Bi-Parametric Magnetic Resonance Imaging Analysis of Biochemical Recurrence of Prostate Cancer after Radical Surgery and Its Predictive Value: A Retrospective Study.

OBJECTIVE: This study aimed to analyse the characteristics of biochemical recurrence after radical prostatectomy via bi-parametric magnetic resonance imaging. METHODS: A total of 200 patients with radical prostatectomy admitted to our hospital from January 2016 to January 2021 were retrospectively enrolled as observation objects. According to whether there was biochemical recurrence after surgery, the patients were divided into the abnormal group (n = 62) and normal group (n = 138). Clinical data, encapsulation infiltration, seminal vesicle infiltration and prostate imaging report and data system (PI-RADS) were collected and compared between the two groups. Propensity score matching (PSM) was used to balance the baseline data of the two groups. Student's t-test and Chi-square test were used to analyse the data. RESULTS: PSM was performed in a 1:1 ratio, and a total of 72 patients were included in the abnormal and normal groups. The baseline data of the patients in each group were not statistically significant. The incidence of extraperitoneal invasion and seminal vesicle invasion was higher in the abnormal group than in the normal group, and we observed a significant difference in PI-RADS scores between the two groups (p < 0.05). Extracapsular invasion, seminal vesicle invasion, PI-RADS score and biochemical recurrence were significantly correlated (p < 0.05). The PI-RADS score has a high value for predicting biochemical recurrence, with an area under the curve value of 0.824, sensitivity of 0.667, specificity of 0.861 and Youden index of 0.528. CONCLUSIONS: Bi-parametric magnetic resonance imaging has a high predictive value in biochemical recurrence after radical prostatectomy, which can provide reference for early intervention measures.

Autonomous Tumor Signature Extraction Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study.

BACKGROUND: Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. METHODS: Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. RESULTS: The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob. CONCLUSIONS: The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.

INTEGRATING PROSTATE SPECIFIC ANTIGEN DENSITY BIOMARKER INTO DEEP LEARNING PROSTATE MRI LESION SEGMENTATION MODELS.

Prostate cancer lesion segmentation in multi-parametric magnetic resonance imaging (mpMRI) is crucial for pre-biopsy diagnosis and targeted biopsy guidance. Deep convolution neural networks have been widely utilized for lesion segmentation. However, these methods fail to achieve a high Dice coefficient because of the large variations in lesion size and location within the gland. To address this problem, we integrate the clinically-meaningful prostate specific antigen density (PSAD) biomarker into the deep learning model using feature-wise transformations to condition the features in latent space, and thus control the size of lesion prediction. We tested our models on a public dataset with 214 annotated mpMRI scans and compared the segmentation performance to a baseline 3D U-Net model. Results demonstrate that integrating the PSAD biomarker significantly improves segmentation performance in both Dice coefficient and centroid distance metric.

Bi-parametric prostate MRI with a recall system for contrast enhanced imaging: Improving accessibility while maintaining quality.

PURPOSE: To review the efficacy of a recall system for bi-parametric non-contrast prostate MRI (bp-MRI). METHODS: A bi-parametric protocol was instituted in July 2020 for all patients who had a prostate MRI requested, excluding those after treatment of prostate cancer, patients with hip prosthesis or pacemaker, and those who lived out-of-town. The protocol consisted of tri-planar T2-weighted and diffusion weighted images (DWI) (b = 50, 800 s/mm2 for ADC map; b = 1,500 s/mm2 acquired separately) in accordance with the Prostate Imaging Reporting & Data system (PI-RADS) v2.1 guidelines. After interpretation of bp-MRI exams, patients with equivocal (PI-RADS 3) lesions in peripheral zone (PZ) or any technical limitations were recalled for contrast administration. RESULTS: Out of 909 bp-MRI scans performed from July 2020 to April 2021, only 52 (5.7 %) were recalled, of which 46 (88.5 %) attended. Amongst these, 41/52 (78.8 %) were recalled for PZ PI-RADS 3 lesions, while the rest of 11 (21.2 %) cases were recalled for technical reasons. Mean time to subsequent recall scan was 11.6 days. On assessment of post-contrast imaging, 29/46 (63 %) cases were upgraded to PI-RADS 4 while 17/46 (37 %) remained PI-RADS 3. This system avoided contrast-agent use in 857 patients, with contrast cost savings of €64,620 (US$68,560) and 214 hours 15 minutes of scanner time was saved. This allowed 255 additional bp-MRI scans to be performed, reducing the waitlist from 1 year to 2-3 weeks. CONCLUSION: A bi-parametric prostate MRI protocol with a robust recall system for contrast administration not only saved time eliminating the marked backlog but was also more cost efficient without compromising the quality of patient care.

Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study.

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.

Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study.

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of Magnetic Resonance Imaging (MRI) sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement (DCE) in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material and prolonging scanning time. More accurate quantitative MRI without DCE and artificial intelligence approaches are needed. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and correlate with the International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasive management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx + SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade ≥ 2 (<2) was judged as CsPCa (CiPCa). Linear and logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R ~ 0.17). Patients assessed with combined MRBx + SysBx and from individual MRI scanners achieved higher R (DR = 0.207+/-0.118) than all patients used in the fits. Conclusions: In the first study, to date, spectral approaches for assessing tumor aggressiveness on SRBP-MRI have been applied and tested and achieved high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA, respectively.

Dual attention guided multiscale neural network trained with curriculum learning for noninvasive prediction of Gleason Grade Group from MRI.

BACKGROUND: The Gleason Grade Group (GG) is essential in assessing the malignancy of prostate cancer (PCa) and is typically obtained by invasive biopsy procedures in which sampling errors could lead to inaccurately scored GGs. With the gradually recognized value of bi-parametric magnetic resonance imaging (bpMRI) in PCa, it is beneficial to noninvasively predict GGs from bpMRI for early diagnosis and treatment planning of PCa. However, it is challenging to establish the connection between bpMRI features and GGs. PURPOSE: In this study, we propose a dual attention-guided multiscale neural network (DAMS-Net) to predict the 5-scored GG from bpMRI and design a training curriculum to further improve the prediction performance. METHODS: The proposed DAMS-Net incorporates a feature pyramid network (FPN) to fully extract the multiscale features for lesions of varying sizes and a dual attention module to focus on lesion and surrounding regions while avoiding the influence of irrelevant ones. Furthermore, to enhance the differential ability for lesions with the inter-grade similarity and intra-grade variation in bpMRI, the training process employs a specially designed curriculum based on the differences between the radiological evaluations and the ground truth GGs. RESULTS: Extensive experiments were conducted on a private dataset of 382 patients and the public PROSTATEx-2 dataset. For the private dataset, the experimental results showed that the proposed network performed better than the plain baseline model for GG prediction, achieving a mean quadratic weighted Kappa (Kw ) of 0.4902 and a mean positive predictive value of 0.9098 for predicting clinically significant cancer (PPVGG>1 ). With the application of curriculum learning, the mean Kw and PPVGG>1 further increased to 0.5144 and 0.9118, respectively. For the public dataset, the proposed method achieved state-of-the-art results of 0.5413 Kw and 0.9747 PPVGG>1 . CONCLUSION: The proposed DAMS-Net trained with curriculum learning can effectively predict GGs from bpMRI, which may assist clinicians in early diagnosis and treatment planning for PCa patients.

Improving Prostate Cancer Detection With MRI: A Multi-Reader, Multi-Case Study Using Computer-Aided Detection (CAD).

RATIONALE AND OBJECTIVES: To evaluate whether addition of a computer-aided diagnostic (CAD) generated MRI series improves detection of clinically significant prostate cancer. MATERIALS AND METHODS: Nine radiologists retrospectively interpreted 150 prostate MRI examinations without and then with an additional random forest-based CAD model-generated MRI series. Characteristics of biopsy negative versus positive (Gleason ≥ 7 adenocarcinoma) groups were compared using the Wilcoxon test for continuous and Pearson's chi-squared test for categorical variables. The diagnostic performance of readers was compared without versus with CAD using MRMC methods to estimate the area under the receiver operator characteristic curve (AUC). Inter-reader agreement was assessed using weighted inter-rater agreement statistics. Analyses were repeated in peripheral and transition zone subgroups. RESULTS: Among 150 men with median age 67 ± 7.4 years, those with clinically significant prostate cancer were older (68 ± 7.6 years vs. 66 ± 7.0 years; p < .02), had smaller prostate volume (43.9 mL vs. 60.6 mL; p < .001), and no difference in prostate specific antigen (PSA) levels (7.8 ng/mL vs. 6.9 ng/mL; p = .08), but higher PSA density (0.17 ng/mL/cc vs. 0.10 ng/mL/cc; p < .001). Inter-rater agreement (IRA) for PI-RADS scores was moderate without CAD and significantly improved to substantial with CAD (IRA = 0.47 vs. 0.65; p < .001). CAD also significantly improved average reader AUC (AUC = 0.72, vs. AUC = 0.67; p = .02). CONCLUSION: Addition of a random forest method-based, CAD-generated MRI image series improved inter-reader agreement and diagnostic performance for detection of clinically significant prostate cancer, particularly in the transition zone.

Evaluation of clinically significant prostate cancer using biparametric magnetic resonance imaging: An evolving concept.

Background: Multiparametric magnetic resonance imaging (mp-MRI) of prostate involves a combination of T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced (DCE) scans. However, controversy exists in the literature regarding the true value of DCE in the detection of clinically significant (CS) prostate cancer (PCa). Aim: The aim of this study is to compare the role of biparametric MRI (bp-MRI) and mp-MRI in the detection of CS PCa. Materials and Methods: Thirty-six patients with raised serum prostate-specific antigen levels were included. Bp-MRI was performed in all patients, whereas mp-MRI was performed in 30 cases only. The findings were characterized on the basis of prostate imaging reporting and data system (PI-RADS) v2 grading. PI-RADS v2 score of 3 or more was considered CS PCa. All patients underwent transrectal ultrasound-guided biopsy. Gleason score >6 was considered CS. Statistical analysis was done using the SPSS software and results interpreted. Results: CS PCa was observed in 31 cases on histopathology. On bp-MRI, CS PCa was seen in 31 patients. Five cases of PI-RADS v2 score 3 were seen on bp-MRI and 3 of them were upgraded to PI-RADS 4 on DCE images. One case of PI-RADS 3 had low Gleason score on biopsy, whereas 1 case of PI-RADS 2 had CS PCa on biopsy. No significant difference was observed between bp-MRI and mp-MRI in the detection of CS PCa. Conclusions: Both bp-MRI and mp-MRI have high sensitivity, specificity, and diagnostic accuracy and were nearly identical in the detection of CS PCa with no significant advantage of DCE images.

Bi-parametric MRI/TRUS fusion targeted repeat biopsy after systematic 10-12 core TRUS-guided biopsy reveals more significant prostate cancer especially in anteriorly located tumors.

Background: MRI and fusion guided biopsy have an increased role in the diagnosis of prostate cancer. Purpose: To demonstrate the possible advantages with Bi-parametric MRI fusion-guided repeat biopsy over systematic 10-12-core biopsy for the diagnosis of prostate cancer. Material and Methods: Four hundred and twenty-three consecutive men, with previous systematic 10-12-core TRUS-guided biopsy, and with suspicion of, or diagnosis of, low-risk prostate cancer underwent fusion-guided prostate biopsy between February 2015 and February 2017. The material was retrospectively assessed. In 220 cases no previous cancer was diagnosed, and in 203 cases confirmatory fusion guided biopsy was performed prior to active monitoring. MRI was classified according to PI-RADS. Systematic biopsy was compared to fusion guided biopsy for the detection of cancer, and PI-RADS was compared to the Gleason score. Results: Fusion guided biopsy detected significantly more cancers than systematic (p < .001). Gleason scores were higher in the fusion biopsy group (p < .001). Anterior tumors were present in 54% of patients. Fusion biopsy from these lesions showed cancer in 53% with previously negative biopsy in systematic biopsies and 66% of them were upgraded from low risk to intermediate or high-risk cancers. Conclusion: These results show superior detection rate and grading of bi-parametric MRI/TRUS fusion targeted repeat biopsy over systematic 10-12 core biopsies. Fusion guided biopsy detects more significant cancers despite using fewer cores. The risk group was changed for many patients initially selected for active surveillance due to upgrading of tumors. Bi-parametric MRI shows promising results in detecting anterior tumors in patients with suspected prostate cancer.

Synthetic magnetic resonance imaging for primary prostate cancer evaluation: Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements.

PURPOSE: Bi-parametric magnetic resonance imaging (bpMRI) with diffusion-weighted images has wide utility in diagnosing clinically significant prostate cancer (csPCa). However, bpMRI yields more false-negatives for PI-RADS category 3 lesions than multiparametric (mp)MRI with dynamic-contrast-enhanced (DCE)-MRI. We investigated the utility of synthetic MRI with relaxometry maps for bpMRI-based diagnosis of csPCa. METHODS: One hundred and five treatment-naïve patients who underwent mpMRI and synthetic MRI before prostate biopsy for suspected PCa between August 2019 and December 2020 were prospectively included. Three experts and three basic prostate radiologists evaluated the diagnostic performance of conventional bpMRI and synthetic bpMRI for csPCa. PI-RADS version 2.1 category 3 lesions were identified by consensus, and relaxometry measurements (T1-value, T2-value, and proton density [PD]) were performed. The diagnostic performance of relaxometry measurements for PI-RADS category 3 lesions in peripheral zone was compared with that of DCE-MRI. Histopathological evaluation results were used as the reference standard. Statistical analysis was performed using the areas under the receiver operating characteristic curve (AUC) and McNemar test. RESULTS: In 102 patients without significant MRI artefacts, the diagnostic performance of conventional bpMRI was not significantly different from that of synthetic bpMRI for all readers (p = 0.11-0.79). The AUCs of the combination of T1-value, T2-value, and PD (T1 + T2 + PD) for csPCa in peripheral zone for PI-RADS category 3 lesions were 0.85 for expert and 0.86 for basic radiologists, with no significant difference between T1 + T2 + PD and DCE-MRI for both expert and basic radiologists (p = 0.29-0.45). CONCLUSION: Synthetic MRI with relaxometry maps shows promise for contrast media-free evaluation of csPCa.

Use of Prostate Systematic and Targeted Biopsy on the Basis of Bi-Parametric Magnetic Resonance Imaging in Biopsy-Naïve Patients.

OBJECTIVES: To explore the performance of targeted biopsy (TB) in combination with systematic biopsy (SB) in the detection of prostate cancer (PCa) in biopsy naïve patients. METHODS: From May 2018 to January 2020, 230 biopsy-naïve men with suspicious bi-parametric MRI [bpMRI; Prostate Imaging Reporting and Data System (PI-RADS) score ≥3] were enrolled. All patients had prostate-specific antigen (PSA) levels of 20 ng/ml or less. For each patient, transrectal ultrasound-guided prostate biopsy was performed. The primary endpoint was the detection rate of CSPC [clinically-significant PCa, International Society of Urological Pathology grade group (ISUP GG) 2 or higher tumors]. The secondary endpoints were the detection rates of CIPC (clinically insignificant PCa, ISUP GG 1 tumors). RESULTS: CSPC was detected in 90 patients. Twelve (13.33%) of them were detected by TB only and 18 (20.00%) by SB only. Detection of CSPC by SB and TB did not differ significantly (p = .36). In 4.35% of 230 patients, CSPC would have been missed if we performed SB only, and in 6.09% of patients if we performed TB only. Moreover, combination of TB and SB did not increase the detection of CIPC. CONCLUSIONS: No significant difference was found in the detection of CSPC between TB and SB; however, both techniques revealed substantial added value and combination of TB and SB could further improve this detection rate without increasing the detection of CIPC.

Predicting Tumor Perineural Invasion Status in High-Grade Prostate Cancer Based on a Clinical-Radiomics Model Incorporating T2-Weighted and Diffusion-Weighted Magnetic Resonance Images.

PURPOSE: To explore the role of bi-parametric MRI radiomics features in identifying PNI in high-grade PCa and to further develop a combined nomogram with clinical information. METHODS: 183 high-grade PCa patients were included in this retrospective study. Tumor regions of interest (ROIs) were manually delineated on T2WI and DWI images. Radiomics features were extracted from lesion area segmented images obtained. Univariate logistic regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for feature selection. A clinical model, a radiomics model, and a combined model were developed to predict PNI positive. Predictive performance was estimated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. RESULTS: The differential diagnostic efficiency of the clinical model had no statistical difference compared with the radiomics model (area under the curve (AUC) values were 0.766 and 0.823 in the train and test group, respectively). The radiomics model showed better discrimination in both the train cohort and test cohort (train AUC: 0.879 and test AUC: 0.908) than each subcategory image (T2WI train AUC: 0.813 and test AUC: 0.827; DWI train AUC: 0.749 and test AUC: 0.734). The discrimination efficiency improved when combining the radiomics and clinical models (train AUC: 0.906 and test AUC: 0.947). CONCLUSION: The model including radiomics signatures and clinical factors can accurately predict PNI positive in high-grade PCa patients.

Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics.

Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.

Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation.

The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients' data from Siemens (Vendor 1), and the inner test set was 70 patients' data from the same vendor. The external test set was 221 patients' data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616, p = 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797, p = 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798, p = 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886, p = 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.

A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population.

PURPOSE: This study aimed to develop and validate a predictive model for the assessment of clinically significant prostate cancer (csPCa) in men, prior to prostate biopsies, based on bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters. MATERIALS AND METHODS: We retrospectively analyzed 300 men with clinical suspicion of prostate cancer (prostate-specific antigen [PSA] ≥ 4.0 ng/mL and/or abnormal findings in a digital rectal examination), who underwent bpMRI-ultrasound fusion transperineal targeted and systematic biopsies in the same session, at a Korean university hospital. Predictive models, based on Prostate Imaging Reporting and Data Systems scores of bpMRI and clinical parameters, were developed to detect csPCa (intermediate/high grade [Gleason score ≥ 3+4]) and compared by analyzing the areas under the curves and decision curves. RESULTS: A predictive model defined by the combination of bpMRI and clinical parameters (age, PSA density) showed high discriminatory power (area under the curve, 0.861) and resulted in a significant net benefit on decision curve analysis. Applying a probability threshold of 7.5%, 21.6% of men could avoid unnecessary prostate biopsy, while only 1.0% of significant prostate cancers were missed. CONCLUSION: This predictive model provided a reliable and measurable means of risk stratification of csPCa, with high discriminatory power and great net benefit. It could be a useful tool for clinical decision-making prior to prostate biopsies.

Use of a Polymer Inclusion Membrane and a Chelating Resin for the Flow-Based Sequential Determination of Copper(II) and Zinc(II) in Natural Waters and Soil Leachates.

A bi-parametric sequential injection method for the determination of copper(II) and zinc(II) when present together in aqueous samples was developed. This was achieved by using a non-specific colorimetric reagent (4-(2-pyridylazo)resorcinol, PAR) together with two ion-exchange polymeric materials to discriminate between the two metal ions. A polymer inclusion membrane (PIM) and a chelating resin (Chelex 100) were the chosen materials to retain zinc(II) and copper(II), respectively. The influence of the flow system parameters, such as composition of the reagent solutions, flow rates and standard/sample volume, on the method sensitivity were studied. The interference of several common metal ions was assessed, and no significant interferences were observed (<10% signal deviation). The limits of detection were 3.1 and 5.6 µg L-1 for copper(II) and zinc(II), respectively; the dynamic working range was from 10 to 40 µg L-1 for both analytes. The newly developed sequential injection analysis (SIA) system was applied to natural waters and soil leachates, and the results were in agreement with those obtained with the reference procedure.

PSA-density does not improve bi-parametric prostate MR detection of prostate cancer in a biopsy naïve patient population.

PURPOSE: Bi-parametric prostate MR (bp-MR) is a valuable tool for detection and characterization of prostate cancer (PCa). Recent studies suggested that PSA-density (PSA-D) in combination with multi-parametric prostate MR as well as bp-MR may achieve a higher diagnostic accuracy than either alone. We aimed to evaluate the diagnostic performance of bp-MR, PSA-D and their combination in biopsy-naïve patients. METHODS AND MATERIALS: We retrospectively analyzed 334 consecutive patients who underwent prostate MR on a 3T scanner. Only patients (n = 114) who underwent TRUS-biopsy within 30 days following MR with no previous prostate biopsies were considered. Our protocol included T2-weighted and DWI sequences. A Likert score based on PI-RADS v2 was used for bp-MR evaluation. Lesions were graded histopathologically using the ISUP score. We assessed three scenarios: detection of lesions independently of ISUP score (ISUP ≥ 1), detection of both intermediate and clinically significant lesions (ISUP ≥ 2) and detection of clinically significant lesions alone (ISUP ≥ 3). Predictive value of bp-MR and PSA-D was evaluated by ROC curves and logistic regression analysis. A p value < 0.05 was considered statistically significant. RESULTS: In all evaluated scenarios, bp-MR showed a significantly higher predictive power (AUC = 0.87-0.95) compared to the performance of PSA-D (AUC = 0.73-0.79), while their combination (AUC = 0.91-0.95) showed no statistically significant improvement compared to bp-MR alone. CONCLUSION: Our results confirm that bp-MR is a powerful tool in detection of clinically significant PCa. Contrary to findings in the recent literature, PSA-D does not appear to significantly improve its diagnostic performance.

Development of a robust, fast screening method for the potentiometric determination of iodide in urine and salt samples.

In this work, a potentiometric flow injection method is described for the fast bi-parametric determination of iodide and iodate in urine and salt samples. The developed methodology aimed for iodine speciation with a potentially portable system (running on batteries). The iodate reduction to iodide was effectively attained in line within the same manifold. The iodide determination was accomplished in the dynamic range of 2.50×10-6-1.00×10-3M and the total iodine dynamic range, resulted from iodide plus iodate, was 3.50×10-6-2.00×10-3M. The calculated limits of detection were 1.39×10-6M and 1.77×10-6M for iodide and iodate, respectively. A determination rate of 21h-1 for the bi-parametric iodide and iodate determination was obtained for sample injection. The urine samples (RSD <5.8% for iodide and RSD <7.0% for iodate) results were in agreement with those obtained by the classic Sandell-Kolthoff reaction colorimetric reference procedure (RD <7.0%) and standard samples from Centers for Disease Control and Prevention, Atlanta, USA (CDC) international inter-laboratory EQUIP program. The developed flow method was also successfully applied to the iodide and iodate determination in salt samples (RSD <3.1% for iodate and iodide), with comparable results to conventional procedures. No significant interferences were observed interference percentage <9% for both determinations.

Microsequential injection lab-on-valve system for the spectrophotometric bi-parametric determination of iron and copper in natural waters.

The determination of iron and copper exploiting a microsequential injection lab-on-valve system with online spectrophotometric detection is described. A new, environmental friendly 3-hydroxy-4-pyridinone chelator, functionalized with a polyethylene glycol chain (MRB12) to improve water solubility, was used for iron determination. For copper determination, 1-(2-pyridylazo)-2-naphthol (PAN) was used. Different parameters affecting the formation of the complexes were studied, namely sample volume, reagent concentration, and buffer composition and concentration. The optimized conditions, 150µL of sample volume and 250mgL-1 of MRB12 for iron determination, and 200µL of sample and 120mgL-1 of PAN for copper determination, enabled an LOD of 15 and 18µgL-1 for iron and copper, respectively. The robustness of the developed procedure was assessed by the calculation of the relative standard deviation (RSD), 5% for iron determination and 2% for copper determination. The accuracy of the method was assessed comparing the results with two certified samples (RD<7.5%) and calculating recovery percentages with five river water samples (average<107%).