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Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 ( P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 ( P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 ( P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 ( P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

Background: Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency., Methods: This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant., Results: DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUC patient : 0.89 vs. 0.86; AUC lesion : 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (OR rectal susceptibility artifact  = 5.46; OR diameter, = 1.12; OR ADC  = 0.998; all P < .001) and false negatives (OR rectal susceptibility artifact  = 3.31; OR diameter  = 0.82; OR ADC  = 1.007; all P ≤ .03) of DL-CAD., Conclusions: Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD., Trial Registration: ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021., (© 2023. The Author(s).)

Objectives: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI., Methods: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups., Results: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023)., Conclusions: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists., Key Points: • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans., Materials and Methods: We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated., Results: The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001)., Conclusions: Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time., Competing Interests: Conflicts of interest and sources of funding: D.J.W. receives research support from the Swiss Society of Radiology and the Research Fund Junior Researchers of the University Hospital Basel (grant no. 3MS1034)., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI., Methods: This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value ≥ 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test., Results: Clinically significant cancer [CSC, Gleason score ≥ 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060-0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value ≥ 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2-3 and comparable to all others at a PI-RADS cutoff value ≥ 4., Conclusions: The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Authors aimed to assess the correlation between the apparent diffusion coefficient (ADC of the tumor, ADC ratio) and final grade group (GG) after radical prostatectomy (RP), and to determine the threshold values of ADC for detecting clinically significant prostate cancer (PC) with subsequent evaluation in a prospective group. 118 patients with PC were included in the retrospective group. These patients underwent RP from 2012 to 2017 with preoperative 3 Tesla multiparametric MRI (mpMRT) with contrast enhancement in a single center. After analyzing all the MRI studies, the average values of tumor ADC and benign tissue ADC were calculated using the maps of ADC. The prospective part of the study included 60 patients with completed pre-biopsy mpMRI and subsequent RP from January 2018 to March 2019. The prospective part of the study demonstrated the effectiveness of applying the obtained diffusion coefficient thresholds. When used as a criterion for determining clinically significant prostate cancer (GG > 6), threshold value of ADC ratio had sensitivity, specificity, accuracy, positive predictive value and negative predictive value were 84%, 91%, 87%, 94% and 78% respectively.

X-ray contrast properties of the new perfluoroorganic emulsion lipobrom were studied in comparison to those of a traditional clinical Roentgen diagnostic agents omnnipaque and ultravist and the well-known blood substitute perftoran. The x-ray patterns were evaluated by a computer morphodensitometry technique.

A mathematical model is proposed to describe the antigen-antibody complex formation under the immunoassay conditions, which allows the antibody-antigen affinity to be evaluated. In particular, it is established that the dissociation constant of the antigen-antibody complex (for antibodies to a tuberculous Mycobacterium) confidently increases (p > 0.95) in the following sequence of diagnostic kits: omnipak > Ultravist > melitrast > Triombrast. The method suggested for analysis of the influence of xenobiotics upon the antigen-antibody reaction can be used to reveal their possible side effects on the antigen-antibody complex formation in any particular case.

It was shown by the method of fluorescent probes that iodine-containing radiocontrast agents Triombrast > Omnipaque > Ultravist > Melitrast increase the content of intracellular Ca2+ in macrophages of the abdominal cavity of rats. Increase in the entry of Ca2+ from the external environment is the main factor of the change in the intracellular concentration of Ca2+ ions in the macrophages in interaction with radiocontrast agents.

The glycoprotein (15-18 kDa) antigen of Mycobacterium tuberculosis H37Rv was affinity isolated on the immunosorbent with monoclonal antibodies S4C1G4 (specific to M. tuberculosis H37Rv; Avdiyenko V. G., Kondrashov S.Yu, Lyashenko S.M.@Probl. Tuberk. 1996, v. 1, p. 6-8 (in Russian). This antigen and PPD (Batch RT 45, Stattens Seruminstitute, Denmark) that was a standard antigen were used for enzyme-linked immunosorbent assay (ELISA), by detecting serum IgG antibodies in patients with pulmonary tuberculosis, and control groups of patients with lung diseases other than tuberculosis (bronchitis and/or asthma, pneumonia) as well as healthy volunteers. The diagnostic parameters of specificity and sensitivity for titers and the same parameters for optic density (OD) (in serum dilution with maximum differences for groups of patients and donors) were compared. The new monoantigen method provided 86.11% specificity and 87.87% sensitivity, which were higher those obtained for optic density (63.89 and 80%, respectively). With PPD, the specificity and sensitivity were 58.04 and 78.78 (for the new titer method) versus 50 and 78.78% (OD data). The method error for titer determination was 10% and for standard OD determination was 27%. The new approach offers additional possibilities of enhancing the quality of ELISA for diagnosis of tuberculosis.

The mechanisms binding X-ray contrast media and magneto-resonance contrast media of various structure with human serum albumin and gamma-globulins were studied. Equilibrium dialysis showed that X-ray contrast media bind with blood plasma proteins, but magneto-resonance contrast media do not interact with these proteins. Electrostatic forces play a significant role in formation of complexes of X-ray contrast media with human blood serum albumin. With increase in the size of the molecule of the X-ray contrast media their affinity for the binding sites of this protein diminishes. The formation of complexes of X-ray contrast media with human blood plasma gamma-globulins occurs through hydrophobic interactions. Increase in the size of the molecules of these media reduces their affinity for the gamma-globulin binding sites.

Molecular mechanism of the interaction between human serum albumin and cloramphenicol was studied by 1H and 13C NMR-spectroscopy. It was found that the main role belongs to [formula: see text] groups of chloramphenicol. A schematic model of the complex-formation between serum albumin and chloramphenicol was proposed.