BACKGROUND: In this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa).
METHODS: Multiple deep learning CNN architectures were developed using the Unet as the baseline. The CNNs use both multiparametric MRI images (T2W, ADC, and High b-value) and quantitative clinical data (prostate specific antigen (PSA), PSA density (PSAD), prostate gland volume & gross tumor volume (GTV)), and only mp-MRI images (n = 118), as input. In addition, co-registered ground truth data from whole mount histopathology images (n = 22) were used as a test set for evaluation.
RESULTS: The CNNs achieved for early/intermediate / late level fusion a precision of 0.41/0.51/0.61, recall value of 0.18/0.22/0.25, an average precision of 0.13 / 0.19 / 0.27, and F scores of 0.55/0.67/ 0.76. Dice Sorensen Coefficient (DSC) was used to evaluate the influence of combining mpMRI with parametric clinical data for the detection of csPCa. We compared the DSC between the predictions of CNN\'s trained with mpMRI and parametric clinical and the CNN\'s trained with only mpMRI images as input with the ground truth. We obtained a DSC of data 0.30/0.34/0.36 and 0.26/0.33/0.34 respectively. Additionally, we evaluated the influence of each mpMRI input channel for the task of csPCa detection and obtained a DSC of 0.14 / 0.25 / 0.28.
CONCLUSIONS: The results show that the decision level fusion network performs better for the task of prostate lesion detection. Combining mpMRI data with quantitative clinical data does not show significant differences between these networks (p = 0.26/0.62/0.85). The results show that CNNs trained with all mpMRI data outperform CNNs with less input channels which is consistent with current clinical protocols where the same input is used for PI-RADS lesion scoring.
BACKGROUND: The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under proposal number Nr. 476/14 & 476/19.

OBJECTIVE: To evaluate the difference in the diagnostic efficacy of 18F-PSMA-1007 PET/CT and pelvic MRI in primary prostate cancer, as well as the correlation between the two methods and histopathological parameters and serum PSA levels.
METHODS: A total of 41 patients with suspected prostate cancer who underwent 18F-PSMA-1007 PET/CT imaging in our department from 2018 to 2023 were retrospectively collected. All patients underwent 18F-PSMA-1007 PET/CT and MRI scans. The sensitivity, PPV and diagnostic accuracy of MRI and 18F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were calculated after comparing the results of MRI and 18F-PSMA-1007 PET/CT with biopsy. The Spearman test was used to calculate the correlation between 18F-PSMA-1007 PET/CT, MRI parameters, histopathological indicators, and serum PSA levels.
RESULTS: Compared with histopathological results, the sensitivity, PPV and diagnostic accuracy of 18F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were 95.1%, 100.0% and 95.1%, respectively. The sensitivity, PPV and diagnostic accuracy of MRI in the diagnosis of prostate cancer were 82.9%, 100.0% and 82.9%, respectively. There was a mild to moderately positive correlation between Gleason (Gs) score, Ki-67 index, serum PSA level and 18F-PSMA-1007 PET/CT parameters (p < 0.05). There was a moderately negative correlation between the expression of AMACR (P504S) and 18F-PSMA-1007 PET/CT parameters (p < 0.05). The serum PSA level and the Gs score were moderately positively correlated with the MRI parameters (p < 0.05). There was no correlation between histopathological parameters and MRI parameters (p > 0.05).
CONCLUSIONS: Compared with MRI, 18F-PSMA-1007 PET/CT has higher sensitivity and diagnostic accuracy in the detection of malignant prostate tumors. In addition, the Ki-67 index and AMACR (P504S) expression were only correlated with 18F-PSMA-1007 PET/CT parameters. Gs score and serum PSA level were correlated with 18F-PSMA-1007 PET/CT and MRI parameters. 18F-PSMA-1007 PET/CT examination can provide certain reference values for the clinical diagnosis, evaluation, and treatment of malignant prostate tumors.

CONCLUSIONS: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.
OBJECTIVE: Illness perception (IP) significantly determines illness outcomes. This study determined the impact of a pharmacist educational intervention on IP in patients with prostate cancer (PCa) and predictors of IP.
METHODS: Using a brief IP questionnaire, an interventional study of patients with PCa was conducted in all cancer reference hospitals in one Nigerian state. After a pre-post assessment of patients\' IP, descriptive and inferential statistical analyses were performed. The impact of pharmacists\' intervention on IP was determined by paired-sample statistics and correlation analysis at the 95% CI. Relationships and predictors of IP were determined using Kendall\'s tau-b (τb), likelihood ratio, and F tests of equality of means, respectively. P < 0.05 was considered statistically significant.
RESULTS: Pharmacists\' educational intervention significantly improved IP (SEM, 0.13; r = 0.875; P < 0.0001) among the 200 participants. The analyses also showed a significant paired sample difference (2.662; SEM, 0.06; 95%CI, 2.536-2.788; t = 41.69; df = 199; P < 0.0001). All subscales of patients\' IP significantly improved except for illness consequences (P = 0.173) and identity (mean [SD], 4.40 [3.730] in both pre- and postintervention assessments). Pre- and postintervention assessments showed a significant negative relationship of IP with age (τb = -110 [P = 0.040] and τb = -14 [P = 0.021], respectively), Gleason score (τb = -0.125 [P = 0.021] and τb = -0.124 [P=0.012], respectively), and age at diagnosis (τb = -0.103 [P = 0.036] post intervention). IP was significantly dependent on the drug therapy (df = 8; mean square [M] = 6.292; F = 2.825; P = 0.006), alcohol intake (df = 1; M = 9.608; F = 4.082; P = 0.045) and Gleason score (df = 9; M = 6.706; F = 3.068; P = 0.002).
CONCLUSIONS: Patients\' IP significantly improved after pharmacists\' educational intervention. Predictors of IP were drug therapies, alcohol use and Gleason score. Findings can be extrapolated in clinical settings to improve treatment outcomes.

The primary objective of this study was to identify predictors for biochemical recurrence (BCR) within 2 years following robot-assisted radical prostatectomy (RARP). Identifying predictors will enable insights that enhance personalized patient management and facilitate the ongoing refinement of postoperative therapy strategies.
This retrospective study included patients undergoing RARP from September 2014 to January 2021. Exclusion criteria were preoperative endocrine therapy, BCR beyond 2 years post-surgery, and incomplete postoperative data. Multivariate analyses were conducted to evaluate predictors of BCR, focusing on preoperative prostate-specific antigen (PSA) level, pathological tumor (pT) stage, Gleason score (GS), extraprostatic extension (EPE), and surgical margin status.
Among 374 patients, 40 experienced BCR within 2 years. Significant predictors of early BCR included initial PSA level ≥10 ng/ml, pT3 or greater, GS ≥8, EPE, and positive surgical margins (RM1). Multivariate analysis identified pT3 or higher, GS ≥8, and RM1 as independent risk factors for early BCR.
Early BCR after RARP is significantly associated with advanced pathological stage, high GS, and positive surgical margins. These findings emphasize the need for tailored postoperative management strategies and highlight the importance of precision in surgical technique to improve patient outcomes.

UNASSIGNED: Tobacco contains harmful carcinogens that have been associated with cancers. Some studies have associated tobacco smoking with prostate cancer (PCa). The relationship between alcohol consumption as a risk factor for prostate cancer has been debated. Some studies associated alcohol consumption with increased risk of PCa, associating alcohol consumption with higher-grade cancers and poorer prognosis. Other studies have found a minimal relationship with PCa, with some even suggesting that alcohol consumption may even be protective. This study evaluates the association between smoking and alcohol consumption in prostate cancer patients.
UNASSIGNED: This is a retrospective study on one hundred and fifty-two patients diagnosed with prostate cancer with a known history of both smoking and or alcohol consumption managed over a 9year period from January 2012 to December 2020 from three Urology referrals hospitals. Patients with incomplete history were excluded. Their data such as age, a history of cigarette smoking, prostate-specific antigen level, prostate biopsy histopathology reports, and Gleason\'s grade were extracted. This was coded into Microsoft Excel and analyzed with SPSS version 20. The results were analyzed and presented in tables and charts.
UNASSIGNED: One hundred and thirty-five patients had a premorbid history of smoking and alcohol consumption with a mean age of 69 years and a modal age in the 70-79-year age group. Fifty-three (39.3%) of the patients had a history of cigarette smoking, ninety-four (69.6%) had a history of alcohol consumption. In comparison, fifty-one (37.8%) had a history of cigarette smoking and alcohol consumption. The high-risk Gleason\'s 8-10 prostate cancer was commoner among smokers than nonsmokers. There was no statistically significant association between cigarette smoking and alcohol consumption alone and combined with PCa.
UNASSIGNED: The high-risk Gleason\'s 8-10 prostate cancer was commoner among smokers than nonsmokers. There was no statistically significant association between cigarette smoking and alcohol consumption and the risk of prostate cancer.

BACKGROUND: This work aimed to investigate the potential role of abnormal lipid metabolism in the development of prostate cancer (PCa).
METHODS: A retrospective study design was used. The clinical data of 520 patients who underwent rectal prostate biopsy in our hospital from January 2020 to June 2023 were analysed. The patients were enrolled and divided into the anterior PCa group including 112 patients and benign prostatic hyperplasia (BPH) group including 408 patients. Univariate and multivariate logistic regression analyses were performed for the two patient groups, and further comparisons were made according to the Gleason score and TNM staging.
RESULTS: Low-density lipoprotein cholesterol (LDL-C) level may be an independent risk factor for PCa, and it was significantly associated with the risk of PCa (odds ratio (OR) = 1.363, p = 0.030). Patients with PCa were further divided into the low risk group and the high risk group according to the Gleason score. Univariate analysis (p = 0.047) and logistic regression analysis (OR = 2.249, p = 0.036) revealed that LDL-C was a significant factor influencing the Gleason score. Patients with PCa were categorised into four groups based on TNM staging. One-way analysis of variance (ANOVA) analysis (p = 0.015) and ordinal logistic regression analysis (OR = 2.414, p = 0.007) demonstrated that LDL-C was a significant factor influencing TNM staging.
CONCLUSIONS: This study revealed the important role of LDL-C in the development of PCa, highlighting its influence as an independent risk factor. Thus, LDL-C may promote the proliferation and invasion of PCa cells.

BACKGROUND: Studies in recent years have shown that ribosome-binding protein-1 (RRBP1) is expressed at high rates in many cancers and that it may be a potential prognostic biomarker. The objective of the present study is to determine the RRBP1 expression level in prostatic carcinoma and neighboring non-neoplastic prostate tissue, the relationship between its expression level with prognostic factors, and the role of RRBP1 in the development of prostate cancer.
METHODS: The study included 45 patients who were diagnosed with prostatic carcinoma and underwent radical prostatectomy in our center between the years 2010 and 2021. Pathology reports were reviewed. Mann-Whitney U test was used for the comparison of RRBP1 and GADPH values of the cases (control and tumoral tissue) between the primary tumor stage (pT) and Gleason score (GS) groups. Hierarchical regression analysis was used to explain the effective variables in explaining the RRBP1 value of the research cases.
RESULTS: According to the Mann-Whitney U test, mean and median RRBP1-T values of the cases with GS ≥ 8 were detected to be statistically significantly higher than the mean and median RRBP1-T values of the cases with GS < 8.
CONCLUSIONS: We found out that RRBP1 was expressed at higher rates in patients with high GS and advanced-stage patients. This result indicated that RRBP1 expression may be important in predicting the prognosis of prostate carcinoma.

OBJECTIVE: The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.
METHODS: A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.
RESULTS: The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.
CONCLUSIONS: MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

OBJECTIVE: To evaluate the International Society of Urological Pathology (ISUP) 5-tier grade grouping (GG) system of prostate cancers as well as previously proposed optimizations.
METHODS: The PROCURE biobank is a prospective cohort study of patients with localized prostate cancer who underwent radical prostatectomy in Quebec province between 2005 and 2013. Surgical specimens were graded by experienced genitourinary pathologists using 2019 ISUP criteria. Follow-up was conducted until November 2021. The current 5-tier and a proposed 6-tier GG system were evaluated, the latter having two changes: 1) Gleason 3 + 4 and 4 + 3 tumors with minor/tertiary Gleason 5 patterns were upgraded to GG 3 and 4, respectively; and 2) patients in GG5 were separated based on primary Gleason pattern (4 or 5). Cox proportional hazards models and Harrell\'s concordance (C) indices were used for statistical analyses.
RESULTS: 2003 patients were included (median follow-up: 8.7 years). The current 5-tier GG system predicted time to recurrence (hazard ratio [HR] 2.12, 95% confidence interval [95%CI] 1.99-2.25, C 0.717), androgen-deprivation therapy (HR 2.58, 95%CI 2.38-2.80, C 0.790), metastasis (HR 2.48, 95%CI 2.17-2.83, C 0.806), castration-resistant prostate cancer (HR 2.67, 95%CI 2.28-3.13, C 0.829), and cancer-specific mortality (HR 2.80, 95%CI 2.27-3.44, C 0.835). Goodness-of-fit further improved with the proposed 6-tier GG system, with Harrell\'s C of 0.733, 0.807, 0.827, 0.853, and 0.853, respectively.
CONCLUSIONS: The 5-tier GG system predicted short- and long-term outcomes for patients with localized prostate cancer, and the proposed 6-tier GG system further improved its accuracy.