Gleason score and laterality concordance between prostate biopsy and prostatectomy specimens

Secreted miR-153 Controls Proliferation and Invasion of Higher Gleason Score Prostate Cancer

Prostate cancer (PC) is a male common neoplasm and is the second leading cause of cancer death in American men. PC is traditionally diagnosed by the evaluation of prostate secreted antigen (PSA) in the blood. Due to the high levels of false positives, digital rectal examination and transrectal ultrasound guided biopsy are necessary in uncertain cases with elevated PSA levels. Nevertheless, the high mortality rate suggests that new PC biomarkers are urgently needed to help clinical diagnosis. In a previous study, we have identified a network of genes, altered in high Gleason Score (GS) PC (GS ≥ 7), being regulated by miR-153. Until now, no publication has explained the mechanism of action of miR-153 in PC. By in vitro studies, we found that the overexpression of miR-153 in high GS cell lines is required to control cell proliferation, migration and invasion rates, targeting Kruppel-like factor 5 (KLF5). Moreover, miR-153 could be secreted by exosomes and microvesicles in the microenvironment and, once entered into the surrounding tissue, could influence cellular growth. Being upregulated in high GS human PC, miR-153 could be proposed as a circulating biomarker for PC diagnosis.

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring

Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.

Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness

Radiomics allows the extraction quantitative features from imaging, as imaging biomarkers of disease. The objective of this exploratory study is to implement a reproducible radiomic-pipeline for the extraction of a magnetic resonance imaging (MRI) signature for prostate cancer (PCa) aggressiveness. One hundred and two consecutive patients performing preoperative prostate multiparametric magnetic resonance imaging (mpMRI) and radical prostatectomy were enrolled. Multiparametric images, including T2-weighted (T2w), diffusion-weighted and dynamic contrast-enhanced images, were acquired at 1.5 T. Ninety-three imaging features (Ifs) were extracted from segmentation of index lesion. Ifs were ranked based on a stability rank and redundant Ifs were excluded. Using unsupervised hierarchical clustering, patients were grouped on the basis of similar radiomic patterns, whose association with Gleason Grade Group (GGG), extracapsular extension (ECE), and nodal involvement (pN) was tested. Signatures composed by IFs from T2w-images and Apparent Diffusion Coefficient (ADC) maps were tested for the prediction of GGG, ECE, and pN. T2w radiomic pattern was associated with pN, ECE, and GGG (p = 0.027, 0.05, 0.03) and ADC radiomic pattern was associated with GGG (p = 0.004). The best performance was reached by the signature combing IFs from multiparametric images (0.88, 0.89, and 0.84 accuracy for GGG, pN, and ECE). A reliable multiparametric MRI radiomic signature was extracted, potentially able to predict PCa aggressiveness, to be further validated on an independent sample.

Prediction of prostate cancer aggressiveness using 18F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for 18F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADCk, K), mono- (ADCm), and biexponential functions (f, Dp, Df) while Logan plots were used to calculate volume of distribution (VT). In total, 16 unique PET (VT, SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for VT was 0.85. The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and 18F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of 18F-FACBC PET derived parameters (VT, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization

Purpose

To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T₂-weighted imaging (T₂w), diffusion weighted imaging (DWI) acquired using high b values, and T₂-mapping (T₂).

Methods

T₂w, DWI (12 b values, 0–2000 s/mm²), and T₂ data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T₂w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.

Results

In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T₂w, ADC_m and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T₂ mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments.

Conclusion

Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T₂w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.

The Impact of 68Ga-PSMA PET/CT and PET/MRI on the Management of Prostate Cancer

Prostate-specific membrane antigen (PSMA) is a transmembrane protein with significantly increased expression in the cells and metastases of prostate carcinoma (CaP). PSMA-expression correlates with higher serum levels of prostate-specific antigen (PSA) and a higher Gleason score (GS). This finding has led to the development of novel imaging modalities such as 68Ga-/18F-labeled PSMA positron emission tomography/computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MRI). This article reviews the literature pertaining to various new imaging technologies for the management of CaP. PSMA positron emission tomography/computed tomography appears to be an excellent diagnostic tool, that may drastically impact the management of a large number of patients with primary and recurrent CaP.

Prospective evaluation of 18F-FACBC PET/CT and PET/MRI versus multiparametric MRI in intermediate- to high-risk prostate cancer patients (FLUCIPRO trial)

PURPOSE

The purpose of this study was to evaluate ¹⁸F-FACBC PET/CT, PET/MRI, and multiparametric MRI (mpMRI) in detection of primary prostate cancer (PCa).

METHODS

Twenty-six men with histologically confirmed PCa underwent PET/CT immediately after injection of 369 ± 10 MBq ¹⁸F-FACBC (fluciclovine) followed by PET/MRI started 55 ± 7 min from injection. Maximum standardized uptake values (SUV_max) were measured for both hybrid PET acquisitions. A separate mpMRI was acquired within a week of the PET scans. Logan plots were used to calculate volume of distribution (V_T). The presence of PCa was estimated in 12 regions with radical prostatectomy findings as ground truth. For each imaging modality, area under the curve (AUC) for detection of PCa was determined to predict diagnostic performance. The clinical trial registration number is NCT02002455.

RESULTS

In the visual analysis, 164/312 (53%) regions contained PCa, and 41 tumor foci were identified. PET/CT demonstrated the highest sensitivity at 87% while its specificity was low at 56%. The AUC of both PET/MRI and mpMRI significantly (p < 0.01) outperformed that of PET/CT while no differences were detected between PET/MRI and mpMRI. SUV_max and V_T of Gleason score (GS) >3 + 4 tumors were significantly (p < 0.05) higher than those for GS 3 + 3 and benign hyperplasia. A total of 442 lymph nodes were evaluable for staging, and PET/CT and PET/MRI demonstrated true-positive findings in only 1/7 patients with metastatic lymph nodes.

CONCLUSIONS

Quantitative ¹⁸F-FACBC imaging significantly correlated with GS but failed to outperform MRI in lesion detection. ¹⁸F-FACBC may assist in targeted biopsies in the setting of hybrid imaging with MRI.

Relaxation along fictitious field, diffusion-weighted imaging, and T2 mapping of prostate cancer: Prediction of cancer aggressiveness

PURPOSE

To evaluate the performance of relaxation along a fictitious field (RAFF) relaxation time (TRAFF ), diffusion-weighted imaging (DWI)-derived parameters, and T2 relaxation time values for prostate cancer (PCa) detection and characterization.

METHODS

Fifty patients underwent 3T MR examination using surface array coils before prostatectomy. DWI was performed using 14 and 12 b values in the ranges of 0-500 s/mm(2) and 0-2000 s/mm(2) , respectively. Repeated MR examination was performed in 16 patients. TRAFF , DWI-derived parameters (monoexponential, kurtosis, biexponential models), and T2 values were measured and averaged over regions of interest placed in PCa and normal tissue. Repeatability of TRAFF and DWI-derived parameters were assessed by coefficient of repeatability and intraclass correlation coefficient ICC(3,1). Areas under the receiver operating characteristic curve (AUCs) for PCa detection and Gleason score classification were estimated. The parameters were correlated with Gleason score groups using Spearman correlation coefficient (ρ).

RESULTS

ICC(3,1) values for TRAFF were in the range of 0.82-0.92. TRAFF values had higher AUC values for Gleason score classification compared with DWI-derived parameters and T2 . The RAFF method demonstrated the highest ρ value (-0.65).

CONCLUSION

In a quantitative region of interest-based analysis, RAFF outperformed DWI ("low" and "high" b values) and T2 mapping in the characterization of PCa.

Rotating frame relaxation imaging of prostate cancer: Repeatability, cancer detection, and Gleason score prediction

PURPOSE

To investigate relaxation along a fictitious field (RAFF) and continuous wave (cw) T1ρ imaging of prostate cancer (PCa) in the terms of repeatability, PCa detection, and characterization.

METHODS

Thirty-six patients (PSA 11.6 ± 7.6 ng/mL, mean ± standard deviation) with histologically confirmed PCa underwent two repeated 3T MR examinations using surface array coils before prostatectomy. Relaxation along fictitious field, cw T1ρ, and T2 relaxation times (TRAFF, T1ρcw, T2) were measured and averaged over regions of interest placed in PCa, normal peripheral zone (PZ), and normal central gland (CG) positioned using whole-mount prostatectomy sections and anatomical T2-weighted images. Receiver operating characteristic curve analysis with area under the curve (AUC) was calculated to distinguish PCa from PZ/CG and PCa with Gleason score (GS) of 3+3 from GS of 3+4/≥ 3+4.

RESULTS

TRAFF and T1ρcw relaxation times were repeatable with coefficients of repeatability as a percentage of median value in the range of 7.8-23.2%. AUC (mean, 95% confidence interval) in the differentiation of PCa with GS of 3+3 from PCa with CS of ≥ 3+4 were 0.88 (0.72-0.99), 0.69 (0.46-0.90), and 0.68 (0.45-0.88), for TRAFF, T1ρcw, and T2, respectively.

CONCLUSION

In quantitative region of interest based analysis, TRAFF outperformed T1ρcw and T2 in PCa detection and characterization.

Is small prostate volume a predictor of Gleason score upgrading after radical prostatectomy?

PURPOSE

We aimed to analyze the relationship between prostate volume and Gleason score (GS) upgrading [higher GS category in the radical prostatectomy (RP) specimen than in the prostate biopsy] in Korean men.

MATERIALS AND METHODS

We retrospectively analyzed the medical records of 247 men who underwent RP between May 2006 and April 2011 at our institution. Transrectal ultrasound (TRUS) volume was categorized as 25 cm³ or less (n=61), 25 to 40 cm³ (n=121) and greater than 40 cm³ (n=65). GS was examined as a categorical variable of 6 or less, 3+4 and 4+3 or greater. The relationship between TRUS volume and upgrading of GS was analyzed using multivariate logistic regression.

RESULTS

Overall, 87 patients (35.2%) were upgraded, 20 (8.1%) were downgraded, and 140 (56.7%) had identical biopsy and pathological Gleason sum groups. Smaller TRUS volume was significantly associated with increased likelihood of upgrading (p trend=0.022). Men with prostates 25 cm³ or less had more than 2.7 times the risk of disease being upgraded relative to men with TRUS volumes more than 40 cm³ (OR 2.718, 95% CI 1.403-8.126).

CONCLUSION

In our study, smaller prostate volumes were at increased risk for GS upgrading after RP. This finding should be kept in mind when making treatment decisions for men with prostate cancer that appears to be of a low grade on biopsy, especially in Asian urologic fields.

Current status of biomarkers for prostate cancer

Prostate cancer (PCa) is a leading cause of cancer-related death of men globally. Since its introduction, there has been intense debate as to the effectiveness of the prostate specific antigen (PSA) test as a screening tool for PCa. It is now evident that the PSA test produces unacceptably high rates of false positive results and is not prognostic. Here we review the current status of molecular biomarkers that promise to be prognostic and that might inform individual patient management. It highlights current efforts to identify biomarkers obtained by minimally invasive methods and discusses current knowledge with regard to gene fusions, mRNA and microRNAs, immunology, and cancer-associated microparticles.

Findings in 12-core transrectal ultrasound-guided prostate needle biopsy that predict more advanced cancer at prostatectomy: analysis of 388 biopsy-prostatectomy pairs

We analyzed 5 features on 12-core transrectal ultrasound-guided prostate needle biopsy (TRUS) to predict the extent of cancer at radical prostatectomy (RP). In 388 TRUS-RP pairs, number of positive cores (NPC), percentage of each core involved (%PC), perineural invasion (PNI), Gleason score (GS), distribution of positive cores (DPC), and preoperative prostate-specific antigen (PSA) were correlated with extraprostatic extension (EPE), seminal vesicle invasion (SVI), positive surgical margin (R1), positive lymph nodes (N1), and tumor volume. All features predicted EPE and SVI. NPC, GS, %PC, and PNI strongly predicted R1 status. RP tumor volume was directly proportional to the NPC and %PC. PSA alone and with selected biopsy findings correlated with tumor volume, stage, SVI, and N1 (P < .0001). Contiguous DPC was a significant risk for EPE and SVI (P < .0001) compared with isolated positive cores. Findings at 12-core TRUS along with preoperative PSA reliably predict advanced local disease and have practical value as guides to effective planning for surgical resections.