Identification and validation of a novel anoikis-related prognostic model for prostate cancer.

BACKGROUND: Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis-related prognostic model for prostate cancer (PCa). METHODS: We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis-related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high-risk group patients. RESULTS: Two subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single-nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP-derived and four PRISM-derived compounds were identified for high-risk patients. CONCLUSIONS: The anoikis-related prognostic model developed in this study could be a useful tool for clinical decision-making. This study may provide a new perspective for the treatment of anoikis-related PCa.

Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.

Background: Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements. Purpose: Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV. Materials and methods: The study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model. Results: The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV. Conclusion: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.

Noninvasive Prediction of Sperm Retrieval Using Diffusion Tensor Imaging in Patients with Nonobstructive Azoospermia.

Microdissection testicular sperm extraction (mTESE) is the first-line treatment plan for nonobstructive azoospermia (NOA). However, studies reported that the overall sperm retrieval rate (SRR) was 43% to 63% among men with NOA, implying that nearly half of the patients fail sperm retrieval. This study aimed to evaluate the diagnostic performance of parameters derived from diffusion tensor imaging (DTI) in predicting SRR in patients with NOA. Seventy patients diagnosed with NOA were enrolled and classified into two groups based on the outcome of sperm retrieval during mTESE: success (29 patients) and failure (41 patients). Scrotal magnetic resonance imaging was performed, and the DTI parameters, including mean diffusivity and fractional anisotropy, were analyzed between groups. The results showed that there was a significant difference in mean diffusivity values between the two groups, and the area under the curve for mean diffusivity was calculated as 0.865, with a sensitivity of 72.2% and a specificity of 97.5%. No statistically significant difference was observed in fractional anisotropy values and sex hormone levels between the two groups. This study demonstrated that the mean diffusivity value might serve as a useful noninvasive imaging marker for predicting the SRR of NOA patients undergoing mTESE.

Cuproptosis-related classification and personalized treatment in lower-grade gliomas to prompt precise oncology.

BACKGROUND: Cuproptosis is implicated in regulating tricarboxylic acid cycle and associated with tumor therapeutic sensitivity, patient outcomes and tumorigenesis. However, the classification and prognostic effect of cuproptosis-associated genes (CAGs), the relationship between cuproptosis and tumor microenvironment (TME) and the treatment of lower-grade glioma (LrGG) remain enigmatic. METHODS: The genetic and transcriptional alterations, prognostic value and classification related to cuproptosis were systematically analyzed. Subtypes of cuproptosis and cuproptosis score (Cuscore) were constructed and further confirmed by two external cohorts. The relationships between cuproptosis and TME, prognosis, and treatment response were also evaluated. RESULTS: Four clusters were identified based on cuproptosis-associated genes. The associations between cuproptosis-associated clusters and clinical features, prognosis, immune cell infiltration, and chemotherapy sensitivity were observed. The Cuscore is an independent prognostic indicator in LrGG patients. The nomogram is constructed according to Cuscore and clinical characteristics, and has good predictive ability and calibration. Patients with high Cuscore had a worse prognosis and advanced performance. A higher Cuscore also indicated a higher stromal score, abundant immune infiltration, and increased tumor mutation burden. A high Cuscore was remarkably related to immune checkpoint inhibitors, immunotherapy response and immune phenotype. CONCLUSIONS: This study demonstrates the clinical effect of CAGs, and suggests that cuproptosis could be a potential therapeutic target in LrGG.

A glycosylation signature for predicting the progression and immunotherapeutic response of prostate cancer.

BACKGROUND: Glycosylation has been proposed as a new cancer hallmark. However, focusing on specific glycans or glycoproteins may lose much data relevant to glycosylation alterations. The present study aimed to first comprehensively investigate the expression and mutation profiles of glycosylation-related genes (GRgenes) in prostate cancer (PCa) and then develop a glycosylation signature and explore its role in predicting the progression and immunotherapeutic response of PCa. METHODS: Based on The Cancer Genome Atlas database, we comprehensively screened potential prognostic GRgenes and analyzed their expression and mutation profiles in PCa. Through consensus clustering analysis, the study cohort was classified to investigate the effect of glycosylation patterns on the prognosis of PCa. Next, we developed a glycosylation signature (i.e., the glycosylation score [Gly_score]) using the differentially expressed genes between glycosylation pattern groups and evaluated its role in predicting the progression and immunotherapeutic response of PCa. RESULTS: We identified two distinct glycosylation patterns in PCa and found that GRgene expression patterns rather than mutations are associated with the prognosis of PCa. The high Gly_score group had significantly shorter progression-free survival, lower PD-L1 levels, less infiltration of immune cells and lower immunophenoscores than the low Gly_score group. When the patients were grouped according to both the Gly_score and PD-L1 level, patients with a combination of low Gly_score and low PD-L1 expression had the best survival outcomes. CONCLUSIONS: In the present study, for the first time, we developed a glycosylation signature and demonstrated that the proposed glycosylation signature is a promising tool for predicting the prognosis and immunotherapeutic response of PCa.

Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics.

Objective: Accurate identification of testicular tumors through better lesion characterization can optimize the radical surgical procedures. Here, we compared the performance of different machine learning approaches for discriminating benign testicular lesions from malignant ones, using a radiomics score derived from magnetic resonance imaging (MRI). Methods: One hundred fifteen lesions from 108 patients who underwent MRI between February 2014 and July 2022 were enrolled in this study. Based on regions-of-interest, radiomics features extraction can be realized through PyRadiomics. For measuring feature reproducibility, we considered both intraclass and interclass correlation coefficients. We calculated the correlation between each feature and the predicted target, removing redundant features. In our radiomics-based analysis, we trained classifiers on 70% of the lesions and compared different models, including linear discrimination, gradient boosting, and decision trees. We applied each classification algorithm to the training set using different random seeds, repeating this process 10 times and recording performance. The highest-performing model was then tested on the remaining 30% of the lesions. We used widely accepted metrics, such as the area under the curve (AUC), to evaluate model performance. Results: We acquired 1,781 radiomic features from the T2-weighted maps of each lesion. Subsequently, we constructed classification models using the top 10 most significant features. The 10 machine-learning algorithms we utilized were capable of diagnosing testicular lesions. Of these, the XGBoost classification emerged as the most superior, achieving the highest AUC value of 0.905 (95% confidence interval: 0.886-0.925) on the testing set and outstripping the other models that typically scored AUC values between 0.697-0.898. Conclusion: Preoperative MRI radiomics offers potential for distinguishing between benign and malignant testicular lesions. An ensemble model like the boosting algorithm embodied by XGBoost may outperform other models.

A Graph Convolutional Multiple Instance Learning on a Hypersphere Manifold Approach for Diagnosing Chronic Obstructive Pulmonary Disease in CT Images.

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic disease with high morbidity and mortality. The early diagnosis of COPD is vital for clinical treatment, which helps patients to have a better quality of life. Because COPD can be ascribed to chronic bronchitis and emphysema, lesions in a computed tomography (CT) image can present anywhere inside the lung with different types, shapes and sizes. Multiple instance learning (MIL) is an effective tool for solving COPD discrimination. In this study, a novel graph convolutional MIL with the adaptive additive margin loss (GCMIL-AAMS) approach is proposed to diagnose COPD by CT. Specifically, for those early stage patients, the selected instance-level features can be more discriminative if they were learned by our proposed graph convolution and pooling with self-attention mechanism. The AAMS loss can utilize the information of COPD severity on a hypersphere manifold by adaptively setting the angular margins to improve the performance, as the severity can be quantified as four grades by pulmonary function test. The results show that our proposed GCMIL-AAMS method provides superior discrimination and generalization abilities in COPD discrimination, with areas under a receiver operating characteristic curve (AUCs) of 0.960 ± 0.014 and 0.862 ± 0.010 in the test set and external testing set, respectively, in 5-fold stratified cross validation; moreover, it demonstrates that graph learning is applicable to MIL and suggests that MIL may be adaptable to graph learning.

p53 positively regulates the proliferation of hepatic progenitor cells promoted by laminin-521.

Hepatic progenitor cells (HPCs) hold tremendous potential for liver regeneration, but their well-known limitation of proliferation hampers their broader use. There is evidence that laminin is required for the proliferation of HPCs, but the laminin isoform that plays the dominant role and the key intracellular downstream targets that mediate the regulation of HPC proliferation have yet to be determined. Here we showed that p53 expression increased gradually and reached maximal levels around 8 days when laminin α4, α5, ß2, ß1, and γ1 subunit levels also reached a maximum during HPC activation and expansion. Laminin-521 (LN-521) promoted greater proliferation of HPCs than do laminin, matrigel or other laminin isoforms. Inactivation of p53 by PFT-α or Ad-p53V143A inhibited the promotion of proliferation by LN-521. Further complementary MRI and bioluminescence imaging analysis showed that p53 inactivation decreased the proliferation of transplanted HPCs in vivo. p53 was activated by LN-521 through the Integrin α6ß1/FAK-Src-Paxillin/Akt axis. Activated p53 was involved in the nuclear translocation of CDK4 and inactivation of Rb by inducing p27Kip1. Taken together, this study identifies LN-521 as an ideal candidate substrate for HPC culture and uncovers an unexpected positive role for p53 in regulating proliferation of HPCs, which makes it a potential target for HPC-based regenerative medicine.

InterNet: Detection of Active Abdominal Arterial Bleeding Using Emergency Digital Subtraction Angiography Imaging With Two-Stage Deep Learning.

Objective: Active abdominal arterial bleeding is an emergency medical condition. Herein, we present our use of this two-stage InterNet model for detection of active abdominal arterial bleeding using emergency DSA imaging. Methods: Firstly, 450 patients who underwent abdominal DSA procedures were randomly selected for development of the region localization stage (RLS). Secondly, 160 consecutive patients with active abdominal arterial bleeding were included for development of the bleeding site detection stage (BSDS) and InterNet (cascade network of RLS and BSDS). Another 50 patients that ruled out active abdominal arterial bleeding were used as negative samples to evaluate InterNet performance. We evaluated the mode's efficacy using the precision-recall (PR) curve. The classification performance of a doctor with and without InterNet was evaluated using a receiver operating characteristic (ROC) curve analysis. Results: The AP, precision, and recall of the RLS were 0.99, 0.95, and 0.99 in the validation dataset, respectively. Our InterNet reached a recall of 0.7, the precision for detection of bleeding sites was 53% in the evaluation set. The AUCs of doctors with and without InterNet were 0.803 and 0.759, respectively. In addition, the doctor with InterNet assistant could significantly reduce the elapsed time for the interpretation of each DSA sequence from 84.88 to 43.78 s. Conclusion: Our InterNet system could assist interventional radiologists in identifying bleeding foci quickly and may improve the workflow of the DSA operation to a more real-time procedure.

Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram.

Background: Implementation of deep learning systems (DLSs) for analysis of barium esophagram, a cost-effective diagnostic test for esophageal cancer detection, is expected to reduce the burden to radiologists while ensuring the accuracy of diagnosis. Objective: To develop an automated DLS to detect esophageal cancer on barium esophagram. Methods: This was a retrospective study using deep learning for esophageal cancer detection. A two-stage DLS (including a Selection network and a Classification network) was developed. Five datasets based on barium esophagram were used for stepwise training, validation, and testing of the DLS. Datasets 1 and 2 were used to respectively train and test the Selection network, while Datasets 3, 4, and 5 were respectively used to train, validate, and test the Classification network. Finally, a positioning box with a probability value was outputted by the DLS. A region of interest delineated by experienced radiologists was selected as the ground truth to evaluate the detection and classification efficiency of the DLS. Standard machine learning metrics (accuracy, recall, precision, sensitivity, and specificity) were calculated. A comparison with the conventional visual inspection approach was also conducted. Results: The accuracy, sensitivity, and specificity of our DLS in detecting esophageal cancer were 90.3%, 92.5%, and 88.7%, respectively. With the aid of DLS, the radiologists' interpretation time was significantly shortened (Reader1, 45.7 s vs. 72.2 s without DLS aid; Reader2, 54.1 s vs. 108.7 s without DLS aid). Respective diagnostic efficiencies for Reader1 with and without DLS aid were 96.8% vs. 89.3% for accuracy, 97.5% vs. 87.5% for sensitivity, 96.2% vs. 90.6% for specificity, and 0.969 vs. 0.890 for AUC. Respective diagnostic efficiencies for Reader2 with and without DLS aid were 95.7% vs. 88.2% for accuracy, 92.5% vs. 77.5% for sensitivity, 98.1% vs. 96.2% for specificity, and 0.953 vs. 0.869 for AUC. Of note, the positioning boxes outputted by the DLS almost overlapped with those manually labeled by the radiologists on Dataset 5. Conclusions: The proposed two-stage DLS for detecting esophageal cancer on barium esophagram could effectively shorten the interpretation time with an excellent diagnostic performance. It may well assist radiologists in clinical practice to reduce their burden.

Effective Connectivity in Cortical Networks During Deception: A Lie Detection Study Based on EEG.

Thus far, when deception behaviors occur, the connectivity patterns and the communication between different brain areas remain largely unclear. In this study, the most important information flows (MIIFs) between different brain cortices during deception were explored. First, the guilty knowledge test protocol was employed, and 64 electrodes' electroencephalogram (EEG) signals were recorded from 30 subjects (15 guilty and 15 innocent). Cortical current density waveforms were then estimated on the 24 regions of interest (ROIs). Next, partial directed coherence (PDC), an effective connectivity (EC) analysis was applied in the cortical waveforms to obtain the brain EC networks for four bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). Furthermore, using the graph theoretical analysis, the network parameters with significant differences in the EC network were extracted as features to identify the two groups. The high classification accuracy of the four bands demonstrated that the proposed method was suitable for lie detection. In addition, based on the optimal features in the classification mode, the brain "hub" regions were identified, and the MIIFs were significantly different between the guilty and innocent groups. Moreover, the fronto-parietal network was found to be most prominent among all MIIFs at the four bands. Furthermore, combining the neurophysiology significance of the four frequency bands, the roles of all MIIFs were analyzed, which could help us to uncover the underlying cognitive processes and mechanisms of deception.

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values.

PURPOSE: To develop a machine-learning-based radiomics signature of ADC for discriminating between benign and malignant testicular masses and compare its classification performance with that of minimum and mean ADC. METHODS: A total of ninety-seven patients with 101 histopathologically confirmed testicular masses (70 malignancies, 31 benignities) were evaluated in this retrospective study. Eight hundred fifty-one radiomics features were extracted from the preoperative ADC map of each lesion. The mean and minimum ADC values are part of the radiomics features. Thirty lesions were randomly selected to estimate the reliability of the features. The redundant features were eliminated using univariate analysis (independent t test and Mann-Whitney U test, where appropriate) and Spearman's rank correlation. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection and radiomics signature generation. The classification performance of the radiomics signature and minimum and mean ADC values were evaluated by receiver operating characteristic (ROC) curve analysis and compared by DeLong's test. RESULTS: The whole lesion-based mean ADC showed no difference between benign and malignant testicular masses (P = 0.070, training cohort; P = 0.418, validation cohort). Compared with the minimum ADC, the ADC-based radiomics signature yielded a higher area under the curve (AUC) in both the training (AUC: 0.904, 95% confidence interval [CI]: 0.832-0.975) and validation cohorts (AUC: 0.868, 95% CI: 0.728-1.00). CONCLUSIONS: Conventional mean ADC values are not always helpful in discriminating between testicular benignities and malignancies. The minimum ADC and radiomics signature might be better alternatives, with the radiomics signature performing better than the minimum ADC.

Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Deception Using EEG Phase Synchrony Analysis.

This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG is mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset.

BACKGROUND: The aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge. METHODS: A total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected. The severity of the survivors' dyspnea was determined by follow-up questionnaire. The evolution of the CT findings from the peak period to discharge and throughout follow-up and the abilities of CT findings and clinical parameters to predict survival with and without dyspnea were analyzed. RESULTS: Ninety-one COVID-19 survivors still had dyspnea at follow-up. The age, comorbidity score, duration of hospital stays, receipt of hormone administration, receipt of immunoglobulin injections, intensive care unit (ICU) admission, receipt of mechanical ventilation, laboratory parameters, clinical classifications and parameters associated with lesion volume of the survivors with dyspnea were significantly different from those of survivors without dyspnea. Among the clinical parameters and CT parameters used to identify dyspnea, parameters associated with lesion volume showed the largest area under the curve (AUC) values, with lesion volume at discharge showing the largest AUC (0.820). Lesion volume decreased gradually from the peak period to discharge and through follow-up, with a notable decrease observed after discharge. Absorption of lesions continued 6 months after discharge. CONCLUSIONS: Among the clinical parameters and subjective CT findings, CT findings associated with lesion volume were the best predictors of post-discharge dyspnea in COVID-19 survivors.

Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions.

PURPOSE: To compare the performance of histogram analysis and intra-perinodular textural transition (Ipris) for distinguishing between benign and malignant testicular lesions. PATIENTS AND METHODS: This retrospective study included 76 patients with 80 pathologically confirmed testicular lesions (55 malignant, 25 benign). All patients underwent preoperative T2-weighted imaging (T2WI) on a 3.0T MR scanner. All testicular lesions were manually segmented on axial T2WI, and histogram and Ipris features were extracted. Thirty enrolled patients were randomly selected to estimate the robustness of the features. We used intraclass correlation coefficients (ICCs) to evaluate intra- and interobserver agreement of features, independent t-test or Mann-Whitney U-test to compare features between benign and malignant lesions, and receiver operating characteristic curve analysis to evaluate the diagnostic performance of features. RESULTS: Eighteen histogram features and forty-eight Ipris features were extracted from T2WI of each lesion. Most (60/66) histogram and Ipris features had good robustness (ICC of both intra- and interobserver variabilities >0.6). Three histogram and nine Ipris features were significantly different between the benign and malignant groups. The area under the curve values for Energy, TotalEnergy, and Ipris_shell1_id_std were 0.807, 0.808, and 0.708, respectively, which were relatively higher than those of other features. CONCLUSION: Ipris features may be useful for identifying benign and malignant testicular tumors but have no significant advantage over conventional histogram features.

Refractory lower urinary tract symptoms in patients with lumbar disc hernia relieved by non-surgical treatment.

PURPOSE: Refractory lower urinary tract symptoms (LUTS) coexisting with lumbar disc hernia (LDH) have been shown to resolve following LDH surgery, implying that LDH causes these LUTS. The purpose of this study was to report outcomes in patients with refractory LUTS and LDH following non-surgical treatment targeting LDH. METHODS: A retrospective cohort study was conducted using outpatient data collected at Tongji Hospital, China, between 2016 and 2018. This study included 131 adult patients with refractory LUTS and LDH. Patients were stratified into two groups. Group A underwent non-surgical treatment for LDH plus pharmacological treatment for LUTS. Group B underwent only pharmacological treatment for LUTS. The International Prostate Symptom Score (IPSS), the IPSS quality of life (QoL) score, and uroflowmetry were used to evaluate outcomes. RESULTS: In group A, following treatment, the maximum flow rate (Qmax) increased by 3.92 ml/s (p < 0.001), the IPSS reduced by 5.99 points (p < 0.001), and the QoL score decreased by 1.51 points (p < 0.001). In group B, the Qmax increased by 0.09 ml/s (p = 0.833), the IPSS reduced by 0.72 points (p = 0.163), and the QoL score decreased by 0.07 points (p = 0.784). CONCLUSIONS: LUTS can be relieved by a combination of pharmacological treatment for LUTS and non-surgical treatment for LDH in some refractory LUTS patients with LDH. MRI is recommended for these patients.

Noninvasive Differentiation of Obstructive Azoospermia and Nonobstructive Azoospermia Using Multimodel Diffusion Weighted Imaging.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of parameters derived from multimodel diffusion weighted imaging (monoexponential, stretched-exponential diffusion weighted imaging and diffusion kurtosis imaging [DKI]) from noninvasive magnetic resonance imaging in distinguishing obstructive azoospermia (OA) from nonobstructive azoospermia (NOA). MATERIALS AND METHODS: Forty-six patients with azoospermia were prospectively enrolled and classified into two groups (21 OA patients and 25 NOA patients). The multimodel parameters of diffusion-weighted imaging (DWI; apparent diffusion coefficient [ADC], distributed diffusion coefficient [DDC], diffusion heterogeneity [α], diffusion kurtosis diffusivity [Dapp], and diffusion kurtosis coefficient [Kapp]) were derived. The diagnostic performance of these parameters for the differentiation of OA and NOA patients were evaluated using receiver operating characteristic analysis. The area under the curve (AUC) was calculated to evaluate the diagnostic accuracy of each parameter. RESULTS: All the parameters (ADC, α, DDC, Dapp, and Kapp) values were significantly different between OA and NOA (P < 0.001 for all). For the differentiation of OA from NOA, Kapp showed the highest AUC value (0.965), followed by DDC (0.946), Dapp (0.933), ADC (0.922), and α (0.887). Kapp had a significantly higher AUC than the conventional ADC (P < 0.05). CONCLUSION: Parameters derived from multimodels of DWI have the potential for the noninvasive differentiation of OA and NOA. The Kapp value derived from the DKI model might serve as a useful imaging marker for the differentiation of azoospermia.

Assessment of the Severity of Coronavirus Disease: Quantitative Computed Tomography Parameters versus Semiquantitative Visual Score.

OBJECTIVE: To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19). MATERIALS AND METHODS: We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used to precisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters, semiquantitative visual score, and clinical classification were calculated using Spearman's correlation. A receiver operating characteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods. RESULTS: There were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groups were not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 had larger values for all volume-dependent parameters (p < 0.001). The percentage of lesions had the strongest correlation with disease severity with a correlation coefficient of 0.495. In comparison, the correlation coefficient of semiquantitative score was 0.349. To classify the severity of COVID-19, area under the curve of the percentage of lesions was the highest (0.807; 95% confidence interval, 0.744-0.861: p < 0.001) and that of the quantitative CT parameters was significantly higher than that of the semiquantitative visual score (p = 0.001). CONCLUSION: The classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitative visual score in terms of evaluating the severity of COVID-19.

Discrimination between benign and malignant testicular lesions using volumetric apparent diffusion coefficient histogram analysis.

PURPOSE: This study aimed to evaluate the role of volumetric apparent diffusion coefficient (ADC) histogram analysis in discriminating between benign and malignant testicular masses. METHODS: In this retrospective study, fifty-nine patients with 61 pathologically confirmed testicular masses were consecutively enrolled, including 18 benign lesions and 43 malignant lesions. All patients conducted preoperative magnetic resonance imaging (MRI) with diffusion-weighted imaging. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. Comparisons were conducted by an independent t-test or Mann-Whitney U test, where appropriate. The classification performance of the parameters that showed significant differences between benign and malignant testicular disease were evaluated via receiver operating characteristic (ROC) curve analysis. RESULTS: Among the 18 histogram parameters we extracted, the energy, total energy, and range of ADC of testicular malignancies were all significantly increased compared with those of benignities. The minimum ADC and 10th percentile ADC of testicular malignancies were both significantly reduced compared with those of benignities. The minimum ADC value achieved the highest diagnostic performance in distinguishing between testicular benignities and malignancies, with an area under the ROC curve (AUC) of 0.822, sensitivity of 81.40 %, and specificity of 77.78 %. CONCLUSIONS: Volumetric ADC histogram analysis might be a useful tool to preoperatively discriminate between benign and malignant testicular masses.

Sight and switch off: Nerve density visualization for interventions targeting nerves in prostate cancer.

Nerve density is associated with prostate cancer (PCa) aggressiveness and prognosis. Thus far, no visualization methods have been developed to assess nerve density of PCa in vivo. We compounded propranolol-conjugated superparamagnetic iron oxide nerve peptide nanoparticles (PSN NPs), which achieved the nerve density visualization of PCa with high sensitivity and high specificity, and facilitated assessment of nerve density and aggressiveness of PCa using magnetic resonance imaging and magnetic particle imaging. Moreover, PSN NPs facilitated targeted therapy for PCa. PSN NPs increased the survival rate of mice with orthotopic PCa to 83.3% and decreased nerve densities and proliferation indexes by more than twofold compared with the control groups. The present study, thus, developed a technology to visualize the nerve density of PCa and facilitate targeted neural drug delivery to tumors to efficiently inhibit PCa progression. Our study provides a potential basis for clinical imaging and therapeutic interventions targeting nerves in PCa.