Causal relationship between lipid-lowering drugs and ovarian cancer, cervical cancer: a drug target mendelian randomization study.

BACKGROUND: The causal impact of lipid-lowering drugs on ovarian cancer (OC) and cervical cancer (CC) has received considerable attention, but its causal relationship is still a subject of debate. Hence, the objective of this study is to evaluate the impact of lipid-lowering medications on the occurrence risk of OC and CC through Mendelian randomization (MR) analysis of drug targets. METHODS: This investigation concentrated on the primary targets of lipid-lowering medications, specifically, 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) and proprotein convertase kexin 9 (PCSK9). Genetic variations associated with HMGCR and PCSK9 were derived from published genome-wide association study (GWAS) findings to serve as substitutes for HMGCR and PCSK9 inhibitors. Employing a MR approach, an analysis was conducted to scrutinize the impact of inhibitors targeting HMGCR and PCSK9 on the occurrence of OC and CC. Coronary heart disease (CHD) risk was utilized as a positive control, and the primary outcomes encompassed OC and CC. RESULTS: The findings of the study suggest a notable elevation in the risk of OC among patients treated with HMGCR inhibitors (OR [95%CI] = 1.815 [1.316, 2.315], p = 0.019). In contrast, no significant correlation was observed between PCSK9 inhibitors and the occurrence of OC. Additionally, the analysis did not reveal any noteworthy connection between HMGCR inhibitors, PCSK9 inhibitors, and CC. CONCLUSION: HMGCR inhibitors significantly elevate the risk of OC in patients, but their mechanism needs further investigation, and no influence of PCSK9 inhibitors on OC has been observed. There is no significant relationship between HMGCR inhibitors, PCSK9 inhibitors, and CC.

Reference Ranges of Ventricular Morphology and Function in Healthy Chinese Adults: A Multicenter 3 T MRI Study.

BACKGROUND: Magnetic resonance imaging (MRI) reference ranges for ventricular morphology and function in the Chinese population are lacking. PURPOSE: To establish the MRI reference ranges of left and right ventricular (LV and RV) morphology and function based on a large multicenter cohort. STUDY TYPE: Prospective. POPULATION: One thousand and twelve healthy Chinese Han adults. FIELD STRENGTH/SEQUENCE: Balanced steady-state free procession cine sequence at 3.0 T. ASSESSMENT: Biventricular end-diastolic, end-systolic, stroke volume, and ejection fraction (EDV, ESV, SV, and EF), LV mass (LVM), end-diastolic and end-systolic dimension (LVEDD and LVESD), anteroseptal wall thickness (AS), and posterolateral wall thickness (PL) were measured. Body surface area (BSA) and height were used to index biventricular parameters. Parameters were compared between age groups and sex. STATISTICAL TESTS: Independent-samples t-tests or Mann-Whitney U test to compare mean values between sexes; ANOVA or Kruskal-Wallis test to compare mean values among age groups; linear regression to assess the relationships between cardiac parameters and age (correlation coefficient, r). A P value <0.05 was considered statistically significant. RESULTS: The biventricular volumes, LVM, LVEDD, RVEDV/LVEDV ratio, LVESD, AS, and PL were significantly greater in males than in females, even after indexing to BSA or height, while LVEF and RVEF were significantly lower in males than in females. For both sexes, age was significantly negatively correlated with biventricular volumes (male and female: LVEDV [r = -0.491; r = -0.373], LVESV [r = -0.194; r = -0.184], RVEDV [r = -0.639; r = -0.506], RVESV [r = -0.270; r = -0.223]), with similar correlations after BSA normalization. LVEF (r = 0.043) and RVEF (r = 0.033) showed a significant correlation with age in females, but not in males (P = 0.889; P = 0.282). DATA CONCLUSION: MRI reference ranges for biventricular morphology and function in Chinese adults are presented and show significant associations with age and sex. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.

BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE: To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE: Retrospective. POPULATION: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE: 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT: DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

A new predictor of coronary artery disease in acute ischemic stroke or transient ischemic attack patients: pericarotid fat density.

OBJECTIVES: The study aims to evaluate the incremental predictive value of pericarotid fat density (PFD) on head and neck computed tomography angiography (CTA) for the obstructive coronary artery disease (CAD) (≥ 50% stenosis) relative to a clinical risk model (Framingham risk score (FRS)) and the degree of carotid artery stenosis and plaque type in acute ischemic stroke (AIS) or transient ischemic attack (TIA) patients without a known history of CAD. METHODS: In a cohort of 134 consecutive stable patients diagnosed with AIS or TIA undergoing head and neck CTA between January 2010 and December 2021, pericarotid adipose tissue density (PFD) was quantified using a dedicated software. We collected demographic and clinical data, assessed the risk of CAD using the FRS, and analyzed coronary and carotid artery CTA images. Univariate and multivariate logistic regression analyses were performed to assess associations between FRS, PFD, CTA variables, and obstructive CAD risk. Four prediction models were established to evaluate the incremental predictive value of PFD relative to FRS, stenosis degree, and plaque types. Receiver operating characteristic (ROC) curves were generated, and the areas under the curves (AUC) were compared. RESULTS: Increasing FRS, stenosis degree, and PFD values were positively correlated with obstructive CAD (all p < 0.05). In the predictive models for obstructive CAD, the model incorporating carotid stenosis exhibited superior predictive performance compared to FRS alone (p < 0.05). Moreover, the predictive model integrating PFD demonstrated enhanced performance and yielded the highest AUC of the receiver operator characteristic curve (AUC = 0.783), with sensitivity and specificity values of 86.89% and 65.75%, respectively. CONCLUSION: CTA-derived PFD measurements offer supplementary predictive value for obstructive CAD beyond FRS and stenosis, thereby facilitating improved risk stratification of TIA or stroke patients without a history of CAD history. CLINICAL RELEVANCE STATEMENT: CTA-derived PFD provides incremental predictive value for obstructive coronary artery disease in acute ischemic stroke or transient ischemic attack patients without CAD history, beyond Framingham risk score and carotid artery stenosis degree, improving risk stratification. KEY POINTS: • Pericarotid fat density is associated with obstructive coronary artery disease in acute ischemic stroke or transient ischemic attack patients. • Higher pericarotid fat density corresponds to an increased risk of obstructive coronary artery disease. • Estimation of pericarotid fat density using computed tomography angiography imparts additional predictive value for obstructive CAD in risk stratification of acute ischemic stroke or transient ischemic attack patients.

Diffusional kurtosis imaging in differentiating nonarteritic anterior ischemic optic neuropathy from acute optic neuritis.

PURPOSE: We aimed to determine the feasibility of using DKI to characterize pathological changes in nonarteritic anterior ischemic optic neuropathy (NAION) and to differentiate it from acute optic neuritis (ON). METHODS: Orbital DKI was performed with a 3.0 T scanner on 75 patients (51 with NAION and 24 with acute ON) and 15 healthy controls. NAION patients were further divided into early and late groups. The mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were calculated to perform quantitative analyses among groups; and receiver operating characteristic curve analyses were also performed to determine their effectiveness of differential diagnosis. In addition, correlation coefficients were calculated to explore the correlations of the DKI-derived data with duration of disease. RESULTS: The MK, RK, and AK in the affected nerves with NAION were significantly higher than those in the controls, while the trend of FA, RD, and AD was a decline; in acute ON patients, except for RD, which increased, all DKI-derived kurtosis and diffusion parameters were significantly lower than controls (all P < 0.008). Only AK and MD had statistical differences between the early and late groups. Except for MD (early group) and FA, all other DKI-derived parameters were higher in NAION than in acute ON; and parameters in the early group showed better diagnostic efficacy in differentiating NAION from acute ON. Correlation analysis showed that time was negatively correlated with MK, RK, AK, and FA and positively correlated with MD, RD, and AD (all P < 0.05). CONCLUSION: DKI is helpful for assessing the specific pathologic abnormalities resulting from ischemia in NAION by comparison with acute ON. Early DKI should be performed to aid in the diagnosis and evaluation of NAION.

Association between severity of nonalcoholic fatty liver disease and major adverse cardiovascular events in patients assessed by coronary computed tomography angiography.

BACKGROUND: The effect of nonalcoholic fatty liver disease (NAFLD) on major adverse cardiovascular events (MACEs) can be influenced by the degree of coronary artery stenosis. However, the association between the severity of NAFLD and MACEs in patients who underwent coronary computed tomography angiography (CCTA) is unclear. METHODS: A total of 341 NAFLD patients who underwent CCTA were enrolled. The severity of NAFLD was divided into mild NAFLD and moderate-severe NAFLD by abdominal CT results. The degree of coronary artery stenosis was evaluated by using Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Cox regression analysis and Kaplan-Meier analysis were used to assess poor prognosis. RESULTS: During the follow-up period, 45 of 341 NAFLD patients (13.20%) who underwent CCTA occurred MACEs. The severity of NAFLD (hazard ratio [HR] = 2.95[1.54-5.66]; p = 0.001) and CAD-RADS categories 3-5 (HR = 16.31[6.34-41.92]; p < 0.001) were independent risk factors for MACEs. The Kaplan-Meier analysis showed that moderate to severe NAFLD patients had a worsen prognosis than mild NAFLD patients (log-rank p < 0.001). Moreover, the combined receiver operating characteristic curve of the severity of NAFLD and CAD-RADS category showed a good predicting performance for the risk of MACEs, with an area under the curve of 0.849 (95% CI = 0.786-0.911). CONCLUSION: The severity of NAFLD was independent risk factor for MACEs in patients with obstructive CAD, having CAD-RADS 3-5 categories on CCTA.

The parcellation of cingulate cortex in neonatal period based on resting-state functional MRI.

The human cingulate cortex (CC) is a complex region that is characterized by heterogeneous cytoarchitecture, connectivity, and function, and it is associated with various cognitive functions. The adult CC has been divided into various subregions, and this subdivision is highly consistent with its functional differentiation. However, only a few studies have focused on the function of neonatal CC. The aim of this study was to describe the cingulate segregation and the functional connectivity of each subdivision in full-term neonates (n = 60) based on resting-state functional magnetic resonance imaging. The neonatal CC was divided into three subregions, and each subregion showed specific connectivity patterns. The anterior cingulate cortex was mainly correlated with brain regions related to the salience (affected) network and default mode network (DMN), the midcingulate cortex was related to motor areas, and the posterior cingulate cortex was coupled with DMN. Moreover, we found that the cingulate subregions showed distinct functional profiles with major brain networks, which were defined using independent component analysis, and exhibited functional lateralization. This study provided new insights into the understanding of the functional specialization of neonatal CC, and these findings may have significant clinical implications, especially in predicting neurological disorder.

Development of a multi-phase CT-based radiomics model to differentiate heterotopic pancreas from gastrointestinal stromal tumor.

BACKGROUND: To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the model's performance. METHODS: Multi-phase CT radiological data were retrospectively collected from 94 patients. Of these, 40 with HP and 54 with GISTs were enrolled between April 2017 and November 2021. One experienced radiologist manually delineated the volume of interest and then resampled the voxel size of the images to 0.5 × 0.5 × 0.5 mm3, 1 × 1 × 1 mm3, and 2 × 2 × 2 mm3, respectively. Radiomics features were extracted using PyRadiomics, resulting in 1218 features from each phase image. The datasets were randomly divided into training set (n = 66) and validation set (n = 28) at a 7:3 ratio. After applying multiple feature selection methods, the optimal features were screened. Radial basis kernel function-based support vector machine (RBF-SVM) was used as the classifier, and model performance was evaluated using the area under the receiver operating curve (AUC) analysis, as well as accuracy, sensitivity, and specificity. RESULTS: The combined phase model performed better than the other phase models, and the resampling method of 0.5 × 0.5 × 0.5 mm3 achieved the highest performance with an AUC of 0.953 (0.881-1), accuracy of 0.929, sensitivity of 0.938, and specificity of 0.917 in the validation set. The Delong test showed no significant difference in AUCs among the three resampling methods, with p > 0.05. CONCLUSIONS: Radiomics can effectively differentiate between HP and GISTs on CT images, and the diagnostic performance of radiomics is minimally affected by different resampling methods.

Brain magnetic resonance imaging findings in children with neurological complications of coronavirus disease 2019 (Omicron variant): a multicenter retrospective observational study.

BACKGROUND: An increasing rate of encephalopathy associated with coronavirus disease 2019 (COVID-19) has been observed among children. However, the literature on neuroimaging data in children with COVID-19 is limited. OBJECTIVE: To analyze brain magnetic resonance imaging (MRI) of pediatric COVID-19 patients with neurological complications. MATERIALS AND METHODS: This multicenter retrospective observational study analyzed clinical (n=102, 100%) and neuroimaging (n=93, 91.2%) data of 102 children with COVID-19 infections and comorbid acute neurological symptoms. These children were hospitalized at five pediatric intensive care units (PICUs) in China between December 1, 2022, and January 31, 2023. RESULTS: All patients were positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as detected via reverse transcriptase polymerase chain reaction. About 75.7% of the children were infected with the Omicron variant BF.7 strain. Brain MRI was performed 1-12 days following the onset of neurological symptoms, which revealed acute neuroimaging findings in 74.2% (69/93) of cases, including evidence of acute necrotizing encephalopathy (33/69, 47.8%), encephalitis (31/69, 44.9%), reversible splenial lesion syndrome (3/69, 4.3%), reversible posterior leukoencephalopathy (1/69, 1.4%), and hippocampal atrophy (1/69, 1.4%). CONCLUSIONS: Overall, these data highlighted five neuroimaging patterns associated with the outbreak of the SARS-CoV-2 Omicron variant, with acute necrotizing encephalopathy being the most common of these neuroimaging findings. Rarely, the brain MRI of these pediatric COVID-19 patients also demonstrate hippocampal atrophy.

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

BACKGROUND: To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). METHODS: This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. RESULTS: PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction. CONCLUSIONS: PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.

Deep Learning for Head and Neck CT Angiography: Stenosis and Plaque Classification.

Background Studies have rarely investigated stenosis detection from head and neck CT angiography scans because accurate interpretation is time consuming and labor intensive. Purpose To develop an automated convolutional neural network-based method for accurate stenosis detection and plaque classification in head and neck CT angiography images and compare its performance with that of radiologists. Materials and Methods A deep learning (DL) algorithm was constructed and trained with use of head and neck CT angiography images that were collected retrospectively from four tertiary hospitals between March 2020 and July 2021. CT scans were partitioned into training, validation, and independent test sets at a ratio of 7:2:1. An independent test set of CT angiography scans was collected prospectively between October 2021 and December 2021 in one of the four tertiary centers. Stenosis grade categories were as follows: mild stenosis (<50%), moderate stenosis (50%-69%), severe stenosis (70%-99%), and occlusion (100%). The stenosis diagnosis and plaque classification of the algorithm were compared with the ground truth of consensus by two radiologists (with more than 10 years of experience). The performance of the models was analyzed in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve. Results There were 3266 patients (mean age ± SD, 62 years ± 12; 2096 men) evaluated. The consistency between radiologists and the DL-assisted algorithm on plaque classification was 85.6% (320 of 374 cases [95% CI: 83.2, 88.6]) on a per-vessel basis. Moreover, the artificial intelligence model assisted in visual assessment, such as increasing confidence in the degree of stenosis. This reduced the time needed for diagnosis and report writing of radiologists from 28.8 minutes ± 5.6 to 12.4 minutes ± 2.0 (P < .001). Conclusion A deep learning algorithm for head and neck CT angiography interpretation accurately determined vessel stenosis and plaque classification and had equivalent diagnostic performance when compared with experienced radiologists. © RSNA, 2023 Supplemental material is available for this article.

Quantum Uncertainty Principles for Measurements with Interventions.

Heisenberg's uncertainty principle implies fundamental constraints on what properties of a quantum system we can simultaneously learn. However, it typically assumes that we probe these properties via measurements at a single point in time. In contrast, inferring causal dependencies in complex processes often requires interactive experimentation-multiple rounds of interventions where we adaptively probe the process with different inputs to observe how they affect outputs. Here, we demonstrate universal uncertainty principles for general interactive measurements involving arbitrary rounds of interventions. As a case study, we show that they imply an uncertainty trade-off between measurements compatible with different causal dependencies.

The feasibility of quantitative assessment of dynamic 18F-fluorodeoxyglucose PET in Takayasu's arteritis: a pilot study.

PURPOSE: PET has been demonstrated to be sensitive for detecting active inflammation in Takayasu's arteritis (TAK) patients, but semi-quantitative-based assessment may be susceptible to various biological and technical factors. Absolute quantification via dynamic PET (dPET) may provide a more reliable and quantitative assessment of TAK-active arteries. The purpose of this study was to investigate the feasibility and efficacy of dPET in quantifying TAK-active arteries compared to static PET. MATERIALS AND METHODS: This prospective study enrolled 10 TAK-active patients (fulfilled the NIH criteria) and 5 control participants from March to October 2022. One-hour dPET scan (all TAK and control participants) and delayed static PET scan at 2-h (all TAK patients) were acquired. For 1-h static PET, summed images from 50 to 60 min of the dPET were extracted. PET parameters derived from 1- and 2-h static PET including SUV (SUV1H and SUV2H), target-to-background ratio (TBR) (TBR1H and TBR2H), net influx rate (Ki), and TBRKi extracted from dPET were obtained. The detectability of TAK-active arteries was compared among different scanning methods using the generalized estimating equation (GEE) with a logistic regression with repeated measures, and the GEE with gamma distribution and log link function was used to evaluate the different study groups or scanning methods. RESULTS: Based on the disease states, 5 cases of TAK were classified as untreated and relapsed, respectively. The SUVmax on 2-h PET was higher than that on 1-h PET in the untreated patients (P < 0.05). However, no significant differences were observed in the median SUVmax between 1-h PET and 2-h PET in the relapsed patients (P > 0.05). The TBRKi was significantly higher than both TBR1H and TBR2H (all P < 0.001). Moreover, the detectability of TAK-active arteries by dPET-derived Ki was significantly higher than 1-h and 2-h PET (all P < 0.001). Significant differences were observed in Kimax, SUVmax-1H, TBR1H, and TBRKi among untreated, relapsed, and control groups (all P < 0.05). CONCLUSIONS: Absolute quantitative assessment by dPET provides an improved sensitivity and detectability in both visualization and quantification of TAK-active arteries. This elucidates the clinical significance of dPET in the early detection of active inflammation and monitoring recurrence.

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). METHODS: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. RESULTS: In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). CONCLUSION: Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.

A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score.

PURPOSE: This study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa). METHODS: The demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed. RESULTS: The AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model. CONCLUSION: P.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.

Preoperative Diagnosis of Dual-Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models.

BACKGROUND: Dual-phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). PURPOSE: To develop and validate clinical and radiomics models based on contrast-enhanced MRI for the preoperative diagnosis of DPHCC. STUDY TYPE: Retrospective. POPULATION: A total of 87 patients with DPHCC and 92 patients with non-DPHCC randomly divided into a training cohort (n = 125: 64 non-DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non-DPHCC; 26 DPHCC). FIELD STRENGTH/SEQUENCE: A 3.0 T; dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging sequence. ASSESSMENT: In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]) and their discriminatory efficacy assessed and compared. STATISTICAL TESTS: The independent sample t test, Mann-Whitney U test, Chi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant. RESULTS: In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831-0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779-0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526-0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543). CONCLUSION: MRI radiomics models may be useful for discriminating DPHCC from non-DPHCC before surgery. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Assessment of Cerebral White Matter Involvement in Amyotrophic Lateral Sclerosis Patients With Disease Progression and Cognitive Impairment by Fixel-Based Analysis and Neurite Orientation Dispersion and Density Imaging.

BACKGROUND: Previous studies using emerging diffusion MRI techniques have revealed damage to the white matter (WM) microstructure in amyotrophic lateral sclerosis (ALS), particularly the influence of crossed fibers, but there is a lack of subgroup analyses. PURPOSE: To detect WM microstructural changes in ALS patients using fixel-based analysis (FBA) and neurite orientation dispersion and density imaging (NODDI) MRI. STUDY TYPE: Prospective. POPULATION: Thirty-six ALS patients (aged 60.50 ± 9.5 years) and 25 healthy controls (HCs) (aged 58.90 ± 8.1 years). FIELD STRENGTH/SEQUENCE: 3 T; NODDI and FBA (b-values = 0, 1000, and 2500 seconds/mm2 ). ASSESSMENT: Subgroups were performed according to progression rate and cognition, including fast and slow progression (FP/SP), ALS with and without cognitive impairment (ALS-ci/ALS-nci). Fiber density (FD), fiber-bundle cross-section (FC), combined fiber density and cross-section (FDC), neurite density index (NDI), orientation dispersion index (ODI), isotropic volume fraction (ISO), and fractional anisotropy (FA) were calculated and their correlation with clinical variables examined. STATISTICAL TESTING: Chi-square test, Mann-Whitney U test, two-sample t test, partial correlation analysis, and false discovery rate (FDR) corrected. A P-value <0.05 was considered significant. RESULTS: ALS patients had lower FD and FDC values predominantly in the corticospinal tract (CST) and corpus callosum (CC) regions, as well as lower NDI value in the CC, radial crown, and internal capsule compared to HCs. Subgroup analysis based on progression rate and cognitive function showed significant differences in FBA results. The FC in the right CST region was significantly lower in the FP than SP, and the FD in the CC region was significantly lower in the ALS-ci than ALS-nci. Furthermore, a negative correlation was found between the mean FC value and the rate of progression in ALS patients (r = -0.408). DATA CONCLUSION: FBA is a powerful tool for detecting complex cerebral WM microstructural damage for evaluating ALS cognition and disease progression.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE: Retrospective. POPULATION: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES: T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Adsorption Recycling and High-Value Reutilization of Heavy-Metal Ions from Wastewater: As a High-Performance Anode Lithium Battery.

The dazzling adsorbent products make people overlook the harm of heavy metals adsorbed on them. Hazardous waste adsorbents cause secondary pollution. In this study, waste lignocellulose was dissolved by alkaline urea solvent and high-intensity ultrasound, then cross-linked by epichlorohydrin to make hydrogel, which was utilized to adsorb toxic heavy-metal wastewater. In situ deposition and high-temperature carbonization turn the gel that has absorbed heavy metals into carbon aerogel-loaded metal oxide energy storage materials that may be employed as anodes in lithium-ion batteries with excellent electrochemical performance. The best reversible capacity was 435.86 mAh g-1 after 100 cycles at 0.2C, indicating that the hazardous solid waste generated by the removal of heavy metals using biomass-based adsorbent has potential lithium battery applications. Thus, we provide a fresh perspective on the efficient recycling of heavy metals as well as an environmentally friendly, high-value conservation strategy for lowering the danger of heavy-metal hazardous wastes.

Risk prediction of intraoperative pain in percutaneous microwave ablation of lung tumors under CT guidance.

OBJECTIVES: To evaluate the effect of intraoperative pain in microwave ablation of lung tumors (MWALT) on local efficacy and establish the pain risk prediction model. METHODS: It was a retrospectively study. Consecutive patients with MWALT from September 2017 to December 2020 were divided into mild and severe pain groups. Local efficacy was evaluated by comparing technical success, technical effectiveness, and local progression-free survival (LPFS) in two groups. All cases were randomly allocated into training and validation cohorts at a ratio of 7:3. A nomogram model was established using predictors identified by logistics regression in training dataset. The calibration curves, C-statistic, and decision curve analysis (DCA) were used to evaluate the accuracy, ability, and clinical value of the nomogram. RESULTS: A total of 263 patients (mild pain group: n = 126; severe pain group: n = 137) were included in the study. Technical success rate and technical effectiveness rate were 100% and 99.2% in the mild pain group and 98.5% and 97.8% in the severe pain group. LPFS rates at 12 and 24 months were 97.6% and 87.6% in the mild pain group and 91.9% and 79.3% in the severe pain group (p = 0.034; HR: 1.90). The nomogram was established based on three predictors: depth of nodule, puncture depth, and multi-antenna. The prediction ability and accuracy were verified by C-statistic and calibration curve. DCA curve suggested the proposed prediction model was clinically useful. CONCLUSIONS: Severe intraoperative pain in MWALT reduced the local efficacy. An established prediction model could accurately predict severe pain and assist physicians in choosing a suitable anesthesia type. CLINICAL RELEVANCE STATEMENT: This study firstly provides a prediction model for the risk of severe intraoperative pain in MWALT. Physicians can choose a suitable anesthesia type based on pain risk, in order to improve patients' tolerance as well as local efficacy of MWALT. KEY POINTS: • The severe intraoperative pain in MWALT reduced the local efficacy. • Predictors of severe intraoperative pain in MWALT were the depth of nodule, puncture depth, and multi-antenna. • The prediction model established in this study can accurately predict the risk of severe pain in MWALT and assist physicians in choosing a suitable anesthesia type.