Assessment of the clinical value of five noninvasive predictors of metabolic dysfunction-associated steatotic liver disease in Han Chinese adults.

BACKGROUND: Fatty Liver Index (FLI), Triglyceride-Glucose Index (TyG), Lipid Accumulation Product (LAP), Zhejiang University Index (ZJU), and Visceral Adiposity Index (VAI) are five classical predictive models for fatty liver disease. Our cross-sectional study aimed to identify the optimal predictors by comparing the predictive value of five models for metabolic dysfunction-associated steatotic liver disease (MASLD) risk. METHODS: Data on 2687 participants were collected from West China Hospital of Sichuan University. Controlled attenuation parameters assessed by transient elastography were used to effectively diagnose MASLD. Logistic regression analysis was used to estimate the odd ratios and 95% confidence intervals between indices and MASLD risk. Receiver operating characteristic curves were plotted to evaluate the predictive value of indices. RESULTS: This study included 1337 normal and 1350 MASLD samples. The average age of MASLD patients is 47 years old, and the prevalence was higher in males (39.3%) than in females (10.9%). Five indices were positively correlated with MASLD risk, with the strongest correlation for TyG. Overall, the area under the curve of the indicators was: ZJU 0.988, FLI 0.987, LAP 0.982, TyG 0.942, and VAI 0.941. In the gender stratification, ZJU (0.989) performed best in males. FLI (0.988) and ZJU (0.987) had similar predictive ability in females. In the age stratification, FLI performed better in predicting the middle-aged group aged 30-40 years (0.991). CONCLUSION: For Chinese Han adults, ZJU is the best predictive index for initial screening of MASLD. FLI can serve as an alternative tool for ZJU to predict females.

Comparing similarities and differences between NAFLD, MAFLD, and MASLD in the general U.S. population.

Background: Recently, the multisociety Delphi consensus renamed non-alcoholic fatty liver disease (NAFLD) terminology [previously renamed metabolic-associated fatty liver disease (MAFLD)] as metabolic dysfunction-associated steatotic liver disease (MASLD). The aim of this study was to compare the similarities and differences between NAFLD, MAFLD, and MASLD and to clarify the impact of this new name change. Methods: A cross-sectional study of 3,035 general subjects with valid vibration-controlled transient elastography data was conducted based on data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020. NAFLD, MAFLD, and MASLD were defined according to the corresponding consensus criteria. Results: Using controlled attenuation parameter (CAP) ≥274 dB/m and liver stiffness measurements (LSM) ≥9.7 kPa as the cutoff values for the presence of hepatic steatosis and advanced liver fibrosis (ALF), the prevalence of NAFLD, MAFLD, and MASLD were 38.01% (95% CI 35.78-40.29%), 41.09% (39.09-43.12%), and 37.9% (35.70-40.14%), respectively, and the corresponding prevalence of ALF was 10.21% (7.09-14.48%), 10.13% (7.06-14.35%), and 10.24% (7.11-14.53%), respectively. The kappa values for the three definitions were above 0.9. The prevalence and severity of the three definitions remained similar when the sensitivity analyses were performed using different CAP thresholds. The prevalence of NAFLD, MAFLD, MASLD, and ALF increased as the number of cardiometabolic risk factors (CMRF) increased. Conclusions: Our findings highlight the consistency among the three definitions, especially between NAFLD and MASLD, so that the new consensus will not disturb the original NAFLD-related findings. Additionally, more attention should be paid to patients with a high number of CMRFs.

An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism.

Accurate labeling of lung nodules in computed tomography (CT) images is crucial in early lung cancer diagnosis and before nodule resection surgery. However, the irregular shape of lung nodules in CT images and the complex lung environment make it much more challenging to segment lung nodules accurately. On this basis, we propose an improved V-Net segmentation method based on pixel threshold separation and attention mechanism for lung nodules. This method first offers a data augment strategy to solve the problem of insufficient samples in 3D medical datasets. In addition, we integrate the feature extraction module based on pixel threshold separation into the model to enhance the feature extraction ability under different thresholds on the one hand. On the other hand, the model introduces channel and spatial attention modules to make the model pay more attention to important semantic information and improve its generalization ability and accuracy. Experiments show that the Dice similarity coefficients of the improved model on the public datasets LUNA16 and LNDb are 94.9% and 81.1% respectively, and the sensitivities reach 92.7% and 76.9% respectively. which is superior to most existing UNet architecture models and comparable to the manual level segmentation results by medical technologists.

EFFNet: A skin cancer classification model based on feature fusion and random forests.

Computer-aided diagnosis techniques based on deep learning in skin cancer classification have disadvantages such as unbalanced datasets, redundant information in the extracted features and ignored interactions of partial features among different convolutional layers. In order to overcome these disadvantages, we propose a skin cancer classification model named EFFNet, which is based on feature fusion and random forests. Firstly, the model preprocesses the HAM10000 dataset to make each category of training set images balanced by image enhancement technology. Then, the pre-training weights of the EfficientNetV2 model on the ImageNet dataset are fine-tuned on the HAM10000 skin cancer dataset. After that, an improved hierarchical bilinear pooling is introduced to capture the interactions of some features between the layers and enhance the expressive ability of features. Finally, the fused features are passed into the random forests for classification prediction. The experimental results show that the accuracy, recall, precision and F1-score of the model reach 94.96%, 93.74%, 93.16% and 93.24% respectively. Compared with other models, the accuracy rate is improved to some extent and the highest accuracy rate can be increased by about 10%.

Comparison of the diagnostic performance of twelve noninvasive scores of metabolic dysfunction-associated fatty liver disease.

BACKGROUND: The absence of distinct symptoms in the majority of individuals with metabolic dysfunction-associated fatty liver disease (MAFLD) poses challenges in identifying those at high risk, so we need simple, efficient and cost-effective noninvasive scores to aid healthcare professionals in patient identification. While most noninvasive scores were developed for the diagnosis of nonalcoholic fatty liver disease (NAFLD), consequently, the objective of this study was to systematically assess the diagnostic ability of 12 noninvasive scores (METS-IR/TyG/TyG-WC/TyG-BMI/TyG-WtHR/VAI/HSI/FLI/ZJU/FSI/K-NAFLD) for MAFLD. METHODS: The study recruited eligible participants from two sources: the National Health and Nutrition Examination Survey (NHANES) 2017-2020.3 cycle and the database of the West China Hospital Health Management Center. The performance of the model was assessed using various metrics, including area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and subgroup analysis. RESULTS: A total of 7398 participants from the NHANES cohort and 4880 patients from the Western China cohort were included. TyG-WC had the best predictive power for MAFLD risk in the NHANES cohort (AUC 0.863, 95% CI 0.855-0.871), while TyG-BMI had the best predictive ability in the Western China cohort (AUC 0.903, 95% CI 0.895-0.911), outperforming other models, and in terms of IDI, NRI, DCA, and subgroup analysis combined, TyG-WC remained superior in the NAHANES cohort and TyG-BMI in the Western China cohort. CONCLUSIONS: TyG-BMI demonstrated satisfactory diagnostic efficacy in identifying individuals at a heightened risk of MAFLD in Western China. Conversely, TyG-WC exhibited the best diagnostic performance for MAFLD risk recognition in the United States population. These findings suggest the necessity of selecting the most suitable predictive models based on regional and ethnic variations.

Development and validation of a new nomogram to screen for MAFLD.

BACKGROUND AND AIM: Metabolic dysfunction-associated fatty liver disease (MAFLD) poses significant health and economic burdens on all nations. Thus, identifying patients at risk early and managing them appropriately is essential. This study's goal was to develop a new predictive model for MAFLD. Additionally, to improve the new model's clinical utility, researchers limited the variables to readily available simple clinical and laboratory measures. METHODS: Based on the National Health and Nutrition Examination Survey (NHANES) cycle 2017-2020.3, the study was a retrospective cross-sectional study involving 7300 participants. By least absolute shrinkage and selection operator (LASSO) regression, significant indicators independently associated with MAFLD were identified, and a predictive model called the MAFLD prediction nomogram (MPN) was developed. The study then compared the MPN with six existing predictive models for MAFLD. The model was evaluated by measuring the area under receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) curve. RESULTS: In this study, researchers identified nine predictors from 33 variables, including age, race, arm circumference (AC), waist circumference (WC), body mass index (BMI), alanine aminotransferase (ALT)-to-aspartate aminotransferase (AST) ratio, triglyceride-glucose index (TyG), hypertension, and diabetes. The diagnostic accuracy of the MPN for MAFLD was significantly better than that of the other six existing models in both the training and validation cohorts (AUC 0.868, 95% confidence interval (CI) 0.858-0.877, and AUC 0.863, 95% CI 0.848-0.878, respectively). The MPN showed a higher net benefit than the other existing models. CONCLUSIONS: This nonimaging-assisted nomogram based on demographics, laboratory factors, anthropometrics, and comorbidities better predicted MAFLD than the other six existing predictive models. Using this model, the general population with MAFLD can be assessed rapidly.