Search Results (41)

Background: Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. Methods: We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI. Results: Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%. Conclusions: Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions. Full article

Background/Objectives: Radiolabeled biomolecules specifically targeting overexpressed structures on tumor cells hold great potential for prostate cancer (PCa) imaging and therapy. Due to heterogeneous target expression, single radiopharmaceuticals may not detect or treat all lesions, while simultaneously applying two or more radiotracers potentially improves staging, stratification, and therapy of cancer patients. This study explores a dual-tracer SPECT approach using [¹¹¹In]In-RM2 (targeting the gastrin-releasing peptide receptor, GRPR) and [^99mTc]Tc-PSMA-I&S (targeting the prostate-specific membrane antigen, PSMA) as a proof of concept. To mimic heterogeneous tumor lesions in the same individual, we aimed to establish a dual xenograft mouse model for preclinical evaluation. Methods: CHO-K1 cells underwent lentiviral transduction for human GRPR or human PSMA overexpression. Six-to-eight-week-old female immunodeficient mice (NOD SCID) were subsequently inoculated with transduced CHO-K1 cells in both flanks, enabling a dual xenograft with similar target density and growth of both xenografts. Respective dual-isotope imaging and γ-counting protocols were established. Target expression was analyzed ex vivo by Western blotting. Results: In vitro studies showed similar target-specific binding and internalization of [¹¹¹In]In-RM2 and [^99mTc]Tc-PSMA-I&S in transduced CHO-K1 cells compared to reference lines PC-3 and LNCaP. However, in vivo imaging showed negligible tumor uptake in xenografts of the transduced cell lines. Ex vivo analysis indicated a loss of the respective biomarkers in the xenografts. Conclusions: Although the technical feasibility of a dual-tracer SPECT imaging approach using ¹¹¹In and ^99mTc has been demonstrated, the potential of [^99mTc]Tc-PSMA-I&S and [¹¹¹In]In-RM2 in a dual-tracer cocktail to improve PCa diagnosis could not be verified. The animal model, and in particular the transduced cell lines developed exclusively for this project, proved to be unsuitable for this purpose. The in/ex vivo experiments indicated that results from an in vitro model may not necessarily be successfully transferred to an in vivo setting. To assess the potential of this dual-tracer concept to improve PCa diagnosis, optimized in vivo models are needed. Nevertheless, our strategies address key challenges in dual-tracer applications, aiming to optimize future SPECT imaging approaches. Full article

Background: The testosterone-to-PSA (T/PSA) ratio has been proposed as a novel biomarker to enhance the diagnostic specificity of prostate-specific antigen (PSA) in prostate cancer (PCa) detection. The objective of this study was to evaluate the diagnostic performance of the T/PSA ratio in distinguishing PCa from benign conditions in men undergoing prostate biopsy. Materials and Methods: Eighty men who underwent systematic and targeted transrectal prostate biopsy were retrospectively studied. Clinical variables included serum PSA, testosterone, prostate volume, PSA density (PSAD), and the T/PSA ratio. Diagnostic accuracy was assessed using Receiver Operating Characteristic (ROC) curve analysis. Optimal cutoffs were determined using Youden’s index. Results: PCa was diagnosed in 53 patients (66.3%). Median T/PSA was significantly lower in PCa versus non-PCa patients (0.46 vs. 0.86; p < 0.01). T/PSA showed good diagnostic performance (AUC = 0.75) with an optimal cutoff of 0.81 (sensitivity: 59.3%, specificity: 86.8%). In patients with PSA ≤10 ng/mL, T/PSA retained strong discriminatory ability (AUC = 0.76), with sensitivity and specificity of 82.4% and 72.7%, respectively. Among all parameters, PSAD showed the highest diagnostic accuracy (AUC = 0.813). T/PSA was not significantly associated with Gleason score (p = 0.48). Conclusions: The T/PSA ratio is a clinically accessible and cost-effective biomarker that may improve PCa risk stratification and reduce unnecessary biopsies, particularly in patients with borderline PSA levels. Although it does not correlate with tumor aggressiveness, its combination with PSAD could enhance diagnostic accuracy in routine clinical practice. Full article

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs >0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions. Full article

Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time–activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes. Full article

Background/Objectives: The presence of seminal vesicle invasion (SVI) in prostate cancer (PCa) is associated with poorer postoperative outcomes. This study evaluates the predictive value of magnetic resonance imaging (MRI) and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) for SVI in PCa. Methods: This cohort study included consecutive robotic prostatectomy patients for PCa at three Australian tertiary referral centres between April 2016 and September 2022. MRI and PSMA PET/CT results, clinicopathological variables, including age, BMI, prostate-specific antigen (PSA), PSA density, DRE, Biopsy Gleason score, Positive biopsy cores, PIRADS v2.1 score, MRI volume and MRI lesion size were extracted. The sensitivity, specificity, and accuracy of MRI and PSMA PET/CT for predicting SVI were compared with the histopathological results by receiver operating characteristic (ROC) analysis. Subgroup univariate and multivariate analysis was performed. Results: Of the 528 patients identified, 86 had SVI on final pathology. MRI had a low sensitivity of 0.162 (95% CI: 0.088–0.261) and a high specificity of 0.963 (95% CI: 0.940–0.979). The PSMA PET/CT had a low sensitivity of 0.439 (95% CI: 0.294–0591) and a high specificity of 0.933 (95% CI: 0.849–0.969). When MRI and PSMA PET/CT were used in combination, the sensitivity and specificity improved to 0.514 (95%CI: 0.356–0.670) and 0.880 (95% CI: 0.813–0.931). The multivariate regression showed a higher biopsy Gleason score (p = 0.033), higher PSA (p < 0.001), older age (p = 0.001), and right base lesions (p = 0.003) to be predictors of SVI. Conclusions: MRI and PSMA PET/CT independently underpredicted SVI. The sensitivity and AUC improved when they were used in combination. Multiple clinicopathological factors were associated with SVI on multivariate regression and predictive models incorporating this information may improve oncological outcomes. Full article

Serum prostate-specific antigen (PSA), its derivatives, and magnetic resonance tomography (MRI) lack sufficient specificity and sensitivity for the prediction of risk reclassification of prostate cancer (PCa) patients on active surveillance (AS). We investigated selected transcripts in urinary extracellular vesicles (uEV) from PCa patients on AS to predict PCa risk reclassification (defined by ISUP 1 with PSA > 10 ng/mL or ISUP 2-5 with any PSA level) in control biopsy. Before the control biopsy, urine samples were prospectively collected from 72 patients, of whom 43% were reclassified during AS. Following RNA isolation from uEV, multiplexed reverse transcription, and pre-amplification, 29 PCa-associated transcripts were quantified by quantitative PCR. The predictive ability of the transcripts to indicate PCa risk reclassification was assessed by receiver operating characteristic (ROC) curve analyses via calculation of the area under the curve (AUC) and was then compared to clinical parameters followed by multivariate regression analysis. ROC curve analyses revealed a predictive potential for AMACR, HPN, MALAT1, PCA3, and PCAT29 (AUC = 0.614–0.655, p < 0.1). PSA, PSA density, PSA velocity, and MRI maxPI-RADS showed AUC values of 0.681–0.747 (p < 0.05), with accuracies for indicating a PCa risk reclassification of 64–68%. A model including AMACR, MALAT1, PCAT29, PSA density, and MRI maxPI-RADS resulted in an AUC of 0.867 (p < 0.001) with a sensitivity, specificity, and accuracy of 87%, 83%, and 85%, respectively, thus surpassing the predictive power of the individual markers. These findings highlight the potential of uEV transcripts in combination with clinical parameters as monitoring markers during the AS of PCa. Full article

Background: The objective of this study was to investigate factors influencing Gallium 68 Prostate Specific Membrane Antigen Positron Emission Tomography (Ga68 PSMA PET-CT) uptake for primary staging in prostate cancer. Methods: Retrospective analysis was conducted on 499 non-metastatic and 243 de novo metastatic prostate cancer cases undergoing Ga68 PSMA PET-CT. Demographic, clinical, and imaging data were collected and analyzed. Multivariate logistic regression determined independent risk factors for metastasis detection on Ga68 PSMA PET-CT. Results: Metastatic cases showed higher levels of total PSA, PSA density (dPSA) and biopsy ISUP grade group compared to non-metastatic cases. Multivariate analysis identified cT2 stage and dPSA as independent predictors of metastasis detection on Ga68 PSMA PET-CT. Conclusions: Ga68 PSMA PET-CT plays a crucial role in prostate cancer staging, with identified factors such as clinical T stage and dPSA significantly impacting its diagnostic accuracy. These findings underscore the importance of Ga68 PSMA PET-CT in refining clinical staging and guiding treatment decisions for prostate cancer patients. Full article

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy. Full article

High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative ⁶⁸Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of ⁶⁸Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm (n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97–99%), recall (68–81%), Dice coefficient (80–88%) and Jaccard index (67–79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room. Full article

Combining systematic biopsy (SB) with targeted biopsy (TB) in the case of a positive result from multiparametric magnetic resonance imaging (mpMRI) is a matter of debate. The Prostate Imaging Reporting and Data System (PIRADS) score of 5 indicates the highest probability of clinically significant prostate cancer (csPC) detection in TB. Potentially, omitting SB in the case of PIRADS 5 may have a marginal impact on the csPC detection rate. The aim of this study was to determine whether SB can be avoided in the case of PIRADS 5 and to identify potential factors allowing for performing TB only. This cohort study involved n = 225 patients with PIRADS 5 on mpMRI (PIRADS 2.0/2.1) who underwent transperineal or transrectal combined biopsy (CB). CsPC was diagnosed in 51.6% (n = 116/225) of cases. TB and SB resulted in the detection of csPC in 48% (n = 108/225) and 20.4% (n = 46/225) of cases, respectively (TB vs. SB, p < 0.001). When the TB was positive, SB detected csPC in n = 38 of the cases (38/108 = 35%). SB added to TB significantly improved csPC detection in 6.9% of cases in absolute terms (n = 8/116) (TB vs. CB, p = 0.008). The multivariate regression model proved that the significant predictors of csPC detection via SB were the densities of the prostate-specific antigen—PSAD > 0.17 ng/mL² (OR = 4.038, 95%CI: 1.568–10.398); primary biopsy setting (OR = 2.818, 95%CI: 1.334–5.952); and abnormal digital rectal examination (DRE) (OR = 2.746, 95%CI: 1.328–5.678). In a primary biopsy setting (n = 103), SB detected 10% (n = 6/60) of the additional cases of csPC (p = 0.031), while in a repeat biopsy setting (n = 122), SB detected 3.5% (n = 2/56) of the additional cases of csPC (p = 0.5). In the case of PSAD > 0.17 ng/mL² (n = 151), SB detected 7.4% (n = 7/95) of additional cases of csPC (p = 0.016), while in the case of PSAD < 0.17 ng/mL² (n = 74), SB detected 4.8% (n = 1/21) of the additional cases of csPC (p = 1.0). The omission of SB had an impact on the csPC diagnosis rate in patients with PIRADS 5 score lesions. Patients who have already undergone prostate biopsy and those with low PSAD are at a lower risk of missing csPC when SB is avoided. However, performing TB only may result in missing other csPC foci located outside the index lesion, which can alter treatment decisions. Full article

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL²) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI–ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. Results: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). Conclusion: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics’ (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients. Full article

Vasculogenic mimicry (VM), the ability of tumour cells to form functional microvasculature without an endothelial lining, may contribute to anti-angiogenic treatment resistance in glioblastoma. We aimed to assess the extent of VM formation in primary and recurrent glioblastomas and to determine whether VM vessels also express prostate-specific membrane antigen (PSMA), a pathological vessel marker. Formalin-fixed paraffin-embedded tissue from 35 matched pairs of primary and recurrent glioblastoma was immunohistochemically labelled for PSMA and CD34 and stained with periodic acid–Schiff (PAS). Vascular structures were categorised as endothelial vessels (CD34+/PAS+) or VM (CD34−/PAS+). Most blood vessels in both primary and recurrent tumours were endothelial vessels, and these significantly decreased in recurrent tumours (p < 0.001). PSMA was expressed by endothelial vessels, and its expression was also decreased in recurrent tumours (p = 0.027). VM was observed in 42.86% of primary tumours and 28.57% of recurrent tumours. VM accounted for only a small proportion of the tumour vasculature and VM density did not differ between primary and recurrent tumours (p = 0.266). The functional contribution of VM and its potential as a treatment target in glioblastoma require further investigation. Full article

As the incidence of prostate cancer (PCa) has increased, screening based on prostate-specific antigen (PSA) has become controversial due to the low specificity of PSA. Therefore, we investigated the diagnostic performance of prostate health index (PHI) density (PHID) for the detection of PCa and clinically significant PCa (csPCa) compared to PSA, PSA density (PSAD), and PHI as a triaging test. We retrospectively reviewed 306 men who underwent prostate biopsy for PSA levels of 2.5 to 10 ng/mL between January 2020 and April 2023. Of all cohorts, 86 (28.1%) and 48 (15.7%) men were diagnosed with PCa and csPCa, respectively. In ROC analysis, the highest AUC was identified for PHID (0.812), followed by PHI (0.791), PSAD (0.650), and PSA (0.571) for PCa. A similar trend was observed for csPCa: PHID (AUC 0.826), PHI (AUC 0.796), PSAD (AUC 0.671), and PSA (0.552). When the biopsy was restricted to men with a PHID ≥ 0.56, 26.5% of unnecessary biopsies could be avoided; however, 9.3% of PCa cases and one csPCa case (2.1%) remained undiagnosed. At approximately 90% sensitivity for csPCa, at the given cut-off values of PHI ≥ 36.4, and PHID ≥ 0.91, 48.7% and 49.3% of unnecessary biopsies could be avoided. In conclusion, PHID had a small advantage over PHI, about 3.6%, for the reduction in unnecessary biopsies for PCa. The PHID and PHI showed almost the same diagnostic performance for csPCa detection. PHID can be used as a triaging test in a clinical setting to pre-select the risk of PCa and csPCa. Full article

Prostate cancer (PCa) is the second most frequently diagnosed cancer in men. Despite the significant progress in cancer diagnosis and treatment over the last few years, the approach to disease detection and therapy still does not include histopathological biomarkers. The dissemination of PCa is strictly related to the creation of a premetastatic niche, which can be detected by altered levels of specific biomarkers. To date, the risk factors for biochemical recurrence include lymph node status, prostate-specific antigen (PSA), PSA density (PSAD), body mass index (BMI), pathological Gleason score, seminal vesicle invasion, extraprostatic extension, and intraductal carcinoma. In the future, biomarkers might represent another prognostic factor, as discussed in many studies. In this review, we focus on histopathological biomarkers (particularly CD169 macrophages, neuropilin-1, cofilin-1, interleukin-17, signal transducer and activator of transcription protein 3 (STAT3), LIM domain kinase 1 (LIMK1), CD15, AMACR, prostate-specific membrane antigen (PSMA), Appl1, Sortilin, Syndecan-1, and p63) and their potential application in decision making regarding the prognosis and treatment of PCa patients. We refer to studies that found a correlation between the levels of biomarkers and tumor characteristics as well as clinical outcomes. We also hypothesize about the potential use of histopathological markers as a target for novel immunotherapeutic drugs or targeted radionuclide therapy, which may be used as adjuvant therapy in the future. Full article