The predictive impact of hematological inflammatory markers in detecting prostate cancer in patients with PI-RADS 3 lesions on multiparametric magnetic resonance imaging.

BACKGROUND: The diagnostic accuracy of suspicious lesions that are classified as PI-RADS 3 in multiparametric prostate magnetic-resonance imaging (mpMRI) is controversial. This study aims to assess the predictive capacity of hematological inflammatory markers such as neutrophil-lymphocyte ratio (NLR), pan-immune-inflammation value (PIV), and systemic immune-response index (SIRI) in detecting prostate cancer in PI-RADS 3 lesions. METHODS: 276 patients who underwent mpMRI and subsequent prostate biopsy after PI-RADS 3 lesion detection were included in the study. According to the biopsy results, the patients were distributed to two groups as prostate cancer (PCa) and no cancer (non-PCa). Data concerning age, PSA, prostate volume, PSA density, PI-RADS 3 lesion size, prostate biopsy results, monocyte counts (109/L), lymphocyte counts (109/L), platelet counts (109/L), neutrophils count (109/L) were recorded from the complete blood count. From these data; PIV value is obtained by monocyte × neutrophil × platelet/lymphocyte, NLR by neutrophil/lymphocyte, and SIRI by monocyte number × NLR. RESULTS: Significant variations in neutrophil, lymphocyte, and monocyte levels between PCa and non-PCa patient groups were detected (p = 0.009, p = 0.001, p = 0.005 respectively, p < 0.05). NLR, PIV, and SIRI exhibited significant differences, with higher values in PCa patients (p = 0.004, p = 0.001, p < 0.001 respectively, p < 0.05). The area under curve of SIRI was 0.729, with a cut-off value of 1.20 and with a sensitivity 57.70%, and a specificity of 68.70%. CONCLUSION: SIRI outperformed NLR and PIV in detecting PCa in PI-RADS 3 lesions, showcasing its potential as a valuable biomarker. Implementation of this parameter to possible future nomograms has the potential to individualize and risk-stratify the patients in prostate biopsy decision.

Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study.

PURPOSE: To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. METHODS: The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer-lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. RESULTS: The difference of PSAD between PCa and benign lesions was statistically significant (P = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). CONCLUSIONS: The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.

Clinical and Radiological Factors for Predicting Clinically Significant Prostate Cancer in Biopsy-Naive Patients With PI-RADS 3 Lesions.

OBJECTIVE: This study intends to examine the anticipatory power of clinical and radiological parameters in detecting clinically significant prostate cancer in patients demonstrating Prostate Imaging Reporting and Data System 3 lesions. METHODS: This was a retrospective study. The study included participation from 453 patients at the First Affiliated Hospital of Soochow University, sampled between September 2017 through August 2022. Each patient underwent a routine 12-core prostate biopsy followed by a 2 to 5 core fusion-targeted biopsy. We utilized both univariate and multivariate logistic regression analyses to identify the parameters that have a correlation with clinically significant prostate cancer. The predictive ability of these parameters was assessed using the receiver operating characteristic curve, leading to the creation of a nomogram. RESULTS: Clinically significant prostate cancer was detected in 68 out of 453 patients with Prostate Imaging Reporting and Data System 3 lesions (15.01%). Among Prostate Imaging Reporting and Data System 3a and 3b patients, 4.78% (3.09% of the total) and 33.75% (11.92% of the total), respectively, had clinically significant prostate cancer. Systematic biopsy improved prostate cancer and clinically significant prostate cancer detection rates by 7.72% and 3.09%, respectively, compared to targeted biopsy. Without systematic biopsy, there would be an undetected rate of 15% for prostate cancer and 8.13% for clinically significant prostate cancer in Prostate Imaging Reporting and Data System 3b patients. Several clinical parameters, including age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination, were statistically significant in the logistic regression analysis for clinically significant prostate cancer. The individual diagnostic accuracies of these parameters for clinically significant prostate cancer were 0.648, 0.645, 0.75, 0.763, and 0.7, respectively, but their combined accuracy improved to 0.866. A well-fit nomogram based on the identified risk factors was constructed (χ2 = 10.254, P = .248). CONCLUSION: The combination of age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination presented a higher diagnostic value for clinically significant prostate cancer than any single parameter in patients with Prostate Imaging Reporting and Data System 3 lesions. Systematic biopsy proved crucial for biopsy-naive patients with Prostate Imaging Reporting and Data System 3 lesions and should not be omitted.

Molecular and diffusion features for identification of clinically significant prostate cancer in PI-RADS 3 lesions.

BACKGROUND: The recommendation to perform biopsy of PIRADS 3 lesions has not been adopted with strength as compared to higher scored lesions on multiparametric MRI. This represents a challenging scenario and an unmet need for clinicians to apply a risk adapted approach in these cases. In the present study, we examined clinical and radiologic characteristics in men with PI-RADS 3 index lesions that can predict csPCa on mpMRI-target biopsy. METHODS: Revision of a prospective database with patients who underwent targeted and systematic biopsies from 2015 to 2023 for PI-RADS 3 lesions identified on mpMRI. Baseline variables were collected, such as PSA density (PSAd), 4Kscore, prostate size, and the apparent diffusion coefficient (ADC) value of the lesion on mpMRI. Logistic regression, receiver operating characteristic (ROC) and decision curve analyses (DCA) assessing the association between clinic-radiologic factors and csPCa were performed. RESULTS: Overall, 230 patients were included in the study and the median age was 65 years. The median prostate size and PSA were 50 g and 6.26 ng/mL, respectively. 17.4% of patients had csPCa, while 27.5% had Gleason group 1. In univariable logistic analyses, we found that age, BMI, prostate size, PSAd, ADC, and 4Kscore were significant csPCa predictors (P < 0.05). PSAd showed the best prediction performance in terms of AUC (= 0.679). On multivariable analysis, PSAd and 4Kscore were associated with csPCa. The net benefit of PSAd combined with clinical features was superior to those of other parameters. Within patients with PSAd < 0.15, 4Kscore was a statistically significant predictor of csPCa (OR = 3.25, P = 0.032). CONCLUSION: PSAd and 4Kscore are better predictors of csPCa in patients with PIRADS 3 lesions compared to ADC. The predictive role of 4Kscore is higher in patients with low PSAd. These results can assist practitioners in the risk stratification of patients with equivocal lesions to determine the need of biopsy.

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.

PI-RADS 3 lesions: a critical review and discussion of how to improve management.

Since the publication of PI-RADS v1 in 2012, the debate regarding the question of how to manage PI-RADS 3 lesions has been mostly unsolved. However, based on our review of the current literature we discuss possible solutions and improvements to the original classification, factors such as PSAD (Prostate Specific Antigen Density), age, and tumor volume, in the decision of whether to proceed with a biopsy or not.

Comparison of PSA density and lesion volume strategies for selecting men with equivocal PI-RADS 3 lesions on bpMRI for biopsies.

PURPOSE: To compare two strategies: Prostate-specific antigen density (PSAd) and lesion volume measurement in ruling out significant prostate cancer (sPCa) in men with equivocal Prostate Imaging Reporting and Data System (PI-RADS) category 3 index lesions on biparametric magnetic resonance imaging. METHODS: In total, 130 men from our database had index lesions with PI-RADS scores of 3. Prostate volume was measured using the ellipsoid method, in accordance with PI-RADS version 2.1 criteria. Index lesion volumes were also measured using the ellipsoidal formula on the diffusion-weighted imaging sequence with the highest b-value and sagittal T2 sequences. RESULTS: Among 130 men with PI-RADS category 3 index lesions, 23 (18%) had sPCa. In total, 6 of the 89 men with PSAd < 0.15 ng/mL2 (7%) had sPCa, whereas 8 of the 49 men with index lesion volumes < 0.5 mL (16%) had sPCa. The difference was statistically significant (McNemar, p < 0.0001). CONCLUSION: The PSAd strategy performed better than the lesion volume strategy in ruling out sPCa in men with equivocal PI-RADS category 3 index lesions.

Optimal PSA density threshold and predictive factors for the detection of clinically significant prostate cancer in patient with a PI-RADS 3 lesion on MRI.

INTRODUCTION: While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions usually justify prostate biopsy (PBx), the management of a PI-RADS 3 lesion can be discussed. The aim of our study was to determine the optimal prostate-specific antigen density (PSAD) threshold and predictive factors of clinically significant prostate cancer (csPCa) in patients with a PI-RADS 3 lesion on MRI. PATIENTS AND METHODS: Using our prospectively maintained database, we conducted a monocentric retrospective study, including all patients with a clinical suspicious of prostate cancer (PCa), all of them had a PI-RADS 3 lesion on the mpMRI prior to PBx. Patients under active surveillance or displaying suspicious digital rectal examination were excluded. Clinically significant (csPCa) was defined as PCa with any ISUP grade group ≥ 2 (Gleason ≥ 3 + 4). RESULTS: We included 158 patients. The detection rate of csPCa was 22.2%. In case of PSAD ≤ 0.15 ng/ml/cm3, PBx would be omitted in 71.5% (113/158) of men at the cost of missing 15.0% (17/113) of csPCa. With a threshold of 0.15 ng/ml/cm3, the sensitivity and the specificity were 0.51 and 0.78 respectively. The positive predictive value was 0.40 and the negative predictive value was 0.85. According to multivariate analysis, age (OR = 1.10, CI95% 1.03-1.19, P = 0.007), and PSAD ≥ 0.15 ng/ml/cm3 (OR = 3.59, CI95% 1.41-9.47, P = 0.008) were independent predictive factors of csPCa. Previous negative PBx was negatively associated with csPCa (OR = 0.24, CI 95% 0.07-0.66, P = 0.01). CONCLUSION: Our result suggests that the optimal PSAD threshold was 0.15 ng/ml/cm3. However, in this case omitting PBx in 71.5% of cases would be at the cost of missing 15.0% of csPCa. PSAD should not be used alone, other predictive factors as age and PBx history should also be considered in the discussion with the patient, to avoid PBx while missing few csPCa.

PI-RADS 3 score: A retrospective experience of clinically significant prostate cancer detection.

Rationale and objectives: The study aims to propose an optimal workflow in patients with a PI-RADS 3 (PR-3) assessment category (AC) through determining the timing and type of pathology interrogation used for the detection of clinically significant prostate cancer (csPCa) in these men based upon a 5-year retrospective review in a large academic medical center. Materials and methods: This United States Health Insurance Probability and Accountability Act (HIPAA)-compliant, institutional review board-approved retrospective study included men without prior csPCa diagnosis who received PR-3 AC on magnetic resonance (MR) imaging (MRI). Subsequent incidence and time to csPCa diagnosis and number/type of prostate interventions was recorded. Categorical data were compared using Fisher's exact test and continuous data using ANOVA omnibus F-test. Results: Our cohort of 3238 men identified 332 who received PR-3 as their highest AC on MRI, 240 (72.3%) of whom had pathology follow-up within 5 years. csPCa was detected in 76/240 (32%) and non-csPCa in 109/240 (45%) within 9.0 ± 10.6 months. Using a non-targeted trans-rectal ultrasound biopsy as the initial approach (n = 55), another diagnostic procedure was required to diagnose csPCa in 42/55 (76.4%) of men, compared with 3/21(14.3%) men with an initial MR targeted-biopsy approach (n = 21); (p < 0.0001). Those with csPCa had higher median serum prostate-specific antigen (PSA) and PSA density, and lower median prostate volume (p < 0.003) compared with non-csPCa/no PCa. Conclusion: Most patients with PR-3 AC underwent prostate pathology exams within 5 years, 32% of whom were found to have csPCa within 1 year of MRI, most often with a higher PSA density and a prior non-csPCa diagnosis. Addition of a targeted biopsy approach initially reduced the need for a second biopsy to reach a for csPCa diagnosis. Thus, a combination of systematic and targeted biopsy is advised in men with PR-3 and a co-existing abnormal PSA and PSA density.

Evaluating the Utility of Prostate-Specific Antigen Density in Risk Stratification of PI-RADS 3 Peripheral Zone Lesions on Non-Contrast-Enhanced Prostate MRI: An Exploratory Single-Institution Study.

Objective This study aimed to explore the potential of prostate-specific antigen density (PSAD) as a supplementary tool for defining high-risk Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions in the peripheral zone on non-contrast-enhanced MRI. This additional stratification tool could supplement the decision-making process for biopsy, potentially helping in identifying higher-risk patients more accurately, minimizing unnecessary procedures in lower-risk patients, and limiting the need for dynamic contrast-enhanced (DCE) scans. Materials and methods Between January 2019 and April 2023, 30 patients with PI-RADS 3 lesions underwent MRI-ultrasound fusion biopsies at our institution. Age and PSAD values were investigated using logistic regression and chi-square automatic interaction detection (CHAID) analysis to discern their predictive value for malignancy. Results The mean patient age was 64.7 years, and the mean PSAD was 0.13 ng/mL2. Logistic regression demonstrated PSAD to be a significant predictor of cancer (p=0.012), but not age (p=0.855). CHAID analysis further identified a PSAD cut-off value of 0.12, below which the cancer detection rate was 23.1% and above which the rate increased to 76.5%. Conclusions This exploratory study suggests that PSAD might be utilized to enhance the stratification of high-risk PI-RADS 3 lesions in the peripheral zone on non-contrast-enhanced MRI, aiding in decision-making for biopsy. While biopsy remains the gold standard for definitive diagnosis, a high PSAD value may suggest a greater need for biopsy in this specific group. Although further validation in larger cohorts is required, our findings contribute to the ongoing discourse on optimizing PI-RADS 3 lesion management. Limitations include a small sample size, the retrospective nature of the study, and the single-center setting, which may impact the generalizability of our results.

An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience.

The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.

Efficacy of plasma atherogenic index in predicting malignancy in the presence of Prostate Imaging-Reporting and Data System 3 (PI-RADS 3) prostate lesions.

PURPOSE: Plasma atherogenic index (PAI) was shown to be positively correlated with the presence of malignity in patients with suspicious findings for renal cell cancer and colon cancer in reported studies. In this study, we aimed to evaluate whether there is an association with the presence of malignity in patients PI-RADS 3 prostate lesions and PAI. METHODS: This retrospective study reviewed the data of 139 patients who underwent transrectal ultrasonography-guided systematic and cognitive fusion prostate biopsy for PI-RADS 3 lesions in multiparametric magnetic resonance imaging. The patients were divided to two groups as malign (n = 33) and benign (n = 106). The association between age, body mass index, comorbidities, smoking status, prostate-specific antigen (PSA), PSA density, free/total PSA, prostate weight, lesion diameter, triglyceride value, high-density lipoprotein-cholesterol value, PAI value data and presence of malignity were investigated by descriptive, multivariate and receiver-operating characteristic (ROC) analysis. RESULTS: PSA, PSAD, lesion diameter and PAI value were statistically significantly higher in the malignant group compared to the benign group, and the free/total PSA ratio was lower. In multivariate logistic regression analysis, PSA > 9.9 ng/ml, free/total PSA < 12.1%, lesion diameter > 13.5 mm and PAI > 0.13 were identified as independent risk factors for presence of prostate malignancy. CONCLUSION: PAI was found to be a predictive parameter for prostate cancer in PI-RADS 3 prostate lesions. Our study can open new thoughts about PAI as metric to assess the prostate cancer risk.

Transverse prostate maximum sectional area can predict clinically significant prostate cancer in PI-RADS 3 lesions at multiparametric magnetic resonance imaging.

Background: To evaluate multiparametric magnetic resonance imaging (mpMRI) parameters, such as TransPA (transverse prostate maximum sectional area), TransCGA (transverse central gland sectional area), TransPZA (transverse peripheral zone sectional area), and TransPAI (TransPZA/TransCGA ratio) in predicting prostate cancer (PCa) in prostate imaging reporting and data system (PI-RADS) 3 lesions. Methods: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), the area under the receiver operating characteristic curve (AUC), and the best cut-off, were calculated. Univariate and multivariate analyses were carried out to evaluate the capability to predict PCa. Results: Out of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm2, 9.1cm2, 5.5cm2 and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P<0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P<0.001) were independent predictors of PCa. The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa. The best cut-off of TransPA for csPCa was 18 (Sensitivity 88.2%, Specificity 37.2%, PPV 35.7%, NPV 88.9%). The discrimination (AUC) of the multivariate model was 0.627 (95% CI: 0.519-0.734, P<0.031). Conclusions: In PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy.

Should Targeted Biopsy be Performed in Patients Who Have Only Pi-rads 3 Lesions?

OBJECTIVE: To determine whether clinical or radiological parameters can predict clinically significant prostate cancer (csPC) in patients with the Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. PATIENTS AND METHODS: Data were obtained from 247 patients with PI-RADS 3 lesions on mpMRI and who had received a software guided transperineal/transrectal MRI/transrectal ultrasonography (MRI/TRUS) fusion prostate biopsy with concomitant standard systematic 12-core biopsy following mpMRI in the prostate cancer and prostate biopsy database of Turkish Urooncology Association, between 2016 and 2020. The cut-off values of clinical parameters were determined using receiver operating characteristic (ROC) curve analysis. Simple and multiple logistic regression analyses were performed to determine the clinical parameters in predicting csPC. RESULTS: A total of 56 patients (22.6%) had prostate cancer, 23 (9.3%) of whom had csPC. In the lesion- based analysis, cancer detection rates (CDRs) of each lesion in targeted biopsy were found to be 6% and 5% for ISUP GG 1 and ISUP GG ≥ 2, respectively. In the patient-based analysis, clinically insignificant CDRs were significantly higher in systematic biopsy compared with targeted biopsy, whereas no significant difference was found in terms of clinically significant CDRs (p = 0.020 and p=0.422, respectively). The cut-off values were determined as 48.3 mL (AUC [95% CI] = 0.68 [0.53-0.82]) for prostate volume, and 0.213 ng/mL/mL (AUC [95% CI] = 0.64 (0.51-0.77]) for PSAD in predicting csPC. In the multiple logistic regression analysis, only PSAD was found to be an independent risk factor in predicting csPC (OR [95% CI]: 3.56 [1.15-10.91], p = 0.024). CONCLUSION: Since PSAD > 0.20 ng/mL/mL was found to be positive independent risk factor in predicting csPC, in the absence of advanced radiological parameters, PSAD could be used for the biopsy decision in patients with PI-RADS 3 lesions.

Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical-Radiological Model.

PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external validation is required. We retrospectively included all patients with at least one PI-RADS 3 lesion (PI-RADS v2.1) detected on a 3T prostate MRI scan at our Institution (June 2016-March 2021). An MRI-targeted biopsy was used as ground truth. We assessed reproducible mpMRI radiomic features found in the literature. Then, we proposed a new model combining PSA density and two radiomic features (texture regularity (T2) and size zone heterogeneity (ADC)). All models were trained/assessed through 100-repetitions 5-fold cross-validation. Eighty patients were included (26 with GS ≥ 7). In total, 9/20 T2 features (Hector's model) and 1 T2 feature (Jin's model) significantly correlated to biopsy on our dataset. PSA density alone predicted clinically significant tumors (sensitivity: 66%; specificity: 71%). Our model obtained a sensitivity of 80% and a specificity of 76%. Standard-compliant works with detailed methodologies achieve comparable radiomic feature sets. Therefore, efforts to facilitate reproducibility are needed, while complex models and imaging protocols seem not, since our model combining PSA density and two radiomic features from routinely performed sequences appeared to differentiate clinically significant cancers.

Utilizing lesion diameter and prostate specific antigen density to decide on magnetic resonance imaging guided confirmatory biopsy of prostate imaging reporting and data system score three lesions in African American prostate cancer patients managed with active surveillance.

OBJECTIVE: The objective of the study is to identify the rate of significant prostate cancer (PCa) detection in PI-RADS3 lesions in AA patients stratified by PSAD threshold of < 0.15 vs. ≥ 0.15 ng/ml2 and lesion diameter of < 1 cm vs ≥ 1 cm. METHODS: We analyzed our institutional database of MRI-TB to identify the rate of significant prostate cancer (PCa) detection in PI-RADS3 lesions in AA patients stratified by PSAD threshold of < 0.15 vs. ≥ 0.15 ng/ml2 and lesion diameter of < 1 cm vs ≥ 1 cm. Significant prostate cancer was defined as Gleason grade group 2 or higher on MRI-TB of the PI-RADS 3 lesion. RESULTS: Of 768 patients included in the database, 211 (27.5%) patients identified themselves as AAs. Mean age of AA patients was 63 years and mean PSAD was 0.21. Sixty nine (32.7%) AA patients were found to have PI-RADS 3 lesions. Mean PSAD of AA patients with PI-RADS 3 lesions was 0.21 ng/ml2 as well. Fifty percent of AA patients with PI-RADS 3 lesions had PSAD ≥ 0.15 ng/ml2. Significant PCa detection rate for AA patients with PI-RADS 3 lesions was 9% for PSAD of ≥ 0.15 vs. 0.03% percent for AA patients with PSAD < 0.15 ng/ml2 (OR 7.056, CI 1.017-167.9, P = 0.04). Stratification by lesion diameter (< 1 cm vs. > 1 cm) resulted in missing 0% of significant PCa when only AA patients with PSAD ≥ 0.15 ng/ml2 and lesion diameter ≥ 1 cm received MRI-TB. CONCLUSIONS: We report on the performance of a reported PSAD density threshold in detecting significant PCa in one of the largest series of AA patients receiving MRI-TB of the prostate. Our results have direct clinical implications when counseling AA patients with PI-RADS 3 lesion on whether they should undergo MRI-TB of such lesions.

The use of 29 MHz transrectal micro-ultrasound to stratify the prostate cancer risk in patients with PI-RADS III lesions at multiparametric MRI: A single institutional analysis.

INTRODUCTION: Magnetic Resonance Imaging (MRI) has emerged as the most accurate diagnostic tool, showing a high sensitivity in the diagnosis of clinically significant prostate cancer (csCaP). However only a minority of patients with a PI-RADS 3 lesion at multiparametric magnetic resonance imaging (MRI) are diagnosed with csCaP. The aim of the current study was to assess whether high resolution micro-ultrasound (microUS) could help in sub-stratifying the risk of csCaP in this specific population. MATERIAL AND METHODS: We retrospectively analyzed the records of 111 consecutive patients scheduled for a prostate biopsy with at least 1 PI-RADS 3 lesions at MRI. We excluded patients with a PIRADS >3 lesion, even if they had a coexisting PIRADS 3 lesions. MicroUS was performed in all patients before prostate biopsy by an operator blind to MRI results. The Prostate Risk Identification using MicroUS (PRI-MUS) protocol was used to assess the risk of CaP and csCaP. All patients received both targeted and systematic biopsies. The primary endpoint was to determine the diagnostic accuracy of microUS in detection of csCaP in patients with a PI-RADS 3 lesion at MRI. Specifically, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of microUS were determined. Multivariable logistic regression models (MLRMs) were fitted to identify predictors of CaP. The diagnostic accuracy was reported as area under the receiver operator characteristic (ROC) curve. RESULTS: Overall, 43 patients (38.7%) harboured CaP and 22 (20%) csCaP. MicroUS showed a high sensitivity and negative predictive value (100%), while its specificity and positive predictive value were 33.7% and 27.2%, respectively. Among patients without lesions at microUS, 25 (83.3%) did not harbour CaP, while 5 (16.7%) patients were diagnosed with a Gleason score 6 CaP, with no patients harbouring csCaP. Using microUS, the csCaP detection would have remained 100%, while reducing the detection of insignificant CaP of a 23.8% extent (n = 5). In MLRMs, lesion identified at microUS and continuously-coded PSAd were independent predictors of CaP. The accuracy of a model including PRI-MUS score, digital rectal examination (DRE), PSA density, age and family history was 0.744 (95% CI: 0.645 - 0.843). CONCLUSION: In our single-institutional retrospective study, microUS was potentially capable to stratify the presence of CaP in patients with an equivocal MRI. Further prospective studies on larger populations are needed to validate our results.

Does Adding Standard Systematic Biopsy to Targeted Prostate Biopsy in PI-RADS 3 to 5 Lesions Enhance the Detection of Clinically Significant Prostate Cancer? Should All Patients with PI-RADS 3 Undergo Targeted Biopsy?

INTRODUCTION: Our aim was to assess the value of adding standard biopsy to targeted biopsy in cases of suspicious multiparametric magnetic resonance imaging (mp-MRI) and also to evaluate when a biopsy of a PI-RADS 3 lesion could be avoided. METHODS: A retrospective study of patients who underwent targeted biopsy plus standard systematic biopsy between 2016-2019 was performed. All the 1.5 T magnetic resonance images were evaluated according to PI-RADSv.2. An analysis focusing on the clinical scenario, lesion location, and PI-RADS score was performed. RESULTS: A total of 483 biopsies were evaluated. The mean age was 65 years, with a PSA density of 0.12 ng/mL/cc. One-hundred and two mp-MRIs were categorized as PI-RADS-3. Standard biopsy was most helpful in detecting clinically significant prostate cancer (csPCa) in patients in the active surveillance (AS) cohort (increasing the detection rate 12.2%), and in peripheral lesions (6.5%). Adding standard biopsy showed no increase in the detection rate for csPCa in patients with PI-RADS-5 lesions. Considering targeted biopsy in patients with PI-RADS 3 lesions, a higher detection rate was shown in biopsy-naïve patients versus AS and in patients with a previous negative biopsy (p = 0.002). Furthermore, in these patients, the highest rate of csPCa detection was in anterior lesions [42.9% (p = 0.067)]. CONCLUSIONS: Our results suggest that standard biopsy could be safely omitted in patients with anterior lesions and in those with PI-RADS-5 lesions. Targeted biopsy for PI-RADS-3 lesions would be less effective in peripheral lesions with a previous negative biopsy.

Clinical evaluation and disease management of PI-RADS 3 lesions. Analysis from a single tertiary high-volume center.

OBJECTIVE: To evaluate the clinical and pathological implications of Prostate Cancer (PCa) patients with a Prostate Imaging - Reporting and Data System (PI-RADS) 3 lesion at multi parametric magnetic resonance imaging (mpMRI). METHODS: We included 356 patients with a PI-RADS score 3 lesion at mpMRI who underwent prostate biopsy for a suspect of PCa at a single tertiary high-volume centre between 2013 and 2016.We developed Uni- (UVA) and multi variable (MVA) logistic regression analyses assessing the predictors of three endpoints: 1) diagnosis of PCa, 2) active surveillance (AS) criteria and 3) clinically significant (CS) PCa at final pathology. RESULTS: PCa was diagnosed in 285 patients (80%), out of these 154 (56%) were eligible for AS according to Prostate Cancer Research International Active Surveillance (PRIAS) criteria. Over the 228 (64%) patients who underwent surgery, 93 (40.8%) had a CS disease at final pathology. Hundred and ninety-three (84.6%) had a pT2 disease and 35 (15.4%) had a pT3 disease. The size of the main lesion, age, PSA and prostate volume efficiently predicted PCa at MVA (all p < 0.05). None of our predictors were significantly associated with AS characteristics. Over those patients who underwent surgery, the biopsy Gleason Score (p = 0.007) efficiently predicted a CS PCa at final pathology. CONCLUSIONS: mpMRI-detected PI-RADS 3 lesions should be sent to a prostate biopsy if other clinical parameters suggest the presence of a PCa. In case of diagnosis of a PCa, patients should undergo confirmatory biopsy before being included in AS protocols to avoid underestimation of a CS disease.

The effect of multiplicity of PI-RADS 3 lesions on cancer detection rate of confirmatory targeted biopsy in patients diagnosed with prostate cancer and managed with active surveillance.

BACKGROUND AND OBJECTIVE: To determine the effect of multiplicity of prostate imaging reporting and data system assessment category 3 (PI-RADS 3) lesions on cancer detection rate (CDR) of confirmatory targeted biopsy of such lesion in patients diagnosed with prostate cancer and managed with active surveillance. METHODS: This study was conducted at a single academic institution. There were 91 men with ≥ 1 PI-RADS 3 lesion detected through magnetic resonance imaging (MRI) after systematic prostate biopsy in the course of management of patients diagnosed with prostate cancer with active surveillance. We compared the CDRs based on targeted biopsy of PI-RADS 3 lesions that occurred (1) as solitary lesions, (2) as 1 of multiple PI-RADS 3 only lesions, or (3) with ≥ 1 higher grade lesion. RESULTS: Median age was 65.0 years (interquartile range 59.5-70.0), median prostate specific antigen was 5.95 ng/ml (interquartile range 4.30-8.83), and median prostate specific antigen density was 0.161 ng/ml2 (0.071-0.194). Forty-three men had solitary PI-RADS 3 lesions, 22 had multiple PI-RADS 3 only lesions, and 26 had multiple lesions with ≥ 1 higher grade lesion. The overall CDR (Gleason score ≥ 3 + 3) based on confirmatory MRI targeted biopsy in a given PI-RADS 3 lesion in each group was 23%, 45%, and 54%, respectively (P = 0.0274). The CDRs for clinically significant disease (Gleason score ≥ 3 + 4) were 16%, 32%, and 35%, respectively (P = 0.1701). CONCLUSIONS: Coexisting lesions increase the CDR of confirmatory MRI targeted biopsy of PI-RADS 3 lesions in patients managed with active surveillance. Risk stratification algorithms for PI-RADS 3 lesion to guide biopsy and management decisions may consider including multiplicity of lesions.