Prostate Cancer Risk Stratification Research Articles

Comparative analysis of extracellular vesicles miRNAs (EV-miRNAs) and cell-free microRNAs (cf-miRNAs) reveals that EV-miRNAs are more promising as diagnostic and prognostic biomarkers for prostate cancer.

MicroRNAs can be found intracellularly incorporated into extracellular vesicles (EV-miRNAs) or extracellularly as cell-free miRNAs (cf-miRNAs). This study aimed to compare the diagnostic and prognostic potential of four miRNAs with recognized roles in prostate cancer as cf-miRNAs and EV-miRNAs, obtained from liquid biopsies (LB). Total RNA was isolated from whole plasma and plasma EVs from 15 controls (CTR) and 30 patients (20 with localized prostate cancer (PCa), 10 with metastatic prostate cancer (mPCa)). The miRNAs were quantified by RT-qPCR and the relative expression of these miRNAs was compared between the three groups, and their associations with clinicopathological parameters were assessed. Receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic potential of the miRNAs in discriminating different groups. Overall, EV-miRNAs showed higher expression compared to cf-miRNAs. All EV-miRNAs analyzed showed diagnostic potential with an area under the curve (AUC) above 0.744. EV-miR-21-5p, EV-miR-375-3p, and EV-miR-1290-3p were overexpressed in PCa and mPCa compared to CTR, while EV-miR-200c-3p was overexpressed only in mPCa in comparison to CTR. Remarkably, EV-miR-375-3p and EV-miR-1290-3p could differentiate mPCa with ISUP ≥ 3, demonstrating their prognostic potential. In addition, EV-miR-1290-3p and EV-4-miR-panel detected patients with PSA > 10 ng/mL. Cf-miRNAs performed lower than EV-miRNAs, which can be explained by the greater stability and specificity of EV-miRNAs, making them superior to cf-miRNA. The results show that LB, a non-invasive strategy, is clinically feasible to identify EV-miRNAs as biomarkers for PCa and may provide additional information for assessing PCa risk stratification.

Diagnostic Value of Radiomics Based on Various Diffusion Models in Magnetic Resonance Imaging for Prostate Cancer Risk Stratification.

The use of Magnetic Resonance Imaging (MRI) and radiomics improves the management of Prostate Cancer (PCa) and helps in differentiating between clinically insignificant and significant PCa. This study has explored the diagnostic value of radiomic analysis based on functional parameter maps from monoexponential and diffusion kurtosis models in MRI for differentiating between clinically insignificant and significant PCa. In total, 105 PCa cases, including 38 clinically insignificant and 67 clinically significant PCa cases, were retrospectively analyzed. The patients were randomly divided into training and testing sets in a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed, and 1,352 radiomic features were extracted from ADC, MD, and MK images. Clinical, radiomic, and clinical-radiomic models were developed and compared using receiver operating characteristic curve analysis, decision curve analysis, and calibration curves. Clinical variables, such as TPSA, PI-RADS, and FPSA, were identified as independent risk factors for differentiating between clinically insignificant and significant PCa. In radiomics, three features were identified as highly weighted indicators. The clinical-radiomic model based on the clinical and radiomic features demonstrated the highest predictive efficacy for clinically insignificant and significant PCa, with area under the curve values of 0.940 and 0.861 in the training and test sets, respectively. The predictive model constructed from clinical and radiomic features exhibited substantial diagnostic differentiation capabilities for clinically insignificant and significant PCa. The clinical-radiomic model displayed the highest predictive performance, promising significant contributions to future clinical treatment and assessment of PCa.

G-Protein-Coupled Receptor-Associated Sorting Protein 1 Overexpression Is Involved in the Progression of Benign Prostatic Hyperplasia, Early-Stage Prostatic Malignant Diseases, and Prostate Cancer.

Background/Objectives: Prostate cancer (PCa) is a prevalent malignancy, necessitating accurate diagnostic methods to distinguish it from benign conditions such as benign prostatic hyperplasia (BPH). Current diagnostic tools, relying primarily on serum prostate-specific antigen (PSA) levels, lack specificity, leading to an over-diagnosis and unnecessary treatment of patients with benign conditions. This study explores G-protein-coupled receptor-associated sorting protein 1 (GASP-1) as a more sensitive biomarker for PCa detection. Methods: Prostate tissue microarrays of healthy, BPH, and prostate cancer patients with different Gleason scores were studied. Polyclonal antibodies targeted against GASP-1 were used for routine immunohistochemistry (IHC) and enzyme-linked immunosorbent assay (ELISA) analyses. Results: The results indicated a 5-fold difference in serum GASP-1 levels between BPH and PCa, which was validated through GASP-1 IHC. Furthermore, a novel scoring system, the H-score, assesses GASP-1 granules' intensity and size, revealing a clear distinction between BPH and PCa. An additional analysis of GASP-1 expression between PCa cases with different Gleason scores reveals that GASP-1 overexpression correlates with PCa severity, providing insights into disease progression. Conclusions: The study supports GASP-1's role as a promising diagnostic marker, supplementing PSA testing, and offering improved risk stratification for PCa. Additionally, an open-source software system is introduced for an efficient GASP-1 granule color analysis, enhancing diagnostic accuracy.

Study of HOXB13 Gene Variants in Prostate Cancer Patients.

Prostate cancer (PC) is a prevalent malignancy with a significant hereditary component. The HOXB13 gene, encoding a transcription factor involved in prostate development, has been implicated in PC risk. The objective of this study was to assess the existence of HOXB13 mutations in PC patients. The retrospective study included 33 PC patients and 23 controls. Demographic data, family history, and smoking habits were recorded. Prostate-specific antigen (PSA) levels were measured. We investigated the second exon of HOXB13 after extracting genomic DNA from blood samples for mutations using polymerase chain reaction and Sanger sequencing. PC patients had a higher mean age (64.7 years), more frequent positive family history (63.64%, N = 21), and higher smoking prevalence (60.61%, N = 20) compared to controls. PSA levels were significantly elevated in patients (76.58 ng/ml) versus controls (7.22 ng/ml). HOXB13 mutations, including thymine (3.03%, N = 1), guanine (27.27%, N = 9), and adenine (33.33%, N = 11) mutations, were observed in patients, while no mutations were found in controls. PC patients had higher mean age, more positive family histories, higher smoking rates, and elevated PSA levels. HOXB13 mutations were significantly higher in patients compared to controls. These findings emphasize the roles of HOXB13, age, family history, smoking, and PSA in PC risk stratification.

Optimizing risk stratification for intermediate-risk prostate cancer – the prognostic value of baseline health-related quality of life

ObjectiveTo investigate the prognostic value of baseline health-related quality of life (HRQOL) for patients with intermediate-risk localized prostate cancer (IR-PCa) undergoing radical prostatectomy (RP).Methods4780 patients with IR-PCa according to NCCN risk stratification were identified from a prospectively maintained database. All patients were treated with RP and had prospectively assessed baseline HRQOL. Main outcomes were oncologic endpoints metastasis-free survival (MFS); biochemical recurrence free survival (BRFS) and overall survival (OS). Multivariable Cox regression models assessed prognostic significance of baseline global health status (GHS) on survival outcomes. Harrell’s discrimination C-index was applied to calculate the predictive accuracy of the model. Decision curve analysis (DCA) tested the clinical net benefit associated with adding the GHS domain to our multivariable model (p < 0.05).ResultsMedian follow-up was 51 months. Multivariable analysis confirmed baseline GHS as an independent predictor for increased MFS (HR 0.976, 95%CI 0.96–0.99; p < 0.001), increased BRFS (HR 0.993, 95%CI 0.99–1.00; p = 0.027) and increased OS (HR 0.969, 95%CI 0.95–0.99; p = 0.002), indicating a relative risk reduction of 2.4% for MFS, 0.7% for BRFS and 3.1% for OS per 1-point increase of baseline GHS. Baseline HRQOL improved discrimination in predicting MFS, BRFS and OS. DCA revealed a net benefit over all threshold probabilities.ConclusionsWe found baseline HRQOL to substantially improve risk stratification for the heterogeneous cohort of IR-PCa. Baseline HRQOL accurately predicts increased MFS, BRFS and OS. Our findings therefore support the role of preoperative HRQOL as an adjunct to established prognosticators for IR-PCa, potentially facilitating guidance of therapy.

The Impact of Baseline Endogenous Testosterone Levels on Risk Stratification in Pathological Organ-Confined Prostate Cancer: Results in 460 Patients Treated with Robot-Assisted Radical Prostatectomy

The Impact of Baseline Endogenous Testosterone Levels on Risk Stratification in Pathological Organ-Confined Prostate Cancer: Results in 460 Patients Treated with Robot-Assisted Radical Prostatectomy

Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multimodal Deep Learning With Digital Histopathology.

Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups. The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups. The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively. The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification.

The aim of this study is to investigate the role of [18F]-PSMA-1007 PET in differentiating high- and low-risk prostate cancer (PCa) through a robust radiomics ensemble model. This retrospective study included 143 PCa patients who underwent [18F]-PSMA-1007 PET/CT imaging. PCa areas were manually contoured on PET images and 1781 image biomarker standardization initiative (IBSI)-compliant radiomics features were extracted. A 30 times iterated preliminary analysis pipeline, comprising of the least absolute shrinkage and selection operator (LASSO) for feature selection and fivefold cross-validation for model optimization, was adopted to identify the most robust features to dataset variations, select candidate models for ensemble modelling, and optimize hyperparameters. Thirteen subsets of selected features, 11 generated from the preliminary analysis plus two additional subsets, the first based on the combination of robust and fine-tuning features, and the second only on fine-tuning features were used to train the model ensemble. Accuracy, area under curve (AUC), sensitivity, specificity, precision, and f-score values were calculated to provide models' performance. Friedman test, followed by post hoc tests corrected with Dunn-Sidak correction for multiple comparisons, was used to verify if statistically significant differences were found in the different ensemble models over the 30 iterations. The model ensemble trained with the combination of robust and fine-tuning features obtained the highest average accuracy (79.52%), AUC (85.75%), specificity (84.29%), precision (82.85%), and f-score (78.26%). Statistically significant differences (p < 0.05) were found for some performance metrics. These findings support the role of [18F]-PSMA-1007 PET radiomics in improving risk stratification for PCa, by reducing dependence on biopsies.

Application of proton density fat fraction imaging in risk stratification of prostate cancer.

Prostate cancer (PCa) as one of the most prevalent malignancies in men. We introduced a non-invasive quantitative measurement of intraprostatic fat content based on magnetic resonance proton density fat fraction (PDFF) imaging. The study aims to determine the fat fraction (FF) of PCa using proton density magnetic resonance imaging (MRI), gather clinical and routine MRI characteristics, and identify risk factors for high-risk PCa through multifactorial logistic regression. Clinical and imaging data from 191 pathologically confirmed PCa patients were collected. Patients were stratified based on Gleason score (GS), with 63 in the intermediate- and low-risk group (GS =3+3, 3+4) and 128 in the high-risk group (GS ≥4+3). All patients underwent routine prostate MRI and FF imaging. Clinical and imaging data related to PCa were analyzed, including age, body mass index (BMI), prostate volume (PV) measured by MRI, smoking history, alcohol history, diabetes history, serum prostate-specific antigen (PSA) level, apparent diffusion coefficient (ADC) value, T2 signal intensity (T2SI), Prostate Imaging Reporting and Data System 2.1 (PI-RADS 2.1) score, GS, lesion FF, whole gland FF, periprostatic fat thickness (PPFT), and subcutaneous fat thickness (SFT). Independent risk factors for stratifying PCa risk were identified through multivariate logistic regression analysis, and a predictive model was established. Receiver operating characteristic (ROC) curve analysis was conducted for visual analysis. Significant differences were found in BMI, PV, PSA, tumor ADC value, standard T2SI, PI-RADS score, lesion FF, and PPFT between low- and medium-risk and high-risk groups (P<0.05). No significant differences were observed in age, smoking history, drinking history, diabetes history, and SFT between the two groups (P>0.05). GS correlated significantly with FF (ρ=0.6, P<0.001), PSA (ρ=0.432, P<0.001), ADC value (ρ=-0.379, P<0.001), and PI-RADS (ρ=0.366, P<0.001). Multiple logistic regression analysis revealed that an increase in FF, a PI-RADS score increase of 5 points, and a decrease in ADC value and PV were independent predictors of high-risk PCa (P<0.05). The ROC curve showed that the best cut-off value for the model was 0.67, with an area under the curve (AUC) of 0.907, sensitivity of 78.1%, and specificity of 88.9%. The FF of PCa determined by proton density MRI is significantly associated with GS, serving as an independent predictor of high-risk PCa.

Association of Prostate-Specific Antigen With Age, Digital Rectal Examination, and Lower Urinary Tract Symptoms in the Lebanese Population: A Cross-Sectional Study.

Prostate cancer (PCa) is a leading cause of mortality in men worldwide. Prostate-specific antigen (PSA) testing is a standard method for PCa detection, yet its associationwith age, digital rectal examination (DRE) results, and lower urinary tract symptoms (LUTS) remains understudied, particularly in the Lebanese population. This study aimed to investigate the associationof PSA levels with age, DRE results, and LUTS severity among Lebanese men. A total of 725 men aged 55-70 years were recruited from a men's health campaign at Saint George Hospital University Medical Center in Lebanon. PSA levels, DRE results, andInternational Prostate Symptom Score(IPSS)were assessed. Statistical analysis included Kruskal-Wallis tests and Spearman's rho correlation coefficient. Participants exhibited a significant correlation between age and PSA levels (r = 0.138, p < 0.01). PSA levels varied significantly across age groups (p = 0.029), with higher mean PSA levels observed in older age groups. IPSS status correlated positively with PSA levels (r = 0.23, p < 0.001), indicating higher PSA levels associated with increased LUTS severity. Abnormal DRE findings were significantly associated with elevated PSA levels (p < 0.00), suggesting their potential as an indicator of prostate abnormalities. This study highlights the importance of age-specific reference ranges for PSA levels in the Lebanese population. Elevated PSA levels were associated with older age, increased LUTS severity, and abnormal DRE findings. These findings highlight the significance of integrating PSA testing with clinical assessments for PCa detection and risk stratification in Lebanon.

Do Prostate Imaging-Reporting and Data System (PIRADS) lesions predict holmium laser enucleation of prostate outcomes?

Prostate magnetic resonance imaging (MRI) is used for prostate cancer (PCa) screening and risk stratification and is helpful for surgical planning for patients undergoing holmium laser enucleation of the prostate (HoLEP). There are few studies investigating the correlation between MRI Prostate Imaging-Reporting and Data System (PIRADS) lesion characteristics and HoLEP pathology and outcomes. We performed retrospective review of patients who underwent HoLEP between January 2021 and August 2023 by a single surgeon. Preoperative, intraoperative, and postoperative characteristics and outcomes were analyzed for all patients who had a documented preoperative prostate MRI. There were 334 patients without a pre-existing diagnosis of PCa and with a preoperative prostate MRI, of which 140 (42%) had at least one PIRADS lesion. There was a total of 203 PIRADS lesions: 91 (45%) in the peripheral zone (PZ), 106 (52%) in the transition zone (TZ), and 6 (2%) not specified. Incidental PCa was noted in 44 (13%) patients at time of HoLEP. Presence or location of lesion was not significantly associated with rate or grade of incidental PCa on pathology. Greater number of lesions and lesion size correlated with longer procedure times. Lesion number, size, or grade were not found to correlate with cancer grade or rate of cancer. Grade, presence, location, size, and number of PIRADS lesions on preoperative prostate MRI for patients with an appropriate prior PCa workup were not significantly associated with incidental PCa or higher PCa grade on HoLEP pathology.

Utility of PSMA-PET derived volumetric parameters in initial risk stratification and prediction of prostate cancer metastasis - a head-to-head comparison of the radiotracers 18F-PSMA-1007 and 68Ga-PSMA-11.

This study aimed to explore and compare the utility of baseline 18F-PSMA-1007 and 68Ga-PSMA-11 PET/computed tomography (CT) derived volumetric parameters in initial risk stratification and prediction of prostate cancer (PCa) metastasis. Forty treatment-naïve, biopsy-proven intermediate-/high-risk PCa patients were prospectively recruited. Each patient underwent PET/CT with 68Ga-PSMA-11 and 18F-PSMA-1007 (within 2 weeks). The maximum and mean standardized uptake values (SUVmax and SUVmean) of primary tumor, prostate PSMA-tumor volume (PSMA-TVp), and prostate total lesion PSMA (TL-PSMAp) were measured. PSMA-TVp and TL-PSMAp (with both radiotracers) mostly exhibited moderate-to-strong correlation with Gleason score, serum prostate-specific antigen level and clinical tumor stage (Spearman ρ = 0.361-0.783, P-values ≤0.022). Primary tumor SUVmax values were similar across initial risk categories. PSMA-TVp and TL-PSMAp, however, were significantly higher in high-risk PCa compared to intermediate-risk PCa (P-values ≤0.001). Receiver operating characteristic (ROC) curve analysis revealed that F-PSMA-TVp, Ga-PSMA-TVp, F-TL-PSMAp, and Ga-TL-PSMAp (optimal cutoff values of 20.9, 23.4, 142.5, and 144.8, respectively) could effectively differentiate high-risk from intermediate-risk PCa [area under the ROC curve (AUCs) 0.859-0.898, P-values <0.001] with high sensitivity (~68.8-75%) and excellent specificity (100%). PSMA-TVp and TL-PSMAp (with both radiotracers) could predict presence of regional and extraregional nodal metastasis (AUCs 0.703-0.801, P-values ≤0.03) with moderate sensitivity (~47.8-70.6%) and excellent specificity (~82.6-94.1%). Our results suggest that baseline PSMA-PET primary tumor volumetric parameters provide a noninvasive, objective, and accurate index for initial risk stratification and can predict presence of regional and extraregional nodal metastasis in PCa patients. Larger studies are warranted to evaluate their incremental role over conventional parameters.

Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm.

Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.3, P = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio = 1.7, P = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS.

MR Molecular Imaging of Extradomain-B Fibronectin for Assessing Progression and Therapy Resistance of Prostate Cancer.

Accurate assessment and characterization of the progression and therapy response of prostate cancer are essential for precision healthcare of patients diagnosed with the disease. MRI is a clinical imaging modality routinely used for diagnostic imaging and treatment planning of prostate cancer. Extradomain B fibronectin (EDB-FN) is an oncofetal subtype of fibronectin highly expressed in the extracellular matrix of aggressive cancers, including prostate cancer. It is a promising molecular target for the detection and risk-stratification of prostate cancer with high-resolution MR molecular imaging (MRMI). In this study, we investigated the effectiveness of MRMI with an EDB-FN specific contrast agent MT218 for assessing the progression and therapy resistance of prostate cancer. Low grade LNCaP prostate cancer cells became an invasive phenotype LNCaP-CXCR2 with elevated EDB-FN expression after acquisition of the C-X-C motif chemokine receptor 2 (CXCR2). MT218-MRMI showed brighter signal enhancement in LNCaP-CXCR2 tumor xenografts with a ∼2-fold contrast-to-noise (CNR) increase than in LNCaP tumors in mice. Enzalutamide-resistant C4-2-DR prostate cancer cells were more invasive, with higher EDB-FN expression than parental C4-2 cells. Brighter signal enhancement with a ∼2-fold CNR increase was observed in the C4-2-DR xenografts compared to that of C4-2 tumors in mice with MT218-MRMI. Interestingly, when invasive PC3 prostate cancer cells developed resistance to paclitaxel, the drug-resistant PC3-DR cells became less invasive with reduced EDB-FN expression than the parental PC3 cells. MT218-MRMI detected reduced brightness in the PC3-DR xenografts with more than 2-fold reduction of CNR compared to PC3 tumors in mice. The signal enhancement in all tumors was supported by the immunohistochemical staining of EDB-FN with the G4 monoclonal antibody. The results indicate that MRMI of EDB-FN with MT218 has promise for detection, risk stratification, and monitoring the progression and therapy response of invasive prostate cancer.

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage

Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood. To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US. In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024. Diagnosis of prostate cancer. The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard. A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy. This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning.

Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various clinical and pathologic parameters including Gleason grading, which suffers from a high interobserver variability. In this study, we determine whether objective machine learning (ML)-driven histopathology image analysis would aid us in better risk stratification of PCa. We propose a deep learning, histopathology image-based risk stratification model that combines clinicopathologic data along with hematoxylin and eosin- and Ki-67-stained histopathology images. We train and test our model, using a five-fold cross-validation strategy, on a data set from 502 treatment-naïve PCa patients who underwent radical prostatectomy (RP) between 2000 and 2012. We used the concordance index as a measure to evaluate the performance of various risk stratification models. Our risk stratification model on the basis of convolutional neural networks demonstrated superior performance compared with Gleason grading and the Cancer of the Prostate Risk Assessment Post-Surgical risk stratification models. Using our model, 3.9% of the low-risk patients were correctly reclassified to be high-risk and 21.3% of the high-risk patients were correctly reclassified as low-risk. These findings highlight the importance of ML as an objective tool for histopathology image assessment and patient risk stratification. With further validation on large cohorts, the digital pathology risk classification we propose may be helpful in guiding administration of adjuvant therapy including radiotherapy after RP.

Agent orange exposure and prostate cancer risk in the million veteran program.

The US government considers veterans to have been exposed to Agent Orange if they served in Vietnam while the carcinogen was in use, and these veterans are often deemed at high risk of prostate cancer (PCa). Here, we assess whether presumed Agent Orange exposure is independently associated with increased risk of any metastatic or fatal PCa in a diverse Veteran cohort still alive in the modern era (at least 2011), when accounting for race/ethnicity, family history, and genetic risk. Participants in the Million Veteran Program (MVP; enrollment began in 2011) who were on active duty during the Vietnam War era (August 1964-April 1975) were included (n = 301,470). Agent Orange exposure was determined using the US government definition. Genetic risk was assessed via a validated polygenic hazard score. Associations with age at diagnosis of any PCa, metastatic PCa, and death from PCa were assessed via Cox proportional hazards models. On univariable analysis, exposure to Agent Orange was not associated with increased PCa (hazard ratio [HR]: 1.02, 95% confidence interval [CI]: 1.00-1.04, p = 0.06), metastatic PCa (HR: 0.98, 95% CI: 0.91-1.05, p = 0.55), or fatal PCa (HR: 0.94, 95% CI: 0.79-1.09, p = 0.41). When accounting for race/ethnicity and family history, Agent Orange exposure was independently associated with slightly increased risk of PCa (HR: 1.06, 95% CI: 1.04-1.09, <10-6) but not with metastatic PCa (HR: 1.07, 95% CI: 0.98-1.15, p = 0.10) or PCa death (HR: 1.02, 95% CI: 0.83-1.23, p = 0.09). Similar results were found when accounting for genetic risk. Agent Orange exposure history may not improve modern PCa risk stratification.

Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication.

The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85-0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719-0.84 and 0.84-0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698-0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized.

Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters.

Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment. Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age. The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2. This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.

Increased α2,3-sialyl N-glycosylated prostate-specific membrane antigen (PSMA) in post-DRE urine is associated with high grade group prostate cancer.

Aberrant glycosylation of proteins is an important hallmark in multiple cancers. Prostate-specific membrane antigen (PSMA), a highly glycosylated protein with 10 N-linked glycosylation sites, is an Food and Drug Administration approved theranostic for prostate cancer. However, glycosylation changes in PSMA that are associated with prostate cancer disease progression have not been fully characterized. We investigated whether urinary PSMA sialylation correlate with high-grade prostate cancer. Urine samples were collected from men after digital rectal examination (DRE) before prostate biopsy. Lectin-antibody enzyme-linked immunoassay was used to quantify α2,3-sialyl PSMA in post-DRE urine samples from subjects with benign prostate tumors, Grade Group 1 prostate cancer and those with Grade Group ≥2 disease. There are significant increases in α2,3-sialylated PSMA in patients with Grade Group ≥2 disease compared to benign (p = 0.0009) and those with Grade Group 1 disease (p = 0.0063). There were no significant differences in α2,3-sialyl PSMA levels between Grade Group 1 and benign prostate tumors (p = 0.7947). Our study shows that there are significant differences in the abundance of α2,3-sialylated PSMA in post-DRE urines from disease stratified prostate cancer patients, and the increase is correlated with progression and disease severity. The detection of increased PSMA sialyation in post-DRE urines from patients with higher Grade Group ≥2 disease states provides novel untapped potential for the development of prognostic biomarkers for prostate cancer. Specifically, quantitation of α2,3-sialylated PSMA shows potential for discriminating between benign to intermediate grade disease, which is a significant clinical challenge in staging and risk stratification of prostate cancer.