Biparametric vs. Multiparametric MRI in the Detection of Cancer in Transperineal Targeted-Biopsy-Proven Peripheral Prostate Cancer Lesions Classified as PI-RADS Score 3 or 3+1: The Added Value of ADC Quantification.

BACKGROUND: Biparametric MRI (bpMRI) has an important role in the diagnosis of prostate cancer (PCa), by reducing the cost and duration of the procedure and adverse reactions. We assess the additional benefit of the ADC map in detecting prostate cancer (PCa). Additionally, we examine whether the ADC value correlates with the presence of clinically significant tumors (csPCa). METHODS: 104 peripheral lesions classified as PI-RADS v2.1 score 3 or 3+1 at the mpMRI underwent transperineal MRI/US fusion-guided targeted biopsy. RESULTS: The lesions were classified as PI-RADS 3 or 3+1; at histopathology, 30 were adenocarcinomas, 21 of which were classified as csPCa. The ADC threshold that maximized the Youden index in order to predict the presence of a tumor was 1103 (95% CI (990, 1243)), with a sensitivity of 0.8 and a specificity of 0.59; both values were greater than those found using the contrast medium, which were 0.5 and 0.54, respectively. Similar results were also found with csPCa, where the optimal ADC threshold was 1096 (95% CI (988, 1096)), with a sensitivity of 0.86 and specificity of 0.59, compared to 0.49 and 0.59 observed in the mpMRI. CONCLUSIONS: Our study confirms the possible use of a quantitative parameter (ADC value) in the risk stratification of csPCa, by reducing the number of biopsies and, therefore, the number of unwarranted diagnoses of PCa and the risk of overtreatment.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions.

PURPOSE OF REVIEW: In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC. RECENT FINDINGS: In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy. SUMMARY: The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation.

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.

Advancing Prostate Cancer Staging: A Single-Step Approach With Bi-parametric and Whole-Body Diffusion MRI in an African Cohort.

OBJECTIVES:  To document our initial experience using whole-body diffusion-weighted magnetic resonance imaging (WB-DWI/MRI) and bi-parametric magnetic resonance imaging (bpMRI) as a single exam in the staging of biopsy-proven prostate cancers. METHODS: This retrospective study involved 120 African men with biopsy-confirmed prostate cancer (PCa). All the participants had a single exam that included both a bpMRI and a WB-DWI/MRI. The results were analyzed based on the American Urological Association's risk stratification system and evaluated using descriptive statistics. RESULTS: The combined imaging approach confirmed PCa in all cases, identifying pelvic lymph node metastases in 21 (17.5%) patients. Among 72 high-risk patients, bpMRI+WB-DWI/MRI detected pelvic lymph node metastases in 18 (25.0%), bone metastases in 15 (20.8%), retroperitoneal lymph node metastases in six (8.3%), and extraprostatic extension in 18 (25%), with no solid organ metastases observed. CONCLUSION: The combination of WB-DWI/MRI and bpMRI in a single-step approach demonstrates diagnostic potential in primary prostate cancer staging for high-risk groups, with the added advantage of shorter examination times, lower patients' costs, and elimination of the risks of adverse events associated with the use of contrast agents and exposure to radiation.

Assessment of the accuracy of biparametric MRI/TRUS fusion-guided biopsy for index tumor evaluation using postoperative pathology specimens.

BACKGROUND: Multiparametric MRI (mpMRI) is widely used for the diagnosis, surveillance, and staging of prostate cancer. However, it has several limitations, including higher costs, longer examination times, and the use of gadolinium-based contrast agents. This study aimed to investigate the accuracy of preoperatively assessed index tumors (ITs) using biparametric MRI (bpMRI)/transrectal ultrasound (TRUS) fusion biopsy compared with radical prostatectomy (RP) specimens. METHODS: We included 113 patients diagnosed with prostate cancer through bpMRI/TRUS fusion-guided biopsies of lesions with a Prostate Imaging Reporting and Data System (PI-RADS) category ≥ 3. These patients underwent robot-assisted laparoscopic radical prostatectomy (RARP) at our institution between July 2017 and March 2023. We examined the localization of preoperative and postoperative ITs, the highest Gleason score (GS), and tumor diameter in these patients. RESULTS: The preoperative cT stage matched the postoperative pT stage in 53 cases (47%), while 31 cases (27%) were upstaged, and 29 cases (26%) were downstaged (Weighted Kappa = 0.21). The preoperative and postoperative IT localizations were consistent in 97 cases (86%). The concordance rate between Gleason groups in targeted biopsies and RP specimens was 51%, with an upgrade in 25 cases (23%) and a downgrade in 27 cases (25%) (Weighted Kappa = 0.42). The maximum diameter of the IT and the maximum cancer core length on biopsy were correlated with the RP tumor's maximum diameter (p < 0.001 for both). CONCLUSION: The diagnostic accuracy of bpMRI/TRUS fusion biopsy is comparable to mpMRI, suggesting that it can be a cost-effective and time-saving alternative.

Development and validation of a nomogram for predicting prostate cancer based on combining contrast-enhanced transrectal ultrasound and biparametric MRI imaging.

Objectives: This study was to explore the feasibility of combining contrast-enhanced transrectal ultrasound (CE-TRUS) with biparametric MRI (CEUS-BpMRI) score for diagnosing prostate cancer (PCa). Methods: A total of 183 patients with suspected PCa who underwent multiparametric MRI (Mp-MRI) and CE-TRUS were included. CEUS-BpMRI score was developed based on the results of Mp-MRI and CE-TRUS. The diagnostic performance was evaluated by the area under the curve (AUC). The diagnostic efficacy of the CEUS-BpMRI score, BpMRI score, and PI-RADS v2.1 score were compared. Total patients were randomly assigned to a training cohort (70%) or validation cohort (30%). A nomogram was constructed based on univariate and multivariate logistic regression. The model was evaluated by AUC and calibration curve. Results: The diagnostic performance of CEUS-BpMRI score (AUC 0.857) was comparable to that of PI-RADS v2.1 (AUC 0.862) (P = 0.499), and both were superior to Bp-MRI score (AUC 0.831, P < 0.05). In peripheral zone lesions with Bp-MRI score of 3, there was no statistically significant difference between PI-RADS v2.1 score (AUC 0.728) and CEUS-BpMRI score (AUC 0.668) (P = 0.479). Multivariate analysis showed that age, total prostate specific antigen/free prostate specific antigen (F/T), time to peak (TTP), and CEUS-BpMRI score were independent factors. The AUC of the nomogram was 0.909 in the training cohort and 0.914 in the validation cohort. Conclusions: CEUS-BpMRI score has high diagnostic efficacy for diagnosing PCa. A nomogram model established by combining age, F/T, TTP, and CEUS-BpMRI score can achieve the best predictive accuracy for PCa.

Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging.

OBJECTIVES: To develop diagnostic algorithms of multisequence prostate magnetic resonance imaging for cancer detection and segmentation using deep learning and explore values of dynamic contrast-enhanced imaging in multiparametric imaging, compared with biparametric imaging. METHODS: We collected 3227 multiparametric imaging sets from 332 patients, including 218 cancer patients (291 biopsy-proven foci) and 114 noncancer patients. Diagnostic algorithms of T2-weighted, T2-weighted plus dynamic contrast-enhanced, biparametric, and multiparametric imaging were built using 2578 sets, and their performance for clinically significant cancer was evaluated using 649 sets. RESULTS: Biparametric and multiparametric imaging had following region-based performance: sensitivity of 71.9% and 74.8% (p = 0.394) and positive predictive value of 61.3% and 74.8% (p = 0.013), respectively. In side-specific analyses of cancer images, the specificity was 72.6% and 89.5% (p < 0.001) and the negative predictive value was 78.9% and 83.5% (p = 0.364), respectively. False-negative cancer on multiparametric imaging was smaller (p = 0.002) and more dominant with grade group ≤2 (p = 0.028) than true positive foci. In the peripheral zone, false-positive regions on biparametric imaging turned out to be true negative on multiparametric imaging more frequently compared with the transition zone (78.3% vs. 47.2%, p = 0.018). In contrast, T2-weighted plus dynamic contrast-enhanced imaging had lower specificity than T2-weighted imaging (41.1% vs. 51.6%, p = 0.042). CONCLUSIONS: When using deep learning, multiparametric imaging provides superior performance to biparametric imaging in the specificity and positive predictive value, especially in the peripheral zone. Dynamic contrast-enhanced imaging helps reduce overdiagnosis in multiparametric imaging.

Literature review: Imaging in prostate cancer.

Imaging plays an increasingly important role in the detection and characterization of prostate cancer (PC). This review summarizes the key conventional and advanced imaging modalities including multiparametric magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging and tries to instruct clinicians in finding the best image modality depending on the patient`s PC-stage. We aim to give an overview of the different image modalities and their benefits and weaknesses in imaging PC. Emphasis is put on primary prostate cancer detection and staging as well as on recurrent and castration resistant prostate cancer. Results from studies using various imaging techniques are discussed and compared. For the different stages of PC, advantages and disadvantages of the different imaging modalities are discussed. Moreover, this review aims to give an outlook about upcoming, new imaging modalities and how they might be implemented in the future into clinical routine. Imaging patients suffering from PC should aim for exact diagnosis, accurate detection of PC lesions and should mirror the true tumor burden. Imaging should lead to the best patient treatment available in the current PC-stage and should avoid unnecessary therapeutic interventions. New image modalities such as long axial field of view PET/CT with photon-counting CT and radiopharmaceuticals like androgen receptor targeting radiopharmaceuticals open up new possibilities. In conclusion, PC imaging is growing and each image modality is aiming for improvement.

Could Biparametric MRI Replace Multiparametric MRI in the Management of Prostate Cancer?

Prostate cancer (PCa) is a worldwide epidemiological problem, since it is one of the most prevalent types of neoplasia among men, and the third-leading cause of cancer-related deaths, after lung and colorectal tumors. Unfortunately, the early stages of PCa have a wide range of unspecific symptoms. For these reasons, early diagnosis and accurate evaluation of suspicious lesions are crucial. Multiparametric MRI (mpMRI) is currently the imaging modality of choice for diagnostic screening and local staging of PCa, but also has a leading role in guiding biopsies and in treatment biparametric MRI (bpMRI) could partially replace mpMRI due to its lack of adverse reactions caused by contrast agents, relatively lower costs, and shorter acquisition time. Further, 31 relevant articles regarding the advantages and disadvantages of the aforementioned imaging techniques were scanned. As a result, while bpMRI has comparable accuracy in detecting PCa, its roles in the other steps of PCa management are limited.

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer.

BACKGROUND: Preoperative assessment of lymphovascular invasion(LVI) of rectal cancer has very important clinical significance. However, accurate preoperative imaging evaluation of LVI is highly challenging because the resolution of MRI is still limited. Relatively few studies have focused on prediction of LVI of rectal cancer with the tool of radiomics, especially in patients with negative statue of MRI-based extramural vascular invasion (mrEMVI).The purpose of this study was to explore the preoperative predictive value of biparametric MRI-based radiomics features for LVI of rectal cancer in patients with the negative statue of mrEMVI. METHODS: The data of 146 cases of rectal adenocarcinoma confirmed by postoperative pathology were retrospectively collected. In the cases, 38 had positive status of LVI. All patients were examined by MRI before the operation. The biparametric MRI protocols included T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). We used whole-volume three-dimensional method and two feature selection methods, minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO), to extract and select the features. Logistics regression was used to construct models. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the diagnostic performance of the radiomics based on T2WI and DWI and the combined models. RESULTS: Radiomics models based on T2WI and DWI had good predictive performance for LVI of rectal cancer in both the training cohort and the validation cohort. The AUCs of the T2WI model were 0.87 and 0.87, and the AUCs of the DWI model were 0.94 and 0.92. The combined model was better than the T2WI model, with AUCs of 0.97 and 0.95. The predictive performance of the DWI model was comparable to that of the combined model. CONCLUSIONS: The radiomics model based on biparametric MRI, especially DWI, had good predictive value for LVI of rectal cancer. This model has the potential to facilitate the clinical recognition of LVI in rectal cancer preoperatively.

The diagnostic performance in clinically significant prostate cancer with PI-RADS version 2.1: simplified bpMRI versus standard mpMRI.

OBJECTIVES: To compare the diagnostic performance for the detection of clinically significant prostate cancer (csPCa) between bpMRI with only axial T2WI (simplified bpMRI) and standard-multiparametric MRI (mpMRI). METHODS: A total of 569 patients who underwent mpMRI followed by biopsy or prostatectomy were enrolled in this retrospective study. According to PI-RADS v2.1, three radiologists (A, B, C) from three centers blinded to clinical variables were assigned scores on lesions with simplified bpMRI and then with mpMRI 2 weeks later. Diagnostic performance of simplified bpMRI was compared with mpMRI using histopathology as reference standard. RESULTS: For all the three radiologists, the diagnostic sensitivity was significantly higher with mpMRI than with simplified bpMRI (P < 0.001 to P = 0.035); and although specificity was also higher with mpMRI than with simplified bpMRI for radiologist B and radiologist C, it was statistically significant only for radiologist B (P = 0.011, P = 0.359, respectively). On the contrary, for radiologist A, specificity was higher with simplified bpMRI than with mpMRI (P = 0.001). The area under the receiver operating characteristic curve (AUC) was significantly higher for mpMRI than for simplified bpMRI except for radiologist A (radiologist A: 0.903 vs 0.913, P = 0.1542; radiologist B: 0.861 vs 0.834 P = 0.0013; and radiologist C: 0.884 vs 0.848, P = 0.0003). Interobserver reliability of PI-RADS v2.1 showed good agreement for both simplified bpMRI (kappa = 0.665) and mpMRI (kappa = 0.739). CONCLUSION: Although the detection of csPCa with simplified bpMRI was comparatively lower than that with mpMRI, the diagnostic performance was still high in simplified bpMRI. Our data justify using mpMRI outperforms simplified bpMRI for prostate cancer screening and imply simplified bpMRI as a potential screening tool.

Diagnostic Accuracy of MRI for Solid Renal Masses: A Systematic Review and Meta-analysis.

BACKGROUND: Biparametric (bp)-MRI and multiparametric (mp)-MRI may improve the diagnostic accuracy of renal mass histology. PURPOSE: To evaluate the available evidence on the diagnostic accuracy of bp-MRI and mp-MRI for solid renal masses in differentiating malignant from benign, aggressive from indolent, and clear cell renal cell carcinoma (ccRCC) from other histology. STUDY TYPE: Systematic review. POPULATION: MEDLINE, EMBASE, and CENTRAL up to January 11, 2022 were searched. FIELD STRENGTH/SEQUENCE: 1.5 or 3 Tesla. ASSESSMENT: Eligible studies evaluated the accuracy of MRI (with at least two sequences: T2, T1, dynamic contrast and diffusion-weighted imaging) for diagnosis of solid renal masses in adult patients, using histology as reference standard. Risk of bias and applicability were assessed using QUADAS-2. STATISTICAL TESTS: Meta-analysis using a bivariate logitnormal random effects model. RESULTS: We included 10 studies (1239 masses from approximately 1200 patients). The risk of bias was high in three studies, unclear in five studies and low in two studies. The diagnostic accuracy of malignant (vs. benign) masses was assessed in five studies (64% [179/281] malignant). The summary estimate of sensitivity was 95% (95% confidence interval [CI]: 77%-99%), and specificity was 63% (95% CI: 46%-77%). No study assessed aggressive (vs. indolent) masses. The diagnostic accuracy of ccRCC (vs. other subtypes) was evaluated in six studies (47% [455/971] ccRCC): the summary estimate of sensitivity was 85% (95% CI: 77%-90%) and specificity was 77% (95% CI: 73%-81%). DATA CONCLUSION: Our study reveals deficits in the available evidence on MRI for diagnosis of renal mass histology. The number of studies was limited, at unclear/high risk of bias, with heterogeneous definitions of solid masses, imaging techniques, diagnostic criteria, and outcome measures. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

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.

Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study.

BACKGROUND: The high level of expertise required for accurate interpretation of prostate MRI. PURPOSE: To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI. STUDY TYPE: Retrospective. SUBJECTS: One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test). FIELD STRENGTH/SEQUENCE: 3.0T/scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and apparent diffusion coefficient map. ASSESSMENT: Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology. STATISTICAL TESTS: Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I2 analysis. RESULTS: In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I2  = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05). DATA CONCLUSION: Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers.

OBJECTIVE: To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. MATERIALS AND METHODS: A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. RESULTS: Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). CONCLUSIONS: AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.

Diagnostic value of integrated 18F-PSMA-1007 PET/MRI compared with that of biparametric MRI for the detection of prostate cancer.

Objective: To assess the diagnostic value of fluorine 18 (18F)-labeled prostate-specific membrane antigen (PSMA)-1007 Positron emission tomography/Magnetic resonance imaging (PET/MRI) and compared with that of biparametric MRI (bpMRI) for the detection of prostate cancer (PCa). Materials and methods: The study enrolled 29 patients with suspected PCa preoperatively who underwent 18F-PSMA-1007 PET/MRI and subsequent targeted biopsy for suspected PCa lesions. Two readers independently assessed the images of each suspected PCa lesion and determined their overall assessment category on bpMRI and 18F-PSMA-1007 PET/MRI. By using biopsy histopathology as the reference standard, the accuracies of 18F-PSMA-1007 PET/MRI and bpMRI for the detection of PCa lesion were determined. Furthermore, the receiver-operating characteristic (ROC) curves of their semi-quantitative parameters of the optimal standardized uptake value (SUVmax) and apparent diffusion coefficient (ADC) for detecting PCa lesions were derived, and their correlations with the International Society of Urological Pathology (ISUP) grade were reported. Results: Of the 48 suspected PCa lesions in 29 patients, 38 were pathologically diagnosed with clinically significant PCa and 10 with nonprostate cancer (non-PCa) lesions. Compared with the pathological results, 18F-PSMA-1007 PET/MRI demonstrated much greater diagnostic accuracy (area under the curve, AUC), sensitivity, specificity, positive predictive value, and negative predictive value than bpMRI: 0.974 versus 0.711, 94.74% versus 92.11%, 100% versus 50%, 100% versus 87.50%, and 83.33% versus 62.50%, respectively. The semi-quantitative parameters of SUVmax demonstrated a higher AUC of 0.874 than that of ADC with 0.776 for detecting PCa. The ISUP grade was positively associated with SUVmax at spearman's rho correlation coefficient (Rho) = 0.539, p = 0), but not associated with ADC (Rho = -0.105, p = 0.529). Conclusion: The diagnostic value of 18F-PSMA-1007 PET/MRI for the detection of PCa is better than that of bpMRI, and a high SUVmax may indicate a lesion with a high ISUP grade.

Utility of Clinical-Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions.

Purpose: To determine the predictive performance of the integrated model based on clinical factors and radiomic features for the accurate identification of clinically significant prostate cancer (csPCa) among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. Materials and Methods: A retrospective study of 103 patients with PI-RADS 3 lesions who underwent pre-operative 3.0-T MRI was performed. Patients were randomly divided into the training set and the testing set at a ratio of 7:3. Radiomic features were extracted from axial T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images of each patient. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) feature selection methods were used to identify the radiomic features and construct a radiomic model for csPCa identification. Moreover, multivariable logistic regression analysis was used to integrate the clinical factors with radiomic feature model to further improve the accuracy of csPCa identification, and the two are presented in the form of normogram. The performance of the integrated model was compared with radiomic model and clinical model on testing set. Results: A total of four radiomic features were selected and used for radiomic model construction producing a radiomic score (Radscore). Radscore was significantly different between the csPCa and the non-csPCa patients (training set: p < 0.001; testing set: p = 0.035). Multivariable logistic regression analysis showed that age and PSA could be used as independent predictors for csPCa identification. The clinical-radiomic model produced the receiver operating characteristic (ROC) curve (AUC) in the testing set was 0.88 (95%CI, 0.75-1.00), which was similar to clinical model (AUC = 0.85; 95%CI, 0.52-0.90) (p = 0.048) and higher than the radiomic model (AUC = 0.71; 95%CI, 0.68-1.00) (p < 0.001). The decision curve analysis implies that the clinical-radiomic model could be beneficial in identifying csPCa among PI-RADS 3 lesions. Conclusion: The clinical-radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients.

Diagnostic Accuracy of Abbreviated Bi-Parametric MRI (a-bpMRI) for Prostate Cancer Detection and Screening: A Multi-Reader Study.

(1) Background: There is currently limited evidence on the diagnostic accuracy of abbreviated biparametric MRI (a-bpMRI) protocols for prostate cancer (PCa) detection and screening. In the present study, we aim to investigate the performance of a-bpMRI among multiple readers and its potential application to an imaging-based screening setting. (2) Methods: A total of 151 men who underwent 3T multiparametric MRI (mpMRI) of the prostate and transperineal template prostate mapping biopsies were retrospectively selected. Corresponding bpMRI (multiplanar T2WI, DWI, ADC maps) and a-bpMRI (axial T2WI and b 2000 s/mm2 DWI only) dataset were derived from mpMRI. Three experienced radiologists scored a-bpMRI, standard biparametric MRI (bpMRI) and mpMRI in separate sessions. Diagnostic accuracy and interreader agreement of a-bpMRI was tested for different positivity thresholds and compared to bpMRI and mpMRI. Predictive values of a-bpMRI were computed for lower levels of PCa prevalence to simulate a screening setting. The primary definition of clinically significant PCa (csPCa) was Gleason ≥ 4 + 3, or cancer core length ≥ 6 mm. (3) Results: The median age was 62 years, the median PSA was 6.8 ng/mL, and the csPCa prevalence was 40%. Using a cut off of MRI score ≥ 3, the sensitivity and specificity of a-bpMRI were 92% and 48%, respectively. There was no significant difference in sensitivity compared to bpMRI and mpMRI. Interreader agreement of a-bpMRI was moderate (AC1 0.58). For a low prevalence of csPCa (e.g., <10%), higher cut offs (MRI score ≥ 4) yield a more favourable balance between the predictive values and positivity rate of MRI. (4) Conclusion: Abbreviated bpMRI protocols could match the diagnostic accuracy of bpMRI and mpMRI for the detection of csPCa. If a-bpMRI is used in low-prevalence settings, higher cut-offs for MRI positivity should be prioritised.

Clinically Significant Prostate Cancer Detection After a Negative Prebiopsy MRI Examination: Comparison of Biparametric Versus Multiparametric MRI.

BACKGROUND. The frequency of clinically significant prostate cancer (csPCa) following negative biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) has not been well investigated in direct comparative studies. OBJECTIVE. The purposes of this study were to compare the frequency of csPCa after negative prebiopsy bpMRI and mpMRI and to evaluate factors predictive of csPCa in the two cohorts. METHODS. This retrospective study included 232 men (mean age, 64.5 years) with negative bpMRI from August 2017 to March 2020 and 193 men (mean age, 69.0 years) with negative mpMRI from January 2018 to December 2018. PI-RADS category 1 or 2 was defined as negative. The study institution offered bpMRI as a low-cost self-pay option for patients without insurer coverage of prebiospy mpMRI. Patient characteristics and subsequent biopsy results were recorded. CsPCa was defined as Gleason score of 3 + 4 or greater. Multivariable regression analyses were performed to identify independent predictors of csPCa. The AUC of PSA density (PSAD) for csPCA was computed, and the diagnostic performance of PSAD was assessed at a clinically established threshold of 0.15 ng/mL2. RESULTS. Systematic biopsy was performed after negative bpMRI for 41.4% (96/232) of patients and after negative mpMRI for 30.5% (59/193) (p = .02). Among those undergoing biopsy, csPCa was present in 15.6% (15/96) in the bpMRI cohort versus 13.6% (8/59) in the mpMRI cohort (p = .69). The NPV for csPCa was 84% (81/96) for bpMRI and 86% (51/59) for mpMRI. In multivariable analyses, independent predictors of csPCa included smaller prostate volume (OR, 0.27; p < .001) and greater PSAD (OR, 3.09; p < .001). In multivariable models, bpMRI (compared with mpMRI) was not independently predictive of csPCa (p > .05). PSAD had an AUC for csPCa of 0.71 (95% CI, 0.56-0.87) in the bpMRI cohort versus 0.68 (95% CI, 0.42-0.93) in the mpMRI cohort. For detecting csPCa, a PSAD threshold of 0.15 ng/mL2 had NPV of 90% and PPV of 28%, in the bpMRI cohort versus NPV of 92% and PPV of 44% in the mpMRI cohort. CONCLUSION. The frequencies of csPCa were not significantly different at systematic biopsy performed after negative bpMRI and mpMRI examinations. PSAD had similar diagnostic utility for csPCa in the two cohorts. CLINICAL IMPACT. Either bpMRI or mpMRI, in combination with PSAD measurement, can help avoid negative prostate biopsies.

Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer.

BACKGROUND: Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group ≥ 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI). PURPOSE: To develop and validate radiomics and kallikrein models for the detection of csPCa. STUDY TYPE: Retrospective. POPULATION: A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n = 72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n = 288; split 2: remaining 50%) were evaluated. FIELD STRENGTH/SEQUENCE: A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI. ASSESSMENT: In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores. STATISTICAL TESTS: For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value < 0.05 were considered significant. RESULTS: The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC = 0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC = 0.83, P-value = 0.306; split 2: AUC = 0.83, P-value = 0.488). DATA CONCLUSION: The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.