Novel anthropometric indicators of visceral obesity predict the severity of hyperlipidemic acute pancreatitis.

BACKGROUND: Obesity substantially contributes to the onset of acute pancreatitis (AP) and influences its progression to severe AP. Although body mass index (BMI) is a widely used anthropometric parameter, it fails to delineate the distribution pattern of adipose tissue. To circumvent this shortcoming, the predictive efficacies of novel anthropometric indicators of visceral obesity, such as lipid accumulation products (LAP), cardiometabolic index (CMI), body roundness index (BRI), visceral adiposity index (VAI), A Body Shape Index (ABSI), and Chinese visceral adiposity index (CVAI) were examined to assess the severity of AP. METHOD: The body parameters and laboratory indices of 283 patients with hyperlipidemic acute pancreatitis (HLAP) were retrospectively analysed, and the six novel anthropometric indicators of visceral obesity were calculated. The severity of HLAP was determined using the revised Atlanta classification. The correlation between the six indicators and HLAP severity was evaluated, and the predictive efficacy of the indicators was assessed using area under the curve (AUC). The differences in diagnostic values of the six indicators were also compared using the DeLong test. RESULTS: Patients with moderate to severe AP had higher VAI, CMI, and LAP than patients with mild AP (all P < 0.001). The highest AUC in predicting HLAP severity was observed for VAI, with a value of 0.733 and 95% confidence interval of 0.678-0.784. CONCLUSIONS: This study demonstrated significant correlations between HLAP severity and VAI, CMI, and LAP indicators. These indicators, particularly VAI, which displayed the highest predictive power, were instrumental in forecasting and evaluating the severity of HLAP.

Space-confined manganese oxides nanosheets for efficient catalytic decomposition of ozone.

Ground-level ozone has long posed a substantial menace to human well-being and the ecological milieu. The widely reported manganese-based catalysts for ozone decomposition still facing the persisting issues encompass inefficiency and instability. To surmount these challenges, we developed a mesoporous silica thin films with perpendicular nanochannels (SBA(⊥)) confined Mn3O4 catalyst (Mn3O4@SBA(⊥)). Under a weight hourly space velocity (WHSV) of 500,000 mL g-1 h-1, the Mn3O4@SBA(⊥) catalyst exhibited 100% ozone decomposition efficiency in 5 h and stability across a wide humidity range, which exceed the performance of bulk Mn3O4 and Mn3O4 confine in commonly reported SBA-15. Rapidly decompose 20 ppm O3 to a safety level below 100 µg m-3 in the presence of dust in smog chamber (60 × 60 × 60 cm) was also realized. This prominent catalytic performance can be attributed to the unique confined structure engenders the highly exposed active sites, facilitate the reactant-active sites contact and impeded the water accumulation on the active sites. This work offers new insights into the design of confined structure catalysts for air purification.

Low blood flow ratio is associated with hemorrhagic transformation secondary to mechanical thrombectomy in patients with acute ischemic stroke.

BACKGROUND AND PURPOSE: A significant decrease of cerebral blood flow (CBF) is a risk factor for hemorrhagic transformation (HT) in acute ischemic stroke (AIS). This study aimed to ascertain whether the ratio of different CBF thresholds derived from computed tomography perfusion (CTP) is an independent risk factor for HT after mechanical thrombectomy (MT). METHODS: A retrospective single center cohort study was conducted on patients with AIS undergoing MT at the First Affiliated Hospital of Wenzhou Medical University from August 2018 to December 2023. The perfusion parameters before thrombectomy were obtained according to CTP automatic processing software. The low blood flow ratio (LFR) was defined as the ratio of brain volume with relative CBF <20 % over volume with relative CBF <30 %. HT was evaluated on the follow-up CT images. Binary logistic regression was used to analyze the correlation between parameters that differ between the two groups with regards to HT occurrence. The predictive efficacy was assessed utilizing the receiver operating characteristic curve. RESULTS: In total, 243 patients met the inclusion criteria. During the follow-up, 46.5 % of the patients (113/243) developed HT. Compared with the Non-HT group, the HT group had a higher LFR (0.47 (0.34-0.65) vs. 0.32 (0.07-0.56); P < 0.001). According to the binary logistic regression analysis, the LFR (aOR: 6.737; 95 % CI: 1.994-22.758; P = 0.002), Hypertension history (aOR: 2.231; 95 % CI: 1.201-4.142; P = 0.011), plasma FIB levels before MT (aOR: 0.641; 95 % CI: 0.456-0.902; P = 0.011), and the mismatch ratio (aOR: 0.990; 95 % CI: 0.980-0.999; P = 0.030) were independently associated with HT secondary to MT. The area under the curve of the regression model for predicting HT was 0.741. CONCLUSION: LFR, a ratio quantified via CTP, demonstrates potential as an independent risk factor of HT secondary to MT.

Association of follow-up neutrophil-to-lymphocyte ratio and systemic inflammation response index with stroke-associated pneumonia and functional outcomes in cerebral hemorrhage patients: a case controlled study.

BACKGROUND: Neutrophil-to-lymphocyte ratio (NLR) and systemic inflammation response index (SIRI) at admission are independent diagnostic biomarkers in stroke-associated pneumonia (SAP). Our study aimed to investigate the association between NLR, SIRI, specifically follow-up NLR and SIRI, and SAP, as well as their relationship with functional outcomes. MATERIALS AND METHODS: We retrospectively included 451 consecutive ICH patients from May 2017 to May 2019. We conducted univariate and multivariable analyses to identify the factors independently associated with SAP and poor functional outcomes. RESULTS: Compared to 127 (28.16%) patients diagnosed with SAP, those without SAP had both lower baseline and follow-up NLR and SIRI values (P<0.001). After adjustments, we found that baseline NLR (OR, 1.039 [95% CI, 1.003-1.077]; P=0.036) and follow-up NLR (OR, 1.054 [95% CI, 1.011-1.098]; P=0.012) were independently associated with SAP. The follow-up NLR was also associated with a higher mRS (OR, 1.124 [95% CI, 1.025-1.233]; P=0.013) and lower ADL-MBI score (OR, 1.167 [95% CI, 1.057-1.289]; P=0.002) at discharge. Multivariable analysis indicated that advanced age and nasogastric tube feeding were independently associated with SAP (P<0.05). We constructed a dynamic nomogram to identify SAP risk. Further subgroup analysis revealed that baseline NLR (OR, 1.062 [95% CI, 1.007-1.120]; P=0.026) is independently associated with SAP in the nasogastric feeding group, while follow-up NLR (OR, 1.080 [95% CI, 1.024-1.139]; P=0.005) was associated with the occurrence of SAP in non-nasogastric feeding patients. CONCLUSIONS: We found elevated baseline and follow-up NLR values were associated with SAP occurrence, and increasing follow-up NLR indicated poor functional outcomes. Inflammatory markers at different stages may offer individualized guidance for patients receiving various treatments.

Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans. METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110). RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001). CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.

Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction.

OBJECTIVES: To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE. METHODS: We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix. RESULTS: AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05). CONCLUSION: The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions. ADVANCES IN KNOWLEDGE: This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.

An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage.

OBJECTIVE: Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it. METHODS: This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram. RESULTS: Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically. CONCLUSIONS: Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH.

Development and validation of a nomogram based on CT texture analysis for discriminating minimally invasive adenocarcinoma from glandular precursor lesions in sub­centimeter pulmonary ground glass nodules.

In a recent reclassification, adenocarcinoma in situ has been redefined as a glandular precursor lesion (GPL), alongside adenomatous hyperplasia. This updated classification necessitates corresponding adaptations in clinical diagnostic and therapeutic protocols. Consequently, the present study aimed to construct and validate a nomogram utilizing computed tomography (CT) texture features to effectively discriminate between minimally invasive adenocarcinoma (MIA) and GPL within sub-centimeter pulmonary ground glass nodules (GGNs). To achieve this objective, the present study employed rigorous statistical methodologies, including the Mann-Whitney U test and binary logistic regression analysis, to identify distinguishing features and establish predictive models. Subsequently, the diagnostic performance of these models underwent evaluation through receiver operating characteristic (ROC) curves. The area under the curve (AUC) in ROC curves was compared using DeLong's test. Additionally, the nomogram was constructed using R software and its diagnostic performance was validated through calibration curves. Within both the training and validation datasets, the AUCs were observed to be 0.992 [95% confidence interval (CI): 0.980-1.000] and 0.975 (95% CI: 0.935-1.000), respectively. DeLong's test revealed significant disparities in the AUCs between the nomogram and single-parameter models (P<0.001). Furthermore, calibration curves demonstrated concordance between the training and validation datasets. In conclusion, the application of a CT texture-based nomogram model has demonstrated aptitude in differentiating between MIA and GPL within sub-centimeter GGNs. This model streamlines the identification of optimal surgical interventions and enhances the sphere of clinical decision-making and management.

The effect of shikonin on the metabolism of lapatinib in vitro, and in vivo.

PURPOSE: The purpose of this study was to develop an assay for simultaneous determination of lapatinib and its metabolites (N-dealkylated lapatinib and O-dealkylated lapatinib) by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), and to determine the interaction between shikonin and lapatinib in vitro, in vivo, in silico and its mechanism of action. METHODS: A new UPLC-MS/MS method for the determination of the concentrations of lapatinib and its metabolites was developed. In vivo, Sprague-Dawley (SD) rats were given lapatinib with or without shikonin. In vitro, to study the interaction mechanism, rat liver microsomes (RLMs), human liver microsomes (HLMs) and recombinant human CYP3A4.1 were used for determining enzyme kinetics. Lastly, we used in silico molecular docking to investigate the molecular mechanism of inhibition. RESULTS: The selectivity, precision, accuracy, stability, matrix effect and recovery of UPLC-MS/MS all met the requirements of quantitative analysis of biological samples. Administration of lapatinib combined with shikonin resulted in significantly increased pharmacokinetic parameters (AUC(0-t) and Cmax) of lapatinib, indicating that shikonin increased the exposure of lapatinib in rats. Moreover, in vitro kinetic measurements indicated that shikonin was a time-independent inhibitor, which inhibited the metabolism of lapatinib through a competitive mechanism in RLMs, while noncompetitive inhibition type in both HLMs and CYP3A4.1. Molecular docking analysis further verified the non-competitive inhibition of shikonin on lapatinib in CYP3A4.1. CONCLUSION: We developed an UPLC-MS/MS assay for simultaneous determination of lapatinib and its metabolites. It could be successfully applied to the study of pharmacokinetic interaction of shikonin on the inhibition of lapatinib metabolism in vivo and in vitro. In the end, further studies are needed to determine if such interactions are indeed valid in humans and if the interaction is clinically relevant.

Acute Phase Blood Glucose Levels and Functional Outcomes in Patients with Spontaneous Intracerebral Hemorrhage.

Objective: Post-stroke hyperglycemia as a common phenomenon is associated with unfavorable outcomes. Focusing on admission hyperglycemia, other markers of dysglycemia were overlooked. This study aimed to explore the contribution of acute phase blood glucose levels in combination with other radiological signs to the prognostication of functional outcomes in patients with spontaneous intracerebral hemorrhage (sICH). Methods: Consecutive patients with sICH with at least five random plasma glucose measurements and complete radiological data during hospitalization were included. We calculated the average, maximum, minimum, standard deviation, and coefficient of variation of blood glucose levels for each patient. Radiological data, including island, black hole, blend, and satellite signs were collected. Functional outcomes were evaluated using the Barthel index. Unfavorable outcomes were defined as a Barthel index score ≤ 60. Univariate and multivariate analyses were performed to identify independent predictors of unfavorable outcomes. Results: Two hundred and thirty-eight patients (mean age 58.5, 163 men and 75 women) were included, and 71 had a history of diabetes. Unfavorable outcomes occurred in 107 patients (45.0%) at 3 months. Multivariate logistic regression analysis demonstrated that maximum blood glucose levels (odds ratio, 1.256; 95% confidence interval, 1.124‒1.404; p < 0.001) and island sign (odds ratio, 2.701; 95% confidence interval, 1.322‒5.521; p = 0.006) were independent predictors of unfavorable outcomes in the nondiabetic group. Meanwhile, patients without diabetes who experienced hematoma expansion had higher average (p = 0.036) and maximum blood glucose levels (p = 0.014). Interpretation: Maximum blood glucose levels and island sign were independently associated with unfavorable outcomes in patients without diabetes, whereas no glycemic variability indices were associated with unfavorable outcomes. Glucose levels influenced hematoma expansion and functional outcomes, particularly in patients without diabetes with sICH. Thus, clinical management of blood glucose levels should be strengthened for patients with sICH with or without a history of diabetes.

Predicting very early recurrence in intrahepatic cholangiocarcinoma after curative hepatectomy using machine learning radiomics based on CECT: A multi-institutional study.

BACKGROUND: Even after curative resection, the prognosis for patients with intrahepatic cholangiocarcinoma (iCCA) remains disappointing due to the extremely high incidence of postoperative recurrence. METHODS: A total of 280 iCCA patients following curative hepatectomy from three independent institutions were recruited to establish the retrospective multicenter cohort study. The very early recurrence (VER) of iCCA was defined as the appearance of recurrence within 6 months. The 3D tumor region of interest (ROI) derived from contrast-enhanced CT (CECT) was used for radiomics analysis. The independent clinical predictors for VER were histological stage, AJCC stage, and CA199 levels. We implemented K-means clustering algorithm to investigate novel radiomics-based subtypes of iCCA. Six types of machine learning (ML) algorithms were performed for VER prediction, including logistic, random forest (RF), neural network, bayes, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Additionally, six clinical ML (CML) models and six radiomics-clinical ML (RCML) models were developed to predict VER. Predictive performance was internally validated by 10-fold cross-validation in the training cohort, and further evaluated in the external validation cohort. RESULTS: Approximately 30 % of patients with iCCA experienced VER with extremely discouraging outcome (Hazard ratio (HR) = 5.77, 95 % Confidence Interval (CI) = 3.73-8.93, P < 0.001). Two distinct iCCA subtypes based on radiomics features were identified, and subtype 2 harbored a higher proportion of VER (47.62 % Vs 25.53 %) and significant shorter survival time than subtype 1. The average AUC values of the CML and RCML models were 0.744 ± 0.018, and 0.900 ± 0.014 in the training cohort, and 0.769 ± 0.065 and 0.929 ± 0.027 in the external validation cohort, respectively. CONCLUSION: Two radiomics-based iCCA subtypes were identified, and six RCML models were developed to predict VER of iCCA, which can be used as valid tools to guide individualized management in clinical practice.

Predicting the formation of mixed pattern hemorrhages in ruptured middle cerebral artery aneurysms based on a decision tree model: A multicenter study.

OBJECTIVE: Mixed-pattern hemorrhages (MPH) commonly occur in ruptured middle cerebral artery (MCA) aneurysms and are associated with poor clinical outcomes. This study aimed to predict the formation of MPH in a multicenter database of MCA aneurysms using a decision tree model. METHODS: We retrospectively reviewed patients with ruptured MCA aneurysms between January 2009 and June 2020. The MPH was defined as subarachnoid hemorrhages with intracranial hematomas and/or intraventricular hemorrhages and/or subdural hematomas. Univariate and multivariate logistic regression analyses were used to explore the prediction factors of the formation of MPH. Based on these prediction factors, a decision tree model was developed to predict the formation of MPH. Additional independent datasets were used for external validation. RESULTS: We enrolled 436 patients with ruptured MCA aneurysms detected by computed tomography angiography; 285 patients had MPH (65.4%). A multivariate logistic regression analysis showed that age, aneurysm size, multiple aneurysms, and the presence of a daughter dome were the independent prediction factors of the formation of MPH. The areas under receiver operating characteristic curves of the decision tree model in the training, internal, and external validation cohorts were 0.951, 0.927, and 0.901, respectively. CONCLUSION: Age, aneurysm size, the presence of a daughter dome, and multiple aneurysms were the independent prediction factors of the formation of MPH. The decision tree model is a useful visual triage tool to predict the formation of MPH that could facilitate the management of unruptured aneurysms in routine clinical work.

Genetic variations of CYP3A4 on the metabolism of itraconazole in vitro.

Itraconazole is a triazole anti-infective drug that has been proven to prevent and treat a variety of fungal and viral infections and has been considered to be a potential therapeutic remedy for COVID-19 treatment. In this study, we aimed to completely evaluate the impacts of Cytochrome P450 3A4 (CYP3A4) variant proteins and drug interactions on the metabolism of itraconazole in recombinant insect microsomes, and to characterize the potential mechanism of substrate selectivity. Incubations with itraconazole (0.2-15 µM) in the presence/absence of lopinavir or darunavir were assessed by CYP3A4 variants, and the metabolite hydroxyitraconazole concentrations were measured by UPLC-MS/MS. Our data showed that when compared with CYP3A4.1, 4 variants (CYP3A4.9, .10, .28 and .34) displayed no significant differences, and 3 variants (CYP3A4.14, .15 and .19) exhibited increased intrinsic clearance (CLint), whereas the remaining 17 variant proteins showed decreased enzyme activities for the catalysis of itraconazole. Moreover, the inhibitory effects of lopinavir and darunavir on itraconazole metabolism varied in different degrees. Furthermore, different changed trend of the kinetic parameters in ten variants (CYP3A4.5, .9, .10, .16, .19, .24, .28, .29, .31, and .33) were observed, especially CYP3A4.5 and CYP3A4.16, and this may be related to the metabolic site-heme iron atom distance. In the present study, we functionally analyzed the effects of 25 CYP3A4 protein variants on itraconazole metabolism for the first time, and provided comprehensive data on itraconazole metabolism in vitro. This may help to better assess the metabolism and elimination of itraconazole in clinic to improve the safety and efficacy of its clinical treatment and also provide new possibilities for the treatment of COVID-19.

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study.

Background: Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. Methods: We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. Results: The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. Conclusions: The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

The interaction of global small vessel disease burden and Alzheimer's disease pathologies do not change the independent association of amyloid-beta with hippocampal volume: A longitudinal study on mild cognitive impairment subjects.

The purpose of this study was to investigate whether the co-existence of global small vessel disease (SVD) burdens and Alzheimer's disease (AD) pathologies change hippocampal volume (HV) and cognitive function of mild cognitive impairment (MCI) subjects. We obtained MRI images, cerebrospinal fluid biomarkers (Aß1-42 and p-tau), and neuropsychological tests of 310 MCI subjects from ADNI. The global SVD score was assessed. We used linear regression and linear mixing effect to analyze the effects of global SVD burdens, AD pathologies, and their interactions (SVD*AD) on baseline and longitudinal HV and cognition respectively. We used simple mediation effect to analyze the influencing pathways. After adjusting for global SVD and SVD*AD, Aß remained independently correlated with baseline and longitudinal HV (std ß = 0.294, p = .007; std ß = 0.292, p < .001), indicating that global SVD did not affect the correlation between Aß and HV. Global SVD score was correlated with longitudinal but not baseline HV (std ß = 0.470, p = .050), suggesting that global SVD may be more representative of long-term permanent impairment. Global SVD, AD pathologies, and SVD*AD were independently correlated with baseline and longitudinal cognitions, in which the association of Aß (B = 0.005, 95% CI: 0.005; 0.024) and p-tau (B = -0.002, 95% CI: -0.004; -0.000) with cognition were mediated by HV, suggesting that HV is more likely to explain the progression caused by AD pathology than SVD. The co-existence of global SVD and AD pathologies did not affect the individual association of Aß on HV; HV played a more important role in the influence of AD pathology on cognition than in SVD.

An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer.

PURPOSE: To establish a nomogram integrating radiomics features based on ultrasound images and clinical parameters for predicting the prognosis of patients with endometrial cancer (EC). MATERIALS AND METHODS: A total of 175 eligible patients with ECs were enrolled in our study between January 2011 and April 2018. They were divided into a training cohort (n = 122) and a validation cohort (n = 53). Least absolute shrinkage and selection operator (LASSO) regression were applied for selection of key features, and a radiomics score (rad-score) was calculated. Patients were stratified into high risk and low-risk groups according to the rad-score. Univariate and multivariable COX regression analysis was used to select independent clinical parameters for disease-free survival (DFS). A combined model based on radiomics features and clinical parameters was ultimately established, and the performance was quantified with respect to discrimination and calibration. RESULTS: Nine features were selected from 1130 features using LASSO regression in the training cohort, which yielded an area under the curve (AUC) of 0.823 and 0.792 to predict DFS in the training and validation cohorts, respectively. Patients with a higher rad-score were significantly associated with worse DFS. The combined nomogram, which was composed of clinically significant variables and radiomics features, showed a calibration and favorable performance for DFS prediction (AUC 0.893 and 0.885 in the training and validation cohorts, respectively). CONCLUSION: The combined nomogram could be used as a tool in predicting DFS and may assist individualized decision making and clinical treatment.

A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk.

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely and appropriate treatment because the fatality rate after rupture is 50 % . Existing methods relying on morphological features (e.g., height-width ratio) measured manually by neuroradiologists are labor intensive and have limited use for risk assessment. Therefore, we propose an end-to-end deep-learning method, called TransIAR net, to automatically learn the morphological features from 3D computed tomography angiography (CTA) data and accurately predict the status of IA rupture. We devise a multiscale 3D convolutional neural network (CNN) to extract the structural patterns of the IA and its neighborhood with a dual branch of shared network structures. Moreover, we learn the spatial dependence within the IA neighborhood with a transformer encoder. Our experiments demonstrated that the features learned by TransIAR are more effective and robust than handcrafted features, resulting in a 10 % - 15 % improvement in the accuracy of rupture status prediction.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study.

PURPOSE: Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection. METHODS: Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS: 127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03). CONCLUSION: ML radiomics models based on CECT are valuable in predicting ER in ICC.

Prediction of Response to Lenvatinib Monotherapy for Unresectable Hepatocellular Carcinoma by Machine Learning Radiomics: A Multicenter Cohort Study.

PURPOSE: We aimed to construct machine learning (ML) radiomics models to predict response to lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC). EXPERIMENTAL DESIGN: Patients with HCC receiving lenvatinib monotherapy at three institutions were retrospectively identified and assigned to training and external validation cohorts. Tumor response after initiation of lenvatinib was evaluated. Radiomics features were extracted from contrast-enhanced CT images. The K-means clustering algorithm was used to distinguish radiomics-based subtypes. Ten ML radiomics models were constructed and internally validated by 10-fold cross-validation. These models were subsequently verified in an external validation cohort. RESULTS: A total of 109 patients were identified for analysis, namely, 74 in the training cohort and 35 in the external validation cohort. Thirty-two patients showed partial response, 33 showed stable disease, and 44 showed progressive disease. The overall response rate (ORR) was 29.4%, and the disease control rate was 59.6%. A total of 224 radiomics features were extracted, and 25 significant features were identified for further analysis. Two distant radiomics-based subtypes were identified by K-means clustering, and subtype 1 was associated with a higher ORR and longer progression-free survival (PFS). Among the 10 ML algorithms, AutoGluon displayed the highest predictive performance (AUC = 0.97), which was relatively stable in the validation cohort (AUC = 0.93). Kaplan-Meier analysis showed that responders had a better overall survival [HR = 0.21; 95% confidence interval (CI): 0.12-0.36; P < 0.001] and PFS (HR = 0.14; 95% CI: 0.09-0.22; P < 0.001) than nonresponders. CONCLUSIONS: Valuable ML radiomics models were constructed, with favorable performance in predicting the response to lenvatinib monotherapy for unresectable HCC.