Using a novel PSMA-PET and PSA-based model to enhance the diagnostic accuracy for clinically significant prostate cancer and avoid unnecessary biopsy in men with PI-RADS ≤ 3 MRI.

INTRODUCTION: The diagnostic evaluation of men with suspected prostate cancer (PCa) yet inconclusive MRI (PI-RADS ≤ 3) presents a common clinical challenge. [68Ga]Ga-labelled prostate-specific membrane antigen ([68Ga]Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has shown promise in identifying clinically significant PCa (csPCa). We aim to establish a diagnostic model incorporating PSMA-PET to enhance the diagnostic process of csPCa in PI-RADS ≤ 3 men. MATERIALS AND METHODS: This study retrospective included 151 men with clinical suspicion of PCa and PI-RADS ≤ 3 MRI. All men underwent [68Ga]Ga-PSMA PET/CT scans and ultrasound/MRI/PET fusion-guided biopsies. csPCa was defined as Grade Group ≥ 2. PRIMARY-scores from PSMA-PET scans were evaluated. A diagnostic model incorporating PSMA-PET and prostate-specific antigen (PSA)-derived parameters was developed. The discriminative performance and clinical utility were compared with conventional methods. Internal validation was conducted using a fivefold cross-validation with 1000 iterations. RESULTS: In this PI-RADS ≤ 3 cohort, areas-under-the-curve (AUCs) for detecting csPCa were 0.796 (95%CI, 0.738-0.853), 0.851 (95%CI, 0.783-0.918) and 0.806 (95%CI, 0.742-0.870) for PRIMARY-score, SUVmax and routine clinical PSMA-PET assessment, respectively. The diagnostic model comprising PRIMARY-score, SUVmax and serum free PSA/total PSA (fPSA/tPSA) achieved a significantly higher AUC of 0.906 (95%CI, 0.851-0.961) compared to strategies based on PRIMARY-score or SUVmax (P < 0.05) and markedly superior to conventional strategies typically based on PSA density (P < 0.001). The average fivefold cross-validated AUC with 1000 iterations was 0.878 (95%CI, 0.820-0.954). Theoretically, using a threshold of 21.6%, the model could have prevented 78% of unnecessary biopsies while missing only 7.8% of csPCa cases in this cohort. CONCLUSIONS: A novel diagnostic model incorporating PSMA-PET derived metrics-PRIMARY-score and SUVmax-along with serum fPSA/tPSA, has been developed and validated. The integrated model may assist clinical decision-making with enhanced diagnostic accuracy over the individual conventional metrics. It has great potential to reduce unnecessary biopsies for men with PI-RADS ≤ 3 MRI results and warrants further prospective and external evaluations.

Diagnostic Value Analysis of PI-RADS v2.1 Combined with ADC Values in the Risk Stratification of Prostate Cancer Gleason Scores: A Retrospective Study.

BACKGROUND: Prostate cancer is a remarkable global health concern, necessitating accurate risk stratification for optimal treatment and outcome prediction. By highlighting the potential of imaging-based approaches to improve risk assessment in prostate cancer, this research aims to evaluate the diagnostic efficacy of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 combined with apparent diffusion coefficient (ADC) values to gain increased context within the broad landscape of clinical needs and advancements in prostate cancer management. METHODS: The clinical data of 145 patients diagnosed with prostate cancer were retrospectively analysed. The patients were divided into low-moderate- and high-risk groups on the basis of Gleason scores. PI-RADS v2.1 scores were assessed by senior radiologists and ADC values were calculated by using diffusion-weighted imaging. Statistical, univariate logistic regression, and receiver operating characteristic curve analyses were employed to evaluate the diagnostic efficacy of each index and combined PI-RADS v2.1 scores and ADC values. RESULTS: This study found significant differences in PI-RADS v2.1 scores and ADC values between the low-moderate- and high-risk groups (p < 0.001). Logistic regression analysis revealed associations of various clinical indicators, PI-RADS score and ADC values with Gleason risk classification. Amongst indices, mean ADC demonstrated the highest sensitivity (0.912) and area under curve (AUC) value (0.962) and the combination of PI-RADS v2.1 with mean ADC showed high predictive value for the Gleason risk grading of prostate cancer with a high AUC value (0.966). CONCLUSIONS: This study provides valuable evidence for the potential utility of imaging-based approaches, specifically PI-RADS v2.1 combined with ADC values, in enhancing the accuracy of risk stratification in prostate cancer.

Diffusion weighted image-guided transitional zone scoring in the detection of transitional zone prostate cancer: comparison with current PI-RADS v2.1 scoring.

PURPOSE: To compare the performance of diffusion-weighted imaging-guided transitional zone (TZ) lesion scoring on T2-weighted imaging (DWI-guided TZ scoring) to conventional PI-RADS TZ scoring. METHODS: Forty patients carried transition zone prostate cancer (TZPCa), and 40 patients had benign prostatic hyperplasia without TZPCa. A lesion-base, one-to-one correlation between the pathologic mapping sheet and the corresponding MR imaging was conducted by consensus between the genitourinary-specialized radiologist and pathologist. DWI-guided TZ scoring was defined as evaluating the DWI/apparent diffusion coefficient (ADC) images first, identifying the suspicious foci, then correlating the foci with the T2-weighted imaging, and finally assigning the PI-RADS score based on PI-RADS v2.1. Three other radiologists independently recorded the PI-RADS v2.1 scoring for TZ and the DWI-guided TZ scoring, with a time interval of 4 weeks. RESULTS: When a PI-RADS score of ≥ 3 was considered a positive lesion, the specificity, PPV, NPV and sensitivity between the DWI-guided TZ scoring and conventional PI-RADS TZ scoring were 0.896 vs. 0.542 (p < .001), 0.764 vs. 0.439 (p < .001), 0.853 vs. 0.759 (p = .001), and 0.687 vs. 0.676 (p = .836), respectively. When PI-RADS scores ≥ 4 was considered cancer-positive, the specificity and PPV were also higher when applying DWI-guided TZ scoring (0.986 vs. 0.944, p = .007; 0.943 vs. 0.810, p = .009, respectively); however, the sensitivity and NPV were not statistically different (0.468 vs. 0.468, p = .998; 0.785 vs. 0.776, p = .537, respectively). The interobserver agreement presented as κ-value was higher in DWI-guided TZ scoring (0.584) than in conventional PI-RADS TZ scoring (0.155) (p = .003). CONCLUSIONS: DWI-guided TZ scoring improves the interobserver agreement, specificity, and predictive value without impairing the sensitivity.

Can we rely on magnetic resonance imaging for prostate cancer detection and surgical planning? Comprehensive analysis of a large cohort of patients undergoing transperineal mapped biopsies.

PURPOSE: To evaluate MRI and histological concordance in prostate cancer (PCa) identification via mapped transperineal biopsies. METHODOLOGY: Retrospective per-lesion analysis of patients undergoing MRI and transperineal biopsy at the Valencian Institute of Oncology (2016-2024) using CAPROSIVO PCa data. Patients underwent MRI, with or without regions of interest (ROI), followed by transperineal biopsies (3-5 cores/ROI, 20-30 systematic). Sensitivity (Se), specificity (Sp), negative predictive value (NPV), positive predictive value (PPV), and area under the curve (AUC) were calculated, considering PI-RADS 3 lesions as positive or negative. Gleason Grade Group (GG) > 1 defined clinically significant PCa (csPCa). RESULTS: 1817 lesions were analyzed from 1325 patients (median age 67, median PSA 6.3 ng/ml). 53% MRI were negative, GG > 1 prevalence was 38.4%. MRI-negative cases showed varying PCa rates: 57.4% negative, 30.2% GG 1, and 12.4% GG > 1. PI-RADS 3 lesions had mixed outcomes: 45.6% benign, 13.1% GG 1, and 41.3% GG > 1. 9.2% PI-RADS 4-5 lesions were negative, 9% GG 1, and 81.7% GG > 1. For PI-RADS 3 lesions considered positive, Se, Sp, NPV, PPV, and AUC were 82.9%, 75%, 87.6%, 67.4%, and 0.79 respectively. Considering PI-RADS 3 as negative yielded 64.8% Se, 91% Sp, 80.6% NPV, 81.7% PPV, and 0.78 AUC. CONCLUSION: MRI and mapped prostate biopsies exhibited moderate concordance. MRI could miss up to one in five csPCa foci and misinterpret one in three ROIs. Careful MRI interpretation is crucial for optimizing patient care.

Prognostic significance of the PI-RADS score in men with prostate cancer undergoing radical prostatectomy.

OBJECTIVES: MRI-targeted biopsy (T-Bx) for which Prostate Imaging Reporting and Data System (PI-RADS) assessment categories are useful has been shown to more accurately detect clinically significant prostate cancer. However, the prognostic significance of the PI-RADS in prostate cancer patients needs further investigation. In the present study, we compared radical prostatectomy findings and postoperative oncologic outcomes in men with prostate cancer initially undergoing T-Bx for PI-RADS 3 vs. 4 vs. 5 lesions. METHODS: We assessed consecutive patients undergoing T-Bx with concurrent systematic biopsy (S-Bx), followed by radical prostatectomy. Within our Surgical Pathology database, we identified a total of 207 men where prostatic adenocarcinoma was detected on either S-Bx or T-Bx, or both. RESULTS: Prostate cancer was detected on S-Bx only (n = 32; 15%), T-Bx only (n = 39; 19%), or both S-Bx and T-Bx (n = 136; 66%). These patients had PI-RADS 3 (n = 42; 20%), 4 (n = 86; 42%), or 5 (n = 79; 38%) lesions, while T-Bx detected cancer in 31 (74%) of PI-RADS 3 cases, 72 (84%) of PI-RADS 4 cases, and 72 (91%) of PI-RADS 5 cases. There were no significant differences in any of the clinicopathologic features examined, including tumor grade on biopsy or prostatectomy and pT or pN stage, among the PI-RADS 3 vs. 4 vs. 5 groups, except a significantly higher rate of positive margin and significantly larger tumor volume in PI-RADS 5 cases than in PI-RADS 3 cases. Univariate and multivariable analyses revealed significantly higher risks of biochemical recurrence after prostatectomy in patients with PI-RADS 5 lesion than in those with PI-RADS 3 or 4 lesion. Additionally, compared with respective controls, detection of any grade cancer (P = 0.046) or Grade Group 2 or higher cancer (P = 0.005) on T-Bx was associated with a significantly higher risk of recurrence in patients with PI-RADS 5 lesion, but not in those with PI-RADS 3 or 4 lesion. CONCLUSION: PI-RADS 5 lesions were thus found to independently predict a significantly poorer postoperative prognosis. Moreover, the failure of detection of any grade cancer or clinically significant cancer on T-Bx of PI-RADS 5 lesion may particularly indicate favorable outcomes in radical prostatectomy cases.

Navigating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions.

INTRODUCTION: The objective of this study is to predict the probability of prostate cancer in PI-RADS 3 lesions using machine learning methods that incorporate clinical and mpMRI parameters. METHODS: The study included patients who had PI-RADS 3 lesions detected on mpMRI and underwent fusion biopsy between January 2020 and January 2024. Radiological parameters (Apparent diffusion coefficient (ADC), tumour ADC/contralateral ADC ratio, Ktrans value, periprostatic adipose tissue thickness, lesion size, prostate volume) and clinical parameters (age, body mass index, total prostate specific antigen, free PSA, PSA density, systemic inflammatory index, neutrophil-lymphocyte ratio [NLR], platelet lymphocyte ratio, lymphocyte monocyte ratio) were documented. The probability of prostate cancer prediction in PI-RADS 3 lesions was calculated using 6 different machine-learning models, with the input parameters being the aforementioned variables. RESULTS: Of the 235 participants in the trial, 61 had malignant fusion biopsy pathology and 174 had benign pathology. Among 6 different machine learning algorithms, the random forest model had the highest accuracy (0.86±0.04; 95% CI 0.85-0.87), F1 score (0.91±0.03; 95% CI 0.91-0.92) and AUC value (0.92±0.06; 95% CI 0.88-0.90). In SHAP analysis based on random forest model, tumour ADC, tumour ADC/contralateral ADC ratio and PSA density were the 3 most successful parameters in predicting malignancy. On the other hand, systemic inflammatory index and neutrophil lymphocyte ratio showed higher accuracy in predicting malignancy than total PSA, age, free PSA/total PSA and lesion size in SHAP analysis. CONCLUSION: Among the machine learning models we developed, especially the random forest model can predict malignancy in PI-RADS 3 lesions and prevent unnecessary biopsy. This model can be used in clinical practice with multicentre studies including more patients.

MRI-based PI-RADS score predicts ISUP upgrading and adverse pathology at radical prostatectomy in men with biopsy ISUP 1 prostate cancer.

INTRODUCTION: Most men diagnosed with very-low and low-risk prostate cancer are candidates for active surveillance; however, there is still a misclassification risk. We examined whether PI-RADS category 4 or 5 combined with ISUP 1 on prostate biopsy predicts upgrading and/or adverse pathology at radical prostatectomy. MATERIALS AND METHODS: A total of 127 patients had ISUP 1 cancer on biopsy after multiparametric MRI (mpMRI) and then underwent radical prostatectomy. We then evaluated them for ISUP upgrading and/or adverse pathology on radical prostatectomy. RESULTS: Eight-nine patients (70%) were diagnosed with PI-RADS 4 or 5 lesions. ISUP upgrading was significantly higher among patients with PI-RADS 4-5 lesions (84%) compared to patients with equivocal or non-suspicious mpMRI findings (26%, p < 0.001). Both PI-RADS 4-5 lesions (OR 24.3, 95% CI 7.3, 80.5, p < 0.001) and stage T2 on DRE (OR 5.9, 95% CI 1.2, 29.4, p = 0.03) were independent predictors of upgrading on multivariate logistic regression analysis. Men with PI-RADS 4-5 lesions also had significantly more extra-prostatic extension (51% vs. 3%, p < 0.001) and positive surgical margins (16% vs. 3%. p = 0.03). The only independent predictor of adverse pathology was PI-RADS 4-5 (OR 21.7, 95% CI 4.8, 99, p < 0.001). CONCLUSION: PI-RADS 4 or 5 lesions on mpMRI were strong independent predictors of upgrading and adverse pathology. Incorporating mpMRI findings when selecting patients for active surveillance must be further evaluated in future studies.

Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor.

Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases, has become a standard prostate cancer (PCa) screening method due to exceptional diagnosis performance. However, PI-RADS 3 lesions are an unmet medical need because PI-RADS provides diagnosis accuracy of only 30-40% at most, accompanied by a high false-positive rate. Here, we propose an explainable artificial intelligence (XAI) based PCa screening system integrating a highly sensitive dual-gate field-effect transistor (DGFET) based multi-marker biosensor for ambiguous lesions identification. This system produces interpretable results by analyzing sensing patterns of three urinary exosomal biomarkers, providing a possibility of an evidence-based prediction from clinicians. In our results, XAI-based PCa screening system showed a high accuracy with an AUC of 0.93 using 102 blinded samples with the non-invasive method. Remarkably, the PCa diagnosis accuracy of patients with PI-RADS 3 was more than twice that of conventional PI-RADS scoring. Our system also provided a reasonable explanation of its decision that TMEM256 biomarker is the leading factor for screening those with PI-RADS 3. Our study implies that XAI can facilitate informed decisions, guided by insights into the significance of visualized multi-biomarkers and clinical factors. The XAI-based sensor system can assist healthcare professionals in providing practical and evidence-based PCa diagnoses.

The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review.

Early detection of clinically significant prostate cancer (csPCa) has substantially improved with the latest PI-RADS versions. However, there is still an overdiagnosis of indolent lesions (iPCa), and radiomics has emerged as a potential solution. The aim of this systematic review is to evaluate the role of handcrafted and deep radiomics in differentiating lesions with csPCa from those with iPCa and benign lesions on prostate MRI assessed with PI-RADS v2 and/or 2.1. The literature search was conducted in PubMed, Cochrane, and Web of Science databases to select relevant studies. Quality assessment was carried out with Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), Radiomic Quality Score (RQS), and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. A total of 14 studies were deemed as relevant from 411 publications. The results highlighted a good performance of handcrafted and deep radiomics methods for csPCa detection, but without significant differences compared to radiologists (PI-RADS) in the few studies in which it was assessed. Moreover, heterogeneity and restrictions were found in the studies and quality analysis, which might induce bias. Future studies should tackle these problems to encourage clinical applicability. Prospective studies and comparison with radiologists (PI-RADS) are needed to better understand its potential.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml.

OBJECTIVES: To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy. PATIENTS AND METHODS: Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit. RESULTS: A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively). CONCLUSION: The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

Urine biomarkers can predict prostate cancer and PI-RADS score prior to biopsy.

Prostate-Specific Antigen (PSA) based screening of prostate cancer (PCa) needs refinement. The aim of this study was the identification of urinary biomarkers to predict the Prostate Imaging-Reporting and Data System (PI-RADS) score and the presence of PCa prior to prostate biopsy. Urine samples from patients with elevated PSA were collected prior to prostate biopsy (cohort = 99). The re-analysis of mass spectrometry data from 45 samples was performed to identify urinary biomarkers to predict the PI-RADS score and the presence of PCa. The most promising candidates, i.e. SPARC-like protein 1 (SPARCL1), Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), Alpha-1-microglobulin/bikunin precursor (AMBP), keratin 13 (KRT13), cluster of differentiation 99 (CD99) and hornerin (HRNR), were quantified by ELISA and validated in an independent cohort of 54 samples. Various biomarker combinations showed the ability to predict the PI-RADS score (AUC = 0.79). In combination with the PI-RADS score, the biomarkers improve the detection of prostate carcinoma-free men (AUC = 0.89) and of those with clinically significant PCa (AUC = 0.93). We have uncovered the potential of urinary biomarkers for a test that allows a more stringent prioritization of mpMRI use and improves the decision criteria for prostate biopsy, minimizing patient burden by decreasing the number of unnecessary prostate biopsies.

Precision in prostate cancer detection: integrating prostate-specific antigen density (PSAD) and the Prostate Imaging Reporting and Data System (PI-RADS) to provide additional risk stratification for a more accurate diagnostic decision.

PURPOSE: This study focuses on integrating prostate-specific antigen density (PSAD) and Prostate Imaging Reporting and Data System (PI-RADS) for enhanced risk stratification in biopsy-naïve patients. METHODS: A prospective study was conducted on 339 patients with suspected prostate cancer, utilizing PSAD and PI-RADS in combination. Logistic regression models were employed, and receiver operating characteristic (ROC) analysis performed to evaluate predictive performance. The patient cohort underwent multiparametric MRI, targeted biopsy, and systematic biopsy. RESULTS: When patients were stratified into four PSAD risk groups, the rate of clinically significant prostate cancer (csPCa) increased significantly with higher PSAD levels. Logistic regression confirmed the independent contribution of PI-RADS and PSAD, highlighting their role in the prediction of csPCa. Combined models showed superior performance, as evidenced by the area under the curve (AUC) for PI-RADS category and PSAD (0.756), which exceeded that of the individual predictors (PSA AUC, 0.627, PI-RADS AUC 0.689, PSAD AUC 0.708). CONCLUSION: This study concludes that combining PSAD and PI-RADS improves diagnostic accuracy and predictive value for csPCa in biopsy-naïve men, resulting in a promising strategy to provide additional risk stratification for more accurate diagnostic decision in biopsy-naïve patients, especially in the PI-RADS 3 group.

Prior Negative Biopsy, PSA Density, and Anatomic Location Impact Cancer Detection Rate of MRI-Targeted PI-RADS Index Lesions.

BACKGROUND: MRI fusion prostate biopsy has improved the detection of clinically significant prostate cancer (CSC). Continued refinements in predicting the pre-biopsy probability of CSC are essential for optimal patient counseling. We investigated potential factors related to improved cancer detection rates (CDR) of CSC in patients with PI-RADS ≥ 3 lesions. METHODS: The pathology of 980 index lesions in 980 patients sampled by transrectal mpMRI-targeted prostate biopsy across four medical centers between 2017-2020 was reviewed. PI-RADS lesion distribution included 291 PI-RADS-5, 374 PI-RADS-4, and 315 PI-RADS-3. We compared CDR of index PI-RADS ≥ 3 lesions based on location (TZ) vs. (PZ), PSA density (PSAD), and history of prior negative conventional transrectal ultrasound-guided biopsy (TRUS). RESULTS: Mean age, PSA, prostate volume, and level of prior negative TRUS biopsy were 66 years (43-90), 7.82 ng/dL (5.6-11.2), 54 cm3 (12-173), and 456/980 (46.5%), respectively. Higher PSAD, no prior history of negative TRUS biopsy, and PZ lesions were associated with higher CDR. Stratified CDR highlighted significant variance across subgroups. CDR for a PI-RADS-5 score, PZ lesion with PSAD ≥ 0.15, and prior negative biopsy was 77%. Conversely, the CDR rate for a PI-RADS-4 score, TZ lesion with PSAD < 0.15, and prior negative biopsy was significantly lower at 14%. CONCLUSIONS: For index PI-RADS ≥ 3 lesions, CDR varied significantly based on location, prior history of negative TRUS biopsy, and PSAD. Such considerations are critical when counseling on the merits and potential yield of prostate needle biopsy.

Limited Utility of Dynamic Contrast Enhancement Imaging Sequences Within the PI-RADS v2.1 Classification Scheme: A Retrospective Cross-Sectional Study of MRI Reports.

Background/Objective: We sought to characterize the proportion of peripheral zone lesions "upgraded" within the PI-RADS v2.1 protocol using DCE imaging sequences in a large patient population undergoing multiparametric prostate MRI. Methods: A retrospective review of radiologist reports for 2742 prostate MRI exams at 2 large Alberta teaching hospitals between January 2017 and January 2022 was conducted. Prostate specific antigen (PSA), prostate volume, sequence specific and overall PI-RADS scores, and lesion positivity for DCE were collected if present in the accompanying radiology report. Further, pathology reports of biopsies of the upgraded lesions within upgraded patients were reviewed to see if upgraded lesions were deemed clinically significant by gleason score/grade group. Results: The median age was 63 years, with a median PSA and PSA density of 7.5 ng/mL and 0.13 ng/mL2 respectively. A total of 1809 lesions were reported, with 69.4% of all lesions being DCE positive. Of the lesions within the peripheral zone, 548 were overall PI-RADS 4. A total of 87/2742 (3.2%) of patients were upgraded to a PI-RADS 4 by DCE imaging. Within these patients, 65 had pathology reports available, of which 18 had a clinically significant lesion at the upgrade site. Conclusion: Contrast enhancement is only beneficial for a very small portion of patients undergoing prostate MRI. Given the invasive nature of contrast enhanced studies, potential contrast induced side effects, added imaging time, and the cost of contrast agent, routine use of contrast for prostate MRI is questioned. Further studies are necessary to determine if it should be part of routine prostate MRI imaging protocols.

Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study.

RATIONALE AND OBJECTIVES: To determine the role of dynamic contrast-enhanced (DCE) MRI-radiomics in predicting the International Society of Urological Pathology Grade Group (ISUP-GG) in therapy-naïve prostate cancer (PCa) patients. MATERIALS AND METHODS: In this ethics review board-approved retrospective study on two prospective clinical trials between 2017 and 2020, 73 men with suspected/confirmed PCa were included. All participants underwent multiparametric MRI. On MRI, dominant lesions (per PI-RADS) were identified. DCE-MRI radiomic features were extracted from the segmented volumes following the image biomarker standardisation initiative (IBSI) guidelines through 14 time points. Histopathology evaluation on the cognitive-fusion targeted biopsies was set as the reference standard. Univariate regression was done to evaluate potential predictors across all calculated features. Random forest imputation was used for multivariate modelling. RESULTS: 73 index lesions were reviewed. Histopathology revealed 28, 16, 13 and 16 lesions with ISUP-GG-Negative/1/2, ISUP-GG-3, ISUP-GG-4 and ISUP-GG-5, respectively. From the extracted features, total lesion enhancement (TLE), minimum enhancement intensity and Grey-Level Run Length Matrix (GLRLM) were the most significantly different parameters among ISUP-GGs (Neg/1/2 vs 3/4 vs 5). 16 features with significant cross-sectional associations with ISUP-GGs entered the multivariate analysis. The final DCE partitioning model used only four features (lesion sphericity, TLE, GLRLM and Grey-Level Zone Length Matrix). For the binarized diagnosis (ISUP-GG≤2 vs ISUP-GG>2), the accuracy reached 81%. CONCLUSION: DCE-MRI radiomics might be used as a non-invasive tool for aiding pathological grade group prediction in therapy-naïve PCa patients, potentially adding complementary information to PI-RADS for supporting tailored diagnostic pathways and treatment planning.

Clinical-imaging metrics for the diagnosis of prostate cancer in PI-RADS 3 lesions.

OBJECTIVE: To explore the feasibility and efficacy of clinical-imaging metrics in the diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in prostate imaging-reporting and data system (PI-RADS) category 3 lesions. METHODS: A retrospective analysis was conducted on lesions diagnosed as PI-RADS 3. They were categorized into benign, non-csPCa and csPCa groups. Apparent diffusion coefficient (ADC), T2-weighted imaging signal intensity (T2WISI), coefficient of variation of ADC and T2WISI, prostate-specific antigen density (PSAD), ADC density (ADCD), prostate-specific antigen lesion volume density (PSAVD) and ADC lesion volume density (ADCVD) were measured and calculated. Univariate and multivariate analyses were used to identify risk factors associated with PCa and csPCa. Receiver operating characteristic curve (ROC) and decision curves were utilized to assess the efficacy and net benefit of independent risk factors. RESULTS: Among 202 patients, 133 had benign prostate disease, 25 non-csPCa and 44 csPCa. Age, PSA and lesion location showed no significant differences (P > 0.05) among the groups. T2WISI and coefficient of variation of ADC (ADCcv) were independent risk factors for PCa in PI-RADS 3 lesions, yielding an area under the curve (AUC) of 0.68. ADC was an independent risk factor for csPCa in PI-RADS 3 lesions, yielding an AUC of 0.65. Decision curve analysis showed net benefit for patients at certain probability thresholds. CONCLUSIONS: T2WISI and ADCcv, along with ADC, respectively showed considerable promise in enhancing the diagnosis of PCa and csPCa in PI-RADS 3 lesions.

A Feasibility Study of AI-Assisted Compressed Sensing in Prostate T2-Weighted Imaging.

RATIONALE AND OBJECTIVES: To evaluate the image quality and PI-RADS scoring performance of prostate T2-weighted imaging (T2WI) based on AI-assisted compressed sensing (ACS). MATERIALS AND METHODS: In this prospective study, adult male urological outpatients or inpatients underwent prostate MRI, including T2WI, diffusion-weighted imaging and apparent diffusion coefficient maps. Three accelerated scanning protocols using parallel imaging (PI) and ACS: T2WIPI, T2WIACS1 and T2WIACS2 were evaluated through comparative analysis. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slope profile, and edge rise distance (ERD). Image quality was qualitatively assessed using a five-point Likert scale (ranging from 1 = non-diagnostic to 5 = excellent). PI-RADS scores were determined for the largest or most suspicious lesions in each patient. The Friedman test and one-way ANOVA with post hoc tests were utilized for group comparisons, with statistical significance set at P < 0.05. RESULTS: This study included 40 participants. Compared to PI, ACS reduced acquisition time by over 50%, significantly enhancing the CNR of sagittal and axial T2WI (P < 0.05), significantly improving the image quality of sagittal and axial T2WI (P < 0.05). No significant differences were observed in slope profile, ERD, and PI-RADS scores between groups (P > 0.05). CONCLUSION: ACS reduced prostate T2WI acquisition time by half while improving image quality without affecting PI-RADS scores.

Integration of PSAd and multiparametric MRI to forecast biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml.

Introduction: This study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy. Methods: This retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models. Results: The CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed. Conclusion: The prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.

Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI

BACKGROUND: Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions. METHODS: A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data. RESULTS: The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters. CONCLUSION: These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study.

OBJECTIVES: To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives. METHODS: One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions. RESULTS: csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only. CONCLUSION: Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.