX, an unsupervised target detection algorithm for prostate cancer. For RX, filtering out PC and applying Regularization achieved higher AUC and YI using ACE and color images as references than unprocessed CM, modified Regularization, and elliptical envelope minimization.

Joint models for dynamic prediction in localised prostate cancer: a literature review

BACKGROUND

Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients' risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer.

METHODS

We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes.

RESULTS

Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations.

CONCLUSIONS

Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models.

Combining and analyzing novel multi-parametric magnetic resonance imaging metrics for predicting Gleason score

BACKGROUND

Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MP-MRI) to determine prostate tumor aggressiveness using the Prostate Imaging Reporting and Data System scoring system (PI-RADS). Recent studies showed that modified signal to clutter ratio (SCR), tumor volume, and eccentricity (elongation or roundness) of prostate tumors correlated with Gleason score (GS). No previous studies have combined the prostate tumor's shape, SCR, tumor volume, in order to predict potential tumor aggressiveness and GS.

METHODS

MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were obtained, resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm [adaptive cosine estimator (ACE)]. Pixel-based blobbing, and labeling were applied to the threshold ACE images. Eccentricity calculation used moments of inertia from the blobs. Tumor volume was computed by counting pixels within multi parametric MRI blobs and tumor outlines based on pathologist assessment of whole mount histology. Pathology assessment of GS was performed on whole mount prostatectomy. The covariance matrix and mean of normal tissue background was computed from normal prostate. Using signatures and normal tissue statistics, the z-score, noise corrected SCR [principal component (PC), modified regularization] from each patient was computed. Eccentricity, tumor volume, and SCR were fitted to GS. Analysis of variance assesses the relationship among the variables.

RESULTS

A multivariate analysis generated correlation coefficient (0.60 to 0.784) and P value (0.00741 to <0.0001) from fitting two sets of independent variates, namely, tumor eccentricity (the eccentricity for the largest blob, weighted average for the eccentricity) and SCR (removing 3 PCs, removing 4 PCs, modified regularization, and z-score) to GS. The eccentricity t-statistic exceeded the SCR t-statistic. The three-variable fit to GS using tumor volume (histology, MRI) yielded correlation coefficients ranging from 0.724 to 0.819 (P value <<0.05). Tumor volumes generated from histology yielded higher correlation coefficients than MRI volumes. Adding volume to eccentricity and SCR adds little improvement for fitting GS due to higher correlation coefficients among independent variables and little additional, independent information.

CONCLUSIONS

Combining prostate tumors eccentricity with SCR relatively highly correlates with GS.

Development and testing quantitative metrics from multi-parametric magnetic resonance imaging that predict Gleason score for prostate tumors

BACKGROUND

Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MRI) to detect possible clinically significant lesions using the Prostate Imaging Reporting and Data System (PI-RADS) protocol. The assessment of imaging, however, relies on the experience and judgement of radiologists creating opportunity for inter-reader variability. Quantitative metrics, such as z-score and signal to clutter ratio (SCR), are therefore needed.

METHODS

Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes for patients undergoing radical prostatectomy. Multi-parametric signatures that characterize prostate tumors were inserted into z-score and SCR. The multispectral covariance matrix was computed for the outlined normal prostate. The z-score from each MRI image was computed and summed. To reduce noise in the covariance matrix, following matrix decomposition, the noisy eigenvectors were removed. Also, regularization and modified regularization was applied to the covariance matrix by minimizing the discrimination score. The filtered and regularized covariance matrices were inserted into the SCR calculation. The z-score and SCR were quantitatively compared to Gleason scores from clinical pathology assessment of the histology of sectioned wholemount prostates.

RESULTS

Twenty-six consecutive patients were enrolled in this retrospective study. Median patient age was 60 years (range, 49 to 75 years), median prostate-specific antigen (PSA) was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL), and median Gleason score was 7 (range, 6 to 9). A linear fit of the summed z-score against Gleason score found a correlation of R=0.48 and a P value of 0.015. A linear fit of the SCR from regularizing covariance matrix against Gleason score found a correlation of R=0.39 and a P value of 0.058. The SCR employing the modified regularizing covariance matrix against Gleason score found a correlation of R=0.52 and a P value of 0.007. A linear fit of the SCR from filtering out 3 and 4 eigenvectors from the covariance matrix against Gleason score found correlations of R=0.50 and 0.44, respectively, and P values of 0.011 and 0.027, respectively.

CONCLUSIONS

Z-score and SCR using filtered and regularized covariance matrices derived from spatially registered multi-parametric MRI correlates with Gleason score with highly significant P values.

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

Background

Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness.

Methods

Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs.

Results

From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction.

Conclusions

Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.

Prostate zones and cancer: lost in transition?

Localized prostate cancer shows great clinical, genetic and environmental heterogeneity; however, prostate cancer treatment is currently guided solely by clinical staging, serum PSA levels and histology. Increasingly, the roles of differential genomics, multifocality and spatial distribution in tumorigenesis are being considered to further personalize treatment. The human prostate is divided into three zones based on its histological features: the peripheral zone (PZ), the transition zone (TZ) and the central zone (CZ). Each zone has variable prostate cancer incidence, prognosis and outcomes, with TZ prostate tumours having better clinical outcomes than PZ and CZ tumours. Molecular and cell biological studies can improve understanding of the unique molecular, genomic and zonal cell type features that underlie the differences in tumour progression and aggression between the zones. The unique biology of each zonal tumour type could help to guide individualized treatment and patient risk stratification.

Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.

Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality

Simple Summary

This article presents a gradient-boosted model that can predict 10-year prostate cancer mortality with high accuracy. The model was developed and validated on prospective multicenter data from the PLCO trial. Using XGBoost and Shapley values, it provides interpretability to understand its prediction. It can be used online to provide predictions and support informed decision-making in PCa treatment.

Abstract

Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users.

Biochemical recurrence after radical prostatectomy according to nadir prostate specific antigen value

The hypersensitive prostate specific antigen (PSA) test can measure in 0.01 ng/mL units, and its efficacy for screening after radical prostatectomy (RP) has been reported. In this study, we assessed patients who underwent RP to evaluate whether the nadir value affects biochemical recurrence (BCR). From 1995 to 2014, patients classified as N0 who had negative resection margins and a nadir PSA of less than 0.2 ng/mL were evaluated. The characteristics, pathological outcomes, PSA after RP, and BCR were assessed. A total of 1483 patients were enrolled. Among them, 323 (21.78%) patients showed BCR after RP. The mean age of the BCR group was 63.86±7.31 years, and while that of the no-recurrence group was 64.06±6.82 years (P = 0.645). The mean preoperative PSA of the BCR group was 9.75±6.92 ng/mL and that of the no-recurrence group was 6.71±5.19 ng/mL (P < 0.001). The mean time to nadir (TTN) in the BCR group was 4.64±7.65 months, while that in the no-recurrence group was 7.43±12.46 months (P < 0.001). The mean PSA nadir value was 0.035±0.034 ng/mL in the BCR group and 0.014±0.009 ng/mL in the no-recurrence group (P < 0.001). In multivariable Cox regression analyses, Gleason score, positive biopsy core percentages, minimal invasive surgery, nadir PSA value, and TTN were independently associated with BCR. The mean BCR occurred at 48.23±2.01 months after RP, and there was a significant difference in BCR occurrence according to the nadir PSA value (P < 0.001). A high PSA nadir value and short TTN may predict the risk of BCR after successful RP, aiding the identification of candidates for adjuvant or salvage therapies after RP.

Assessment of Agreement between Two Difference Prostate-Specific Antigen Assay Modalities

Simple Summary

Prostate-specific antigen is a biomarker for prostate cancer. If the level of prostate-specific antigen is high, a prostate biopsy is needed to diagnose prostate cancer. However, the definite level of prostate-specific antigen that requires prostate biopsy has not been established. Currently, there are many kinds of assay modalities that have been used for prostate-specific antigen testing. This study was conducted under the hypothesis that there will be differences between different assay modalities; therefore, there is no definite prostate-specific antigen level for prostate biopsy. In our study, the level of prostate-specific antigens was measured in one blood sample per patient, with two different assay modalities in 4810 patients. As a result, we confirmed that the overall agreement between the two modalities is excellent, but the agreement is slightly different in some ranges that may give clinical significance. Accordingly, the conformity between each assay modality should be secured in the future, and the threshold for the level of prostate-specific antigens for biopsy by each assay modality should be independently determined.

Abstract

There is controversy over the usefulness of prostate-specific antigen (PSA) as a prostate cancer (PCa) biomarker. This controversy arises when there are differences in the results of PSA assay modalities. In this study, which aimed to evaluate a proper validation between the two PSA assay modalities, the agreement between the results of the two modalities was analyzed. PSA examinations were conducted using two PSA assay modalities in 4810 patients. The intra-class correlation coefficient (ICC) and weighted kappa analysis were used to evaluate the agreement between the two assay modalities. A linear regression was performed to evaluate the association between the two assay modalities. According to ICC values (ICC: 0.999, p < 0.001) and weighted kappa analysis values (kappa: 0.951, alpha’s standard error (ASE): 0.001, p < 0.0001), the agreement between the assay modalities was rated as excellent. However, the strength of agreement was poor in the following PSA sub-groups: 0.05–0.1 ng/mL (ICC: 0.281, p = 0.0860); 0.15–0.2 ng/mL (ICC: 0.288, p = 0.0036); 1.5–2.0 ng/mL (ICC: 0.360, p = 0.0860); and 2.0–2.5 ng/mL (ICC: 0.303, p = 0.0868). In linear regression analysis, when modality B PSA yielded a value of 0.2 ng/mL, the expected value for modality A was 0.258 ng/mL (95% CI: 0.255–0.260), and when modality B PSA yielded a value of 4 ng/mL, the expected value for modality A was 3.192 ng/mL (95% CI: 3.150–3.235). The difference in the PSA values between the two PSA assay modalities is confirmed, and this difference may be clinically meaningful.

Modified J-CAPRA scoring system in predicting treatment outcomes of metastatic prostate cancer patients undergoing androgen deprivation therapy

The J-CAPRA score is an assessment tool which stratifies risk and predicts outcome of primary androgen deprivation therapy (ADT) using prostate-specific antigen, Gleason score, and clinical TNM staging. Here, we aimed to assess the generalisability of this tool in multi-ethnic Asians. Performance of J-CAPRA was evaluated in 782 Malaysian and 16,946 Japanese patients undergoing ADT from the Malaysian Study Group of Prostate Cancer (M-CaP) and Japan Study Group of Prostate Cancer (J-CaP) databases, respectively. Using the original J-CAPRA, 69.6% metastatic (M1) cases without T and/or N staging were stratified as intermediate-risk disease in the M-CaP database. To address this, we first omitted clinical T and N stage variables, and calculated the score on a 0-8 scale in the modified J-CAPRA scoring system for M1 patients. Notably, treatment decisions of M1 cases were not directly affected by both T and N staging. The J-CAPRA score threshold was adjusted for intermediate (modified J-CAPRA score 3-5) and high-risk (modified J-CAPRA score ≥6) groups in M1 patients. Using J-CaP database, validation analysis showed that overall survival, prostate cancer-specific survival, and progression-free survival of modified intermediate and high-risk groups were comparable to those of original J-CAPRA (p > 0.05) with Cohen's coefficient of 0.65. Around 88% M1 cases from M-CaP database were reclassified into high-risk category. Modified J-CAPRA scoring system is instrumental in risk assessment and treatment outcome prediction for M1 patients without T and/or N staging.

The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer

INTRODUCTION

Neighborhood socioeconomic (nSES) factors have been implicated in prostate cancer (PCa) disparities. In line with the Precision Medicine Initiative that suggests clinical and socioenvironmental factors can impact PCa outcomes, we determined whether nSES variables are associated with time to PCa diagnosis and could inform PCa clinical risk assessment.

MATERIALS AND METHODS

The study sample included 358 high risk men (PCa family history and/or Black race), aged 35-69 years, enrolled in an early detection program. Patient variables were linked to 78 nSES variables (employment, income, etc.) from previous literature via geocoding. Patient-level models, including baseline age, prostate specific antigen (PSA), digital rectal exam, as well as combined models (patient plus nSES variables) by race/PCa family history subgroups were built after variable reduction methods using Cox regression and LASSO machine-learning. Model fit of patient and combined models (AIC) were compared; p-values<0.05 were significant. Model-based high/low nSES exposure scores were calculated and the 5-year predicted probability of PCa was plotted against PSA by high/low neighborhood score to preliminarily assess clinical relevance.

RESULTS

In combined models, nSES variables were significantly associated with time to PCa diagnosis. Workers mode of transportation and low income were significant in White men with a PCa family history. Homeownership (%owner-occupied houses with >3 bedrooms) and unemployment were significant in Black men with and without a PCa family history, respectively. The 5-year predicted probability of PCa was higher in men with a high neighborhood score (weighted combination of significant nSES variables) compared to a low score (e.g., Baseline PSA level of 4ng/mL for men with PCa family history: White-26.7% vs 7.7%; Black-56.2% vs 29.7%).

DISCUSSION

Utilizing neighborhood data during patient risk assessment may be useful for high risk men affected by disparities. However, future studies with larger samples and validation/replication steps are needed.

Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis

Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the progression of PCa. Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment.

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

BACKGROUND

There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models.

METHODS

We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18-50 years) with clinically diagnosed diabetes recruited from primary and secondary care (n = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset (n = 504, 21% with type 1 diabetes).

RESULTS

Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities.

CONCLUSION

Logistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.

Personalized strategies in population screening for prostate cancer

This review discusses evidence for population-based screening with contemporary screening tools. In Europe, prostate-specific antigen (PSA)-based screening led to a relative reduction of prostate cancer (PCa) mortality, but also to a substantial amount of overdiagnosis and unnecessarily biopsies. Risk stratification based on a single variable (a clinical variable or based on the presence of a lesion on prostate imaging) or based on multivariable approaches can aid in reducing unnecessary prostate biopsies and overdiagnosis by selecting men who can benefit from further clinical assessment. Multivariable approaches include clinical variables, and biomarkers, often combined in risk calculators or nomograms. These risk calculators can also incorporate the result of MRI imaging. In general, as compared to a purely PSA based approach, the combination of relevant prebiopsy information results in superior selection of men at higher risk of harboring clinically significant prostate cancer. Currently, it is not possible to draw any conclusions on the superiority of these multivariable risk-based approaches since head-to-head comparisons are virtually lacking. Recently initiated large population-based screening studies in Finland, Germany and Sweden, incorporating various multivariable risk stratification approaches will hopefully give more insight in whether the harm-benefit ratio can be improved, that is, maintain (or improving) the ability to reduce metastatic disease and prostate cancer mortality while reducing harm caused by unnecessary testing and overdiagnosis including related overtreatment.

PROSPeCT: A Predictive Research Online System for Prostate Cancer Tasks

PURPOSE

An online clinical information system, called Predictive Research Online System Prostate Cancer Tasks (PROSPeCT), was developed to enable users to query the Alberta Prostate Cancer Registry database hosted by the Alberta Prostate Cancer Research Initiative. To deliver high-quality patient treatment, prostate cancer clinicians and researchers require a user-friendly system that offers an easy and efficient way to obtain relevant and accurate information about patients from a robust and expanding database.

METHODS

PROSPeCT was designed and implemented to make it easy for users to query the prostate cancer patient database by creating, saving, and reusing simple and complex definitions. We describe its intuitive nature by exemplifying the creation and use of a complex definition to identify a "high-risk" patient cohort.

RESULTS

PROSPeCT was made to minimize user error and to maximize efficiency without requiring the user to have programming skills. Thus, it provides tools that allow both novice and expert users to easily identify patient cohorts, manage individual patient care, perform Kaplan Meier estimates, plot aggregate PSA views, compute PSA-doubling time, and visualize results.

CONCLUSION

This report provides an overview of PROSPeCT, a system that helps clinicians to identify appropriate patient treatments and researchers to develop prostate cancer hypotheses, with the overarching goal of improving the quality of life of patients with prostate cancer. We have made available the code for the PROSPeCT implementation at https://github.com/max-uhlich/e-PROSPeCT .

Retrospective Cohort Analysis from a High-Volume Center of Prognostic Factors Affecting Biochemical Relapse in Patients with Encapsulated, Margin-Negative, Isolated Seminal Vesicle Invasion After Robot-Assisted Laparoscopic Prostatectomy: A Novel Study

Purpose: Specimen pathology findings collectively impact the long-term outcomes of robot-assisted laparoscopic prostatectomy. Since seminal vesicle invasion (SVI) is an important independent predictor of biochemical recurrence (BCR), this study was designed to evaluate the influence of isolated SVI in the absence of capsular/margin invasion on BCR. Material and Methods: Pathology reports of 2009 robot-assisted laparoscopic prostatectomy specimens were analyzed retrospectively excluding capsular breach and/or margin-positive cases to include 1409 patients in the study. Factors predicting SVI and BCR in this select group of patients were assessed and statistically analyzed. Survival analysis for PSA (prostate-specific antigen) failure probability and binomial regressions for variable predictability were performed. Results: The African American race was associated with SVI (p < 0.05). PSA had a directly proportional correlation with the occurrence of SVI and BCR. SVI was found to be an independent predictor of BCR, leading to higher odds of BCR at 5 years (odds ratio [OR] 8.2, 95% confidence interval [CI] 4.5-14.6, p < 0.0001). When the seminal vesicle was invaded, the specimen Gleason grade group (SGGG; OR 1.9, 95% CI 1.02-3.7, p = 0.04), PSA (OR 1.2, 95% CI 1.01-1.4, p = 0.03), and BMI (body mass index) (OR 1.2, 95% CI 1.04-1.5, p = 0.01) predicted BCR. Seminal vesicle involvement was not found in SGGG 1. Risk stratification of significant predictors of BCR with isolated SVI identified a subgroup with BMI ≤27.9 kg/m², PSA ≤8.6 ng/mL, and SGGG 2, which had a significantly better prognosis (p = 0.029, log-rank test). Conclusions: Seminal vesicles are infrequently involved with SGGG 1. Select groups of patients with isolated SVI who have low-grade disease with relatively lower PSA and BMI do not have an aggressive biological behavior and are unlikely to have a BCR, thereby circumventing unnecessary adjuvant therapy with its attendant side effects. The BMI significantly predicted PSA failures and should be considered as an additional risk assessment tool.

Overexpression of certain transient receptor potential and Orai channels in prostate cancer is associated with decreased risk of systemic recurrence after radical prostatectomy

BACKGROUND

Several studies had suggested the potential role of calcium signaling in prostate cancer (PCa) prognosis and agressiveness. We aimed to investigate selected proteins contributing to calcium (Ca²⁺ ) signaling, (Orai, stromal interaction molecule (STIM), and transient receptor potential (TRP) channels) and involved in cancer hallmarks, as independent predictors of systemic recurrence after radical prostatectomy (RP).

METHODS

A case-control study including 112 patients with clinically localized PCa treated by RP between 2002 and 2009 and with at least 6-years' follow-up. Patients were divided into two groups according to the absence or presence of systemic recurrence. Expression levels of 10 proteins involved in Ca²⁺ signaling (TRPC1, TRPC4, TRPV5, TRPV6, TRPM8, STIM1, STIM2, Orai1, Orai2, and Orai3), were assessed by immunohistochemistry using tissue microarrays (TMAs) constructed from paraffin-embedded PCa specimens. The level of expression of the various transcripts in PCa was assessed using quantitative polymerase chain reaction (qPCR) analysis. RNA samples for qPCR were obtained from fresh frozen tissue samples of PCa after laser capture microdissection on RP specimens. Relative gene expression was analyzed using the 2-▵▵Ct method.

RESULTS

Multivariate analysis showed that increased expression of TRPC1, TRPC4, TRPV5, TRPV6, TRPM8, and Orai2 was significantly associated with a lower risk of systemic recurrence after RP, independently of the prostate-specific antigen (PSA) level, percentage of positive biopsies, and surgical margin (SM) status (P = .007, P = .01, P < .001, P = .0065, P = .007, and P = .01, respectively). For TRPC4, TRPV5, and TRPV6, this association was also independent of Gleason score and pT stage. Moreover, overexpression of TRPV6 and Orai2 was significantly associated with longer time to recurrence after RP (P = .048 and .023, respectively). Overexpression of TRPC4, TRPV5, TRPV6, and Orai2 transcripts was observed in group R- (3.71-, 5.7-, 1.14-, and 2.65-fold increase, respectively).

CONCLUSIONS

This is the first study to suggest the independent prognostic value of certain proteins involved in Ca²⁺ influx in systemic recurrence after RP: overexpression of TRPC1, TRPC4, TRPV5, TRPV6, TRPM8, and Orai2 is associated with a lower risk of systemic recurrence. TRPC4, TRPV5, and TRPV6 appear to be particularly interesting, as they are independent of the five commonly used predictive factors, that is, PSA, percentage of positive biopsies, SM status, Gleason score, and pT stage.

Smoking history, intensity, and duration and risk of prostate cancer recurrence among men with prostate cancer who received definitive treatment

PURPOSE

To examine the association of smoking history and multiple measures of smoking intensity and duration with risk of biochemical recurrence in men treated for prostate cancer.

METHODS

We conducted a prospective cohort study of 1641 men (773 ever-smokers) treated with radical prostatectomy or radiation between 2003 and 2010. The association between ever-smoking and risk of biochemical recurrence was examined using Cox Proportional Hazards models with adjustment for confounders. Among ever-smokers, we further assessed the association between multiple measures of smoking duration and intensity and risk of biochemical recurrence.

RESULTS

In the full cohort, we observed no association between ever-smoking and risk of biochemical recurrence. However, among ever-smokers, a smoking duration of greater than or equal to 10 years was significantly associated with biochemical recurrence (hazard ratio: 2.32, 95% confidence interval: 1.01, 5.33). Our results also suggested that greater than or equal to 10 pack-years of smoking may be associated with an increased risk of biochemical recurrence (hazard ratio: 1.75, 95% confidence interval: 0.97, 3.15). No association was observed between packs smoked per day or years since smoking cessation (among former smokers) and risk of biochemical recurrence.

CONCLUSION

Smoking duration is a significant predicator of biochemical recurrence among men with prostate cancer who are current or former smokers.

Controversial issues in Gleason and International Society of Urological Pathology (ISUP) prostate cancer grading: proposed recommendations for international implementation

The Gleason Grading system has been used for over 50 years to prognosticate and guide the treatment for patients with prostate cancer. At consensus conferences in 2005 and 2014 under the guidance of the International Society of Urological Pathology (ISUP), the system has undergone major modifications to reflect modern diagnostic and therapeutic practices. The 2014 consensus conference yielded recommendations regarding cribriform, mucinous, glomeruloid and intraductal patterns, the most significant of which was the removal of any cribriform pattern from Gleason grade 3. Furthermore, a Gleason score grouping system was endorsed which consisted of five grades where Gleason score 6 (3+3) was classified as grade 1 which better reflected the mostly indolent behaviour of these tumours. Another issue discussed at the meeting and subsequently endorsed was that in Gleason score 7 cases, the percentage pattern 4 should be recorded. This is especially important in situations where modern active surveillance protocols expand to include men with low volume pattern 4. While major progress was made at the conference, several issues were either not resolved or not discussed at all. Most of these items relate to details of assignment of Gleason score and ISUP grade in specific specimen types and grading scenarios. This detailed review looks at the 2014 ISUP conference results and subsequent literature from an international perspective and proposes several recommendations. The specific issues addressed are percentage pattern 4 in Gleason score 7 tumours, percentage patterns 4 and 5 or 4/5 in Gleason score 8-10 disease, minor (≤5%) high grade patterns when either 2 or 3 patterns are present, level of reporting (core, specimen, case), dealing with grade diversity among site (highest and composite scores) and reporting scores in radical prostatectomy specimens with multifocal disease. It is recognised that for many of these issues, a strong evidence base does not exist, and further research studies are required. The proposed recommendations mostly reflect consolidated expert opinion and they are classified as established if there was prior agreement by consensus and provisional if there was no previous agreement or if the item was not discussed at prior consensus conferences. For some items there are reporting options that reflect the local requirements and diverse practice models of the international urological pathology community. The proposed recommendations provide a framework for discussion at future consensus meetings.

Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review

OBJECTIVES

Men diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models.

DESIGN

Systematic literature review, pre-specified and registered on PROSPERO (CRD42018086394).

DATA SOURCES

MEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms.

ELIGIBILITY CRITERIA

Inclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis.

DATA EXTRACTION AND SYNTHESIS

Title, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool.

RESULTS

Database searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available.

CONCLUSION

Few long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.

Communicating uncertainty in cancer prognosis: A review of web-based prognostic tools

Objective To review how web-based prognosis tools for cancer patients and clinicians describe aleatory (risk estimates) and epistemic (imprecision in risk estimates) uncertainties. Methods We reviewed prognostic tools available online and extracted all uncertainty descriptions. We adapted an existing classification and classified each extracted statement by presentation of uncertainty. Results We reviewed 222 different prognostic risk tools, which produced 772 individual estimates. When describing aleatory uncertainty, almost all (90%) prognostic tools included a quantitative description, such as "chances of survival after surgery are 10%", though there was heterogeneity in the use of percentages, natural frequencies, and use of graphics. Only 14% of tools described epistemic uncertainty. Of those that did, most used a qualitative prefix such as "about" or "up to", while 22 tools described quantitative descriptions using confidence intervals or ranges. Conclusions Considerable heterogeneity exists in the way uncertainties are communicated in cancer prognostic tools. Few tools describe epistemic uncertainty. This variation is predominately explained by a lack of evidence and consensus in risk communication, particularly for epistemic uncertainty. Practice Implications As precision medicine seeks to improve prognostic estimates, the community may not be equipped with the tools to communicate the results accurately and effectively to clinicians and patients.

Circulating miRNAs in localized/locally advanced prostate cancer patients after radical prostatectomy and radiotherapy

BACKGROUND

Overtreatment is a well-known clinical challenge in local prostate cancer (PCa). Although risk assessment models have contributed to a better stratification of patients with local PCa, a tailored management is still in its infancy. Over the last few decades, microRNAs (miRNAs) have shown promising results as biomarkers in PCa. The aim of this study was to investigate circulating miRNAs after management of local PCa.

METHODS

The relative expression of four miRNAs (miRNA-21, -93, -125b, and miRNA-221) was assessed in plasma from 149 newly diagnosed patients with local or locally advanced PCa. Real-time polymerase chain reaction was used for analysis. A baseline sample at time of diagnosis and a follow-up sample after 6 months were assessed. The patients were grouped in an interventional cohort (radical prostatectomy, curative intent radiotherapy, or androgen-deprivation therapy alone) and an observational cohort (watchful waiting or active surveillance).

RESULTS

In the interventional cohort, levels of both miRNA-93 and miRNA-221 were significantly lower in the follow-up samples compared to baseline z = -2.738, P = 0.006, and z = -4.498, P < 0.001, respectively. The same observation was recorded for miRNA-125b in the observational cohort (z = -2.656, P = 0.008). Both miRNA-125b and miRNA-221 were correlated with risk assessment r = 0.23, P = 0.015, and r = 0.203, P = 0.016 respectively, while miRNA-93 showed tendency to significant correlation with the prostatectomy Gleason score (r = 0.276, P = 0.0576).

CONCLUSIONS

The current results indicate a possible role of miRNA-93 and miRNA-221 in disease monitoring in localized and locally advanced PCa. Larger studies are warranted to assess the clinical impact of these biomarkers.

A national survey of radiation oncologists and urologists on prediction tools and nomograms for localized prostate cancer

PURPOSE

Although prediction tools for prostate cancer (PCa) are essential for high-quality treatment decision-making, little is known about the degree of confidence in existing tools and whether they are used in clinical practice from radiation oncologists (RO) and urologists (URO). Herein, we performed a national survey of specialists about perceived attitudes and use of prediction tools.

METHODS

In 2017, we invited 940 URO and 911 RO in a national survey to query their confidence in and use of the D'Amico criteria, Kattan Nomogram, and CAPRA score. The statistical analysis involved bivariate association and multivariable logistic regression analyses to identify physician characteristics (age, gender, race, practice affiliation, specialty, access to robotic surgery, ownership of linear accelerator and number of prostate cancer per week) associated with survey responses and use of active surveillance (AS) for low-risk PCa.

RESULTS

Overall, 691 (37.3%) specialists completed the surveys. Two-thirds (range 65.6-68.4%) of respondents reported being "somewhat confident", but only a fifth selected "very confident" for each prediction tool (18.0-20.1%). 19.1% of specialists in the survey reported not using any prediction tools in clinical practice, which was higher amongst URO than RO (23.9 vs. 13.4%; p < 0.001). Respondents who reported not using prediction tools were also associated with low utilization of AS in their low-risk PCa patients (adjusted OR 2.47; p = 0.01).

CONCLUSIONS

While a majority of RO and URO view existing prediction tools for localized PCa with some degree of confidence, a fifth of specialists reported not using any such tools in clinical practice. Lack of using such tools was associated with low utilization of AS for low-risk PCa.

Evaluation of the most optimal multiparametric magnetic resonance imaging sequence for determining pathological length of capsular contact

OBJECTIVES

To assess the most optimal multi-parametric magnetic resonance imaging sequence (Mp-MRI) in determining pathological length of capsular contact (LCC) for the diagnosis of prostate cancer extraprostatic extension (EPE).

METHODS

105 patients with prostate cancer who underwent Mp-MRI of prostate prior to radical prostatectomy were enrolled in this retrospective study. LCC was determined from T2-weighted images (T2WI), Apparent Diffusion Coefficient (ADC) map, dynamic contrast-enhanced MRI (DCE-MRI) separately by two blinded radiologists. The LCCs in patients with and without EPE were compared with Mann Whitney-U test. The relationship between pathological LCC and the LCC that was measured from each Mp-MRI sequences were calculated by using Spearman test. The ability of all individual Mp-MRI sequences in determining pathological LCC was calculated by drawing receiver operator characteristic (ROC) curves. The diagnostic accuracy of LCC based on each MRI sequences for EPE diagnosis was also calculated with ROC curve analysis.

RESULTS

The patients with EPE had longer median LCC than patients without EPE for each Mp-MRI sequences and for both readers. In addition, the LCC showed a broader overlapping between patients with and without EPE on ADC map (reader-1, p = 0.01; reader-2, p = 0.01) when compared with T2WI (reader-1, p = 0.002; reader-2, p = 0.001) and DCE-MRI (reader-1, p = 0.001; reader-2, p = 0.001). LCC based on DCE-MRI showed the strongest correlation with pathological LCC. The area under the curve (AUC) based on LCC was higher when using the DCE-MRI (reader-1: 0.874, p = 0.030; reader-2: 0.862, p = 0.02) than when using T2WI and ADC map in predicting pathological LCC for both readers. While the LCC based on ADC map showed poor diagnostic accuracy, LCC based on T2WI and DCE-MRI had fair diagnostic accuracy for EPE diagnosis.

CONCLUSION

The contact between prostate tumor and capsule seems to be a useful and objective parameter for evaluating the EPE of prostate cancer with Mp-MRI. More specifically, LCC based on DCE-MRI has highest correlation with pathological LCC and has better ability to predict pathological LCC when compared with other Mp-MRI sequences. However, the performance of LCC based on T2WI and DCE-MRI was similar for EPE diagnosis. It seems measurement of LCC from DCE-MRI and measurement of LCC from T2WI does not show any difference in clinical EPE assessment.

Multi-disciplinary and shared decision-making approach in the management of organ-confined prostate cancer

Decision-making in the management of organ-confined prostate cancer is complex as it is based on multi-factorial considerations. It is complicated by a multitude of issues, which are related to the patient, treatment, disease, availability of equipment(s), expertise, and physicians. Combination of all these factors play a major role in the decision-making process and provide for an interactive decision-making preferably in the multi-disciplinary team (MDT) meeting. MDT decisions are comprehensive and are often based on all factors including patients' biological status, disease and its aggressiveness, and physician and centres' expertise. However, one important and often under rated factor is patient-related factors. There is considerable evidence that patients and physicians have different goals for treatment and physicians' understanding of their own patients' preferences is not accurate. Several patient-related key factors have been identified such as age, religious beliefs, sexual health, educational background, and cognitive impairment. We have focused on these areas and highlight some key factors that need to be taken considered whilst counselling a patient and understanding his choice of treatment, which might not always be match with the clinicians' recommendation.

Performance of prostate cancer recurrence nomograms by obesity status: a retrospective analysis of a radical prostatectomy cohort

Background

Obesity has been associated with aggressive prostate cancer and poor outcomes. It is important to understand how prognostic tools for that guide prostate cancer treatment may be impacted by obesity. The goal of this study was to evaluate the predicting abilities of two prostate cancer (PCa) nomograms by obesity status.

Methods

We examined 1576 radical prostatectomy patients categorized into standard body mass index (BMI) groups. Patients were categorized into low, medium, and high risk groups for the Kattan and CaPSURE/CPDR scores, which are based on PSA value, Gleason score, tumor stage, and other patient data. Time to PCa recurrence was modeled as a function of obesity, risk group, and interactions.

Results

As expected for the Kattan score, estimated hazard ratios (95% CI) indicated higher risk of recurrence for medium (HR = 2.99, 95% CI = 2.29, 3.88) and high (HR = 8.84, 95% CI = 5.91, 13.2) risk groups compared to low risk group. The associations were not statistically different across BMI groups. Results were consistent for the CaPSURE/CPDR score. However, the difference in risk of recurrence in the high risk versus low risk groups was larger for normal weight patients than the same estimate in the obese patients.

Conclusions

We observed no statistically significant difference in the association between PCa recurrence and prediction scores across BMI groups. However, our study indicates that there may be a stronger association between high risk status and PCa recurrence among normal weight patients compared to obese patients. This suggests that high risk status based on PCa nomogram scores may be most predictive among normal weight patients. Additional research in this area is needed.

High levels of glioma tumor suppressor candidate region gene 1 predicts a poor prognosis for prostate cancer

Glioma tumor suppressor candidate region gene 1 (GLTSCR1) is associated with the progression of oligodendroglioma. However, there has been little study of GLTSCR1 in prostate cancer. In the present study, the association between the expression of GLTSCR1, and the progression and prognosis of tumors in patients with prostate cancer was assessed. An immunohistochemical analysis was performed using a human tissue microarray for GLTSCR1 at the protein expression level and the immunostaining results were evaluated against clinical variables of patients with prostate cancer. Subsequently, The Cancer Genome Atlas (TCGA) was used to validate the analysis results at the mRNA level and to study the prognostic value of GLTSCR1 in prostate cancer. Immunohistochemistry and TCGA data analysis revealed that GLTSCR1 expression in the prostate cancer tissues was significantly higher than that in the benign prostate tissues (immunoreactivity score, P=0.015; mRNA levels: cancer, 447.7±6.45 vs. benign, 343.5±4.21; P<0.001). Additionally, the increased GLTSCR1 protein expression was associated with certain clinical variables in the prostate cancer tissues, including advanced clinical stage (P<0.001), enhanced tumor invasion (P=0.003), lymph node metastasis (P=0.003) and distant metastasis (P=0.001). TCGA data revealed similar results, demonstrating that the upregulation of GLTSCR1 mRNA expression was associated with the Gleason score (P<0.001), enhanced tumor invasion (P=0.011), lymph node metastasis (P=0.001) and distant metastasis (P=0.002). Furthermore, Kaplan-Meier analysis suggested that among all patients, high GLTSCR1 expression indicated a decreased overall survival (P=0.028) and biochemical recurrence (BCR)-free survival (P=0.004), compared with patients with low GLTSCR1 expression. Finally, multivariate analysis revealed that the expression of GLTSCR1 was an independent predictor of poor BCR-free survival (P=0.049). The present study suggested that the increased expression of GLTSCR1 was associated with the progression of prostate cancer. Furthermore, GLTSCR1 may be a novel biomarker that is able to predict the clinical outcome in prostate cancer patients.

Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population

PURPOSE

The use of prostate-specific antigen as a biomarker for prostate cancer (PC) has been controversial and is, therefore, not used by many countries in their national health screening programs. The biological characteristics of PC in East Asians including Koreans and Japanese are different from those in the Western populations. Potential lifestyle risk factors for PC were evaluated with the aim of developing a risk prediction model.

Materials and Methods

A total of 1,179,172 Korean men who were cancer free from 1996 to 1997, had taken a physical examination, and completed a lifestyle questionnaire, were enrolled in our study to predict their risk for PC for the next eight years, using the Cox proportional hazards model. The model's performance was evaluated using the C-statistic and Hosmer‒Lemeshow type chi-square statistics.

RESULTS

The risk prediction model studied age, height, body mass index, glucose levels, family history of cancer, the frequency of meat consumption, alcohol consumption, smoking status, and physical activity, which were all significant risk factors in a univariate analysis. The model performed very well (C statistic, 0.887; 95% confidence interval, 0.879 to 0.895) and estimated an elevated PC risk in patients who did not consume alcohol or smoke, compared to heavy alcohol consumers (hazard ratio [HR], 0.78) and current smokers (HR, 0.73) (p < 0.001).

CONCLUSION

This model can be used for identifying Korean and other East Asian men who are at a high risk for developing PC, as well as for cancer screening and developing preventive health strategies.

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.

High levels of DEPDC1B predict shorter biochemical recurrence-free survival of patients with prostate cancer

DEP domain-containing protein 1B (DEPDC1B) has been reported to serve important functions in breast cancer and non-small cell lung cancer. However, its involvement in the development of prostate cancer (PCa) remains unclear. Therefore, the present study aimed to investigate the expression and clinical significance of DEPDC1B in tumor tissues from patients diagnosed with PCa. A total of 80 prostate tissue samples were collected following prostatectomy to generate a tissue microarray for immunohistochemical analysis of DEPDC1B protein expression. High throughput sequencing of mRNAs from 179 prostate tissue samples, either from patients with PCa or from healthy controls, was included in the Taylor dataset. The expression levels of DEPDC1B in tumor tissues from patients with PCa were revealed to be significantly increased compared with those in normal prostate tissues (P=0.039). Increased expression of DEPDC1B was significantly associated with advanced clinical stage (P=0.006), advanced T stage (P=0.012) and lymph node metastasis (P=0.004). Kaplan-Meier analysis demonstrated that patients with high levels of DEPDC1B mRNA had significantly shorter biochemical recurrence (BCR)-free survival times. Multivariate analysis using Cox proportional hazards model revealed that levels of DEPDC1B mRNA were significant independent predictors of BCR-free survival time of patients with PCa. Therefore, the expression of DEPDC1B may be used as an independent predictor of biochemical recurrence-free survival time of patients with PCa.

Tissue-Based MicroRNAs as Predictors of Biochemical Recurrence after Radical Prostatectomy: What Can We Learn from Past Studies?

With the increasing understanding of the molecular mechanism of the microRNAs (miRNAs) in prostate cancer (PCa), the predictive potential of miRNAs has received more attention by clinicians and laboratory scientists. Compared with the traditional prognostic tools based on clinicopathological variables, including the prostate-specific antigen, miRNAs may be helpful novel molecular biomarkers of biochemical recurrence for a more accurate risk stratification of PCa patients after radical prostatectomy and may contribute to personalized treatment. Tissue samples from prostatectomy specimens are easily available for miRNA isolation. Numerous studies from different countries have investigated the role of tissue-miRNAs as independent predictors of disease recurrence, either alone or in combination with other clinicopathological factors. For this purpose, a PubMed search was performed for articles published between 2008 and 2017. We compiled a profile of dysregulated miRNAs as potential predictors of biochemical recurrence and discussed their current clinical relevance. Because of differences in analytics, insufficient power and the heterogeneity of studies, and different statistical evaluation methods, limited consistency in results was obvious. Prospective multi-institutional studies with larger sample sizes, harmonized analytics, well-structured external validations, and reasonable study designs are necessary to assess the real prognostic information of miRNAs, in combination with conventional clinicopathological factors, as predictors of biochemical recurrence.

Risk Stratification of Newly Diagnosed Prostate Cancer with Genomic Platforms

INTRODUCTION

Interest in novel risk stratification tools for men with newly diagnosed prostate cancer has flourished, aiming to offer increasingly accurate predictions of future disease behavior to ultimately better guide clinical management. We highlight the use of genomic platforms attempting to refine clinical decisions at the point of initial diagnosis.

METHODS

In the context of a benchmark standard of clinical risk stratification tools we reviewed the role of genomic tests, including individual gene expression assays, as well as a growing number of tissue based expression tests assessing multiple gene panels, to improve predictions at initial diagnosis.

RESULTS

The role of single gene status including TMPRSS2:ERG fusion and PTEN expression has been investigated among men with newly diagnosed prostate cancer. Gene expression profiles incorporating panels of genes associated with prostate cancer outcome have received external validation and have commercial application in assays that incorporate baseline clinical risk to offer predictions of immediate pathological and downstream disease end points. Comparisons of gene signatures have offered insights into relative predictive performance in archival tissue. However, to date no studies appear to directly support a single genomic assay offering superior clinical usefulness for decision making at the time of diagnosis.

CONCLUSIONS

Risk stratification tools incorporating genomic analysis of prostate cancer have been developed which seek to improve the accuracy of initial predictions. Further study is warranted to define the additive clinical benefit associated with their use if implemented broadly.

A comparison of 3 on-line nomograms with the detection of primary circulating prostate cells to predict prostate cancer at initial biopsy

INTRODUCTION

The use of nomograms which include the PSA may improve the predictive power of obtaining a prostate biopsy (PB) positive for cancer. We compare the use of three on-line nomagrams with the detection of primary malignant circulating prostate cells (CPCs) to predict the results of an initial PB in men with suspicion of prostate cancer.

METHODS AND PATIENTS

Consecutive men with suspicion of prostate cancer underwent a 12 core TRUS prostate biopsy; age, total serum PSA, percent free PSA, family history, ethnic origin and prostate ultrasound results were used for risk assessment using the online nomograms. Mononuclear cells were obtained by differential gel centrifugation from 8ml of blood and CPCs were identified using double immunomarcation with anti-PSA and anti-P504S. A CPC was defined as a cell expressing PSA and P504S and defined as negative/positive. Biopsies were classified as cancer/no-cancer. Areas under the curve (AUC) for each parameter were calculated and compared and diagnostic yields were calculated.

RESULTS

1,223 men aged>55 years participated, 467 (38.2%) had a biopsy positive for cancer of whom 114/467 (24.4%) complied with the criteria for active observation. Area under the curve analysis showed CPC detection to be superior (p<0.001), avoiding 57% of potential biopsies while missing 4% of clinically significant prostate cancers.

CONCLUSIONS

The CPC detection was superior to the nomograms in predicting the presence of prostate cancer at initial biopsy; its high negative predictive value potentially reduces the number of biopsies while missing few significant cancers, being superior to the nomograms in this aspect. Being a positive/negative test the detection of CPCs avoids defining a cutoff value which may differ between populations.