Executive function performance in children and adolescent patients with narcolepsy type 1.

OBJECTIVE: The executive function profile in patients with narcolepsy type 1 (NT1) has been mentioned; however, limited research exists on children and adolescent patients with NT1.This study aims to assess executive function in children and adolescent patients with NT1 in China, examine potential influencing factors and evaluate the short-term treatment effect on executive function. METHODS: 53 NT1 patients (36 males, age 12.2 ± 3.4 years) and 37 healthy controls (23 males, age 12.2 ± 2.5 years) underwent self-reported measures assessing subjective sleepiness, depression, anxiety and sleep quality. A comprehensive neuropsychological test was administered to assess executive function domains, including processing speed, inhibitory control, cognitive flexibility and working memory. These assessments were repeated in NT1 patients after three-day regular drug treatment. RESULTS: NT1 patients exhibited higher levels of excessive daytime sleepiness, depression, anxiety, and poor sleep quality compared to healthy controls. Patients showed impaired processing speed, inhibitory control and cognitive flexibility (p < 0.05), whereas working memory was unaffected (p > 0.05). Regression analysis revealed that parameters from sleep monitoring, such as sleep efficiency and sleep latency, were correlated with executive function performance after controlling for age, gender, and education years. The short-term treatment led to improvements in inhibitory control, cognitive flexibility, and working memory. CONCLUSION: The findings showed that executive function was impaired among children and adolescent patients with NT1, which was associated with objective sleep parameters. Furthermore, this study emphasizes the necessity of neuropsychological assessments and early interventions among children and adolescent NT1 patients.

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.

A molecular classification system for estimating radiotherapy response and anticancer immunity for individual breast cancer patients.

Objective: Radiotherapy is a cornerstone of breast cancer therapy, but radiotherapy resistance is a major clinical challenge. Herein, we show a molecular classification approach for estimating individual responses to radiotherapy. Methods: Consensus clustering was adopted to classify radiotherapy-sensitive and -resistant clusters in the TCGA-BRCA cohort based upon prognostic differentially expressed radiotherapy response-related genes (DERRGs). The stability of the classification was proven in the GSE58812 cohort via NTP method and the reliability was further verified by quantitative RT-PCR analyses of DERRGs. A Riskscore system was generated through Least absolute shrinkage and selection operator (LASSO) analysis, and verified in the GSE58812 and GSE17705. Treatment response and anticancer immunity were evaluated via multiple well-established computational approaches. Results: We classified breast cancer patients as radiotherapy-sensitive and -resistant clusters, namely C1 and C2, also verified by quantitative RT-PCR analyses of DERRGs. Two clusters presented heterogeneous clinical traits, with poorer prognosis, older age, more advanced T, and more dead status in the C2. The C1 tumors had higher activity of reactive oxygen species and response to X-ray, proving better radiotherapeutic response. Stronger anticancer immunity was found in the C1 tumors that had rich immune cell infiltration, similar expression profiling to patients who responded to anti-PD-1, and activated immunogenic cell death and ferroptosis. The Riskscore was proposed for improving patient prognosis. High Riskscore samples had lower radiotherapeutic response and stronger DNA damage repair as well as poor anticancer immunity, while low Riskscore samples were more sensitive to docetaxel, doxorubicin, and paclitaxel. Conclusion: Our findings propose a novel radiotherapy response classification system based upon molecular profiles for estimating radiosensitivity for individual breast cancer patients, and elucidate a methodological advancement for synergy of radiotherapy with ICB.

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.

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.

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.

Comparing the effects of dexmedetomidine versus propofol on the treatment of emergence agitation in adult patients after general anesthesia: study protocol for a randomized, superiority, controlled trial (DP-TEA Trial).

BACKGROUND: Emergence agitation (EA) after general anesthesia is a common complication in the post-anesthesia care unit (PACU). Once EA occurs, there are still no guidelines established for the treatment in adults. Propofol is excessively used in managing agitated patients in the PACU, but it lacks analgesia and can result in apnea. Intraoperative infusion of dexmedetomidine has been proven to have a preventive effect on EA, but the treatment effect of dexmedetomidine on EA remains unknown. This study aims to compare the effects between dexmedetomidine and propofol on relieving EA in adult patients after general anesthesia in the PACU. METHODS: In this randomized, superiority, controlled clinical study, a total of 120 adult patients aged 18-65 years of both genders, with American Society of Anesthesiologists (ASA) classification I or II developing EA in the PACU after general anesthesia, will be enrolled. Patients will be randomized at a 1:1 ratio into two groups, receiving either a single dose of dexmedetomidine (0.7µg/kg) or propofol (0.5 mg/kg). The primary outcome is the proportion of patients having a recurrent EA within 15 min after intervention in the PACU. DISCUSSION: Previous studies have focused on premedication for preventing EA, while therapeutics for reliving EA have rarely been reported. To our knowledge, this study is the first randomized, superiority, controlled trial to compare a bolus of dexmedetomidine with the current routine care for this indication. TRIAL REGISTRATION: ClinicalTrials.gov NCT04142840 . Registered on October 26, 2019.

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.

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.

T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma.

Objective: To evaluate the performance of a T2-weighted image (T2WI)-based radiomics signature for differentiating between seminomas and nonseminomas. Materials and Methods: In this retrospective study, 39 patients with testicular germ-cell tumors (TGCTs) confirmed by radical orchiectomy were enrolled, including 19 cases of seminomas and 20 cases of nonseminomas. All patients underwent 3T magnetic resonance imaging (MRI) before radical orchiectomy. Eight hundred fifty-one radiomics features were extracted from the T2WI of each patient. Intra- and interclass correlation coefficients were used to select the features with excellent stability and repeatability. Then, we used the minimum-redundancy maximum-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms for feature selection and radiomics signature development. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of the radiomics signature. Results: Five features were selected to build the radiomics signature. The radiomics signature was significantly different between the seminomas and nonseminomas (p < 0.01). The area under the curve (AUC), sensitivity, and specificity of the radiomics signature for discriminating between seminomas and nonseminomas were 0.979 (95% CI: 0.873-1.000), 90.00 (95% CI: 68.3-98.8), and 100.00 (95% CI: 82.4-100.0), respectively. Conclusion: The T2WI-based radiomics signature has the potential to non-invasively discriminate between seminomas and nonseminomas.

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

PURPOSE: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa). MATERIALS AND METHODS: Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts. RESULTS: Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823-0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669-0.976), 0.841, and 0.727, respectively, in the test cohort. CONCLUSION: Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.

Abbreviated Biparametric Versus Standard Multiparametric MRI for Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis.

OBJECTIVE: The objective of our study was to evaluate the diagnostic accuracy of abbreviated biparametric MRI (bpMRI) versus standard multiparametric MRI (mpMRI) for prostate cancer (PCa) using guided biopsy or prostatectomy histopathology results as the reference standard. MATERIALS AND METHODS: A comprehensive literature search of PubMed, Web of Science, and Cochrane Library databases was performed by two researchers independently and the relevant references were assessed. Original research studies comparing bpMRI with mpMRI in diagnosing PCa were included. The methodologic quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data necessary to complete 2 × 2 contingency tables were obtained to calculate the diagnostic performance of bpMRI and mpMRI using Stata (version 14). RESULTS: Ten studies were included, and a total of 1705 patients and 3419 lesions were analyzed. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) of mpMRI in diagnosing PCa were 0.79 (95% CI, 0.69-0.87), 0.89 (95% CI, 0.70-0.96), 6.9 (95% CI, 2.5-18.8), 0.24 (95% CI, 0.16-0.35), and 29 (95% CI, 10-83). Sensitivity, specificity, positive LR, negative LR, and DOR of bpMRI in diagnosing PCa were 0.79 (95% CI, 0.69-0.87), 0.88 (95% CI, 0.73-0.95), 6.4 (95% CI, 2.9-14.5), 0.24 (95% CI, 0.16-0.35), and 27 (95% CI, 11-67). Meta-analysis showed no statistically significant difference between bpMRI and mpMRI for the diagnosis of PCa, and the areas under the summary ROC (SROC) curves were 0.89 and 0.88, respectively (p = 0.9944). Results of the sensitivity analysis were consistent, and the area under the SROC curve for bpMRI and mpMRI was 0.89 for both (p = 0.9349). CONCLUSION: The available evidence indicates that bpMRI and mpMRI have similar diagnostic efficacy in diagnosing PCa.

Diagnostic Performance and Interobserver Consistency of the Prostate Imaging Reporting and Data System Version 2: A Study on Six Prostate Radiologists with Different Experiences from Half a Year to 17 Years.

BACKGROUND: One of the main aims of the updated Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) is to diminish variation in the interpretation and reporting of prostate imaging, especially among readers with varied experience levels. This study aimed to retrospectively analyze diagnostic consistency and accuracy for prostate disease among six radiologists with different experience levels from a single center and to evaluate the diagnostic performance of PI-RADS v2 scores in the detection of clinically significant prostate cancer (PCa). METHODS: From December 2014 to March 2016, 84 PCa patients and 99 benign prostatic shyperplasia patients who underwent 3.0T multiparametric magnetic resonance imaging before biopsy were included in our study. All patients received evaluation according to the PI-RADS v2 scale (1-5 scores) from six blinded readers (with 6 months and 2, 3, 4, 5, or 17 years of experience, respectively, the last reader was a reviewer/contributor for the PI-RADS v2). The correlation among the readers' scores and the Gleason score (GS) was determined with the Kendall test. Intra-/inter-observer agreement was evaluated using κ statistics, while receiver operating characteristic curve and area under the curve analyses were performed to evaluate the diagnostic performance of the scores. RESULTS: Based on the PI-RADS v2, the median κ score and standard error among all possible pairs of readers were 0.506 and 0.043, respectively; the average correlation between the six readers' scores and the GS was positive, exhibiting weak-to-moderate strength (r = 0.391, P = 0.006). The AUC values of the six radiologists were 0.883, 0.924, 0.927, 0.932, 0.929, and 0.947, respectively. CONCLUSION: The inter-reader agreement for the PI-RADS v2 among the six readers with different experience is weak to moderate. Different experience levels affect the interpretation of MRI images.

Effects of Echo Time on IVIM Quantification of the Normal Prostate.

The two-compartment intravoxel incoherent motion (IVIM) theory assumes that the transverse relaxation time is the same in both compartments. However, blood and tissue have different T2 values, and echo time (TE) may thus have an effect on the quantitative parameters of IVIM. The purpose of this study was to investigate the effects of TE on IVIM-DWI-derived parameters of the prostate. In total, 17 healthy volunteers underwent two repeat examinations. IVIM-DWI data were scanned 6 times with variable TE values of 60, 70, 80, 90, 100, and 120 ms. The ADC of a mono-exponential model and the D, D*, and f parameters of the IVIM model were calculated separately for each TE. Repeat measures were assessed by calculating the coefficient of variation and Bland-Altman limits of agreement for each parameter. Spearman's rho test was used to analyse relationships between IVIM indices and TE. Our results showed that TE had an effect on IVIM quantification, which should be kept constant in the examination protocol at each individual institution. Alternatively, an extended IVIM could be used to eliminate the effect of the TE value on the quantitative parameters of IVIM. This may be helpful for guiding clinical research, especially for longitudinal studies.