Non-invasive Diagnostic Model Research Articles

Evaluating the efficacy of 8 non-invasive models in predicting MASLD and progression: a prospective study

BackgroundSelecting the optimal non-invasive diagnostic model for MASLD (Metabolic Dysfunction-Associated Steatosis Liver Disease) and steatosis progression is a critical issue given the variety of available models. We aimed to compare the performance of eight clinical prediction models for diagnosing and predicting the progression of hepatic steatosis using MRI-PDFF (Magnetic Resonance Imaging-Derived Proton Density Fat Fraction), and validate the findings with FibroScan and histopathological results.MethodsIn this study, 846 participants were initially enrolled, with 108 undergoing liver biopsy and 706 completing one-year follow-up, including 26 who underwent repeat biopsy. We calculated scores for eight clinical prediction models (FAST, KNAFLD, HSI, FLI, Liver Fat Score, Liver Fat Equation, BAAT, LAP) using collected clinical data and defined steatosis progression as a 30% relative increase in liver fat content (LFC) measured by MRI-PDFF. CAP(Controlled Attenuation Parameter) and LSM (Liver Stiffness Measurement) were obtained by Fibroscan. MRI-PDFF served as the reference standard for evaluating model accuracy, and sensitivity analyses were performed using liver biopsy and Fibroscan results.ResultsAmong the eight clinical models, NAS (nonalcoholic fatty liver disease activity score) showed higher correlation with the FAST and KNAFLD models (r: 0.62 and 0.52, respectively). Among the whole cohort (N = 846), KNAFLD was the best model for predicting different degrees of hepatic steatosis (AUC = 0.84). When the KNAFLD score was above 2.935, LFC was significantly higher (4.4% vs. 19.7%, P < 0.001). After 1 year of follow-up (N = 706), FAST performed best in predicting MASLD progression (AUC = 0.84); with dFAST > -0.02, LFC increased (8.6–10.9%, P < 0.05), mean LSM increased by 0.51 kPa, and with dFAST < -0.02, LFC significantly decreased (11.5–8.5%, P < 0.05), mean LSM and NAS decreased by 0.87 kPa and 0.76, respectively (both P < 0.05).ConclusionsMost models demonstrated good diagnostic and prognostic capabilities for hepatic steatosis, with FAST and KNAFLD showing particular promise as primary non-invasive tools in clinical practice.Trail RegistrationChinese Clinical Trial Registry NO: ChiCTR2100054743, Registered December 26, 2021.

Noninvasive diagnostic modalities and prediction models for detecting pulmonary hypertension associated with interstitial lung disease: a narrative review.

Pulmonary hypertension (PH) is highly prevalent in patients with interstitial lung disease (ILD) and is associated with increased morbidity and mortality. Widely available noninvasive screening tools are warranted to identify patients at risk for PH, especially severe PH, that could be managed at expert centres. This review summarises current evidence on noninvasive diagnostic modalities and prediction models for the timely detection of PH in patients with ILD. It critically evaluates these approaches and discusses future perspectives in the field. A comprehensive literature search was carried out in PubMed and Scopus, identifying 39 articles that fulfilled inclusion criteria. There is currently no single noninvasive test capable of accurately detecting and diagnosing PH in ILD patients. Estimated right ventricular pressure (RVSP) on Doppler echocardiography remains the single most predictive factor of PH, with other indirect echocardiographic markers increasing its diagnostic accuracy. However, RVSP can be difficult to estimate in patients due to suboptimal views from extensive lung disease. The majority of existing composite scores, including variables obtained from chest computed tomography, pulmonary function tests and cardiopulmonary exercise tests, were derived from retrospective studies, whilst lacking validation in external cohorts. Only two available scores, one based on a stepwise echocardiographic approach and the other on functional parameters, predicted the presence of PH with sufficient accuracy and used a validation cohort. Although several methodological limitations prohibit their generalisability, their use may help physicians to detect PH earlier. Further research on the potential of artificial intelligence may guide a more tailored approach, for timely PH diagnosis.

Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records.

Colorectal Polyps are the main source of precancerous lesions in colorectal cancer. To increase the early diagnosis of tumors and improve their screening, we aimed to develop a simple and non-invasive diagnostic prediction model for colorectal polyps based on machine learning (ML) and using accessible health examination records. We conducted a single-center observational retrospective study in China. The derivation cohort, consisting of 5426 individuals who underwent colonoscopy screening from January 2021 to January 2024, was separated for training (cohort 1) and validation (cohort 2). The variables considered in this study included demographic data, vital signs, and laboratory results recorded by health examination records. With features selected by univariate analysis and Lasso regression analysis, nine machine learning methods were utilized to develop a colorectal polyp diagnostic model. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley additive explanation method (SHAP) was used to rank the feature importance and explain the final model. 14 independent predictors were identified as the most valuable features to establish the models. The adaptive boosting machine (AdaBoost) model exhibited the best performance among the 9 ML models in cohort 1, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) of 0.632 (0.618-0.646), 0.635 (0.550-0.721), 0.674 (0.591-0.758), 0.593 (0.576-0.611), 0.673 (0.654-0.691), 0.608 (0.560-0.655) and 0.687 (0.626-0.749), respectively. The final model gave an AUC of 0.675 in cohort 2. Additionally, the precision recall (PR) curve for the AdaBoost model reached the highest AUPR of 0.648, positioning it nearest to the upper right corner. SHAP analysis provided visualized explanations, reaffirming the critical factors associated with the risk of colorectal polyps in the asymptomatic population. This study integrated the clinical and laboratory indicators with machine learning techniques to establish the predictive model for colorectal polyps, providing non-invasive, cost-effective screening strategies for asymptomatic individuals and guiding decisions for further examination and treatment.

Development of a non-invasive diagnostic model for high-risk esophageal varices based on radiomics of spleen CT.

To evaluate the diagnostic performance of radiomics models derived from multi-phase spleen CT for high-risk esophageal varices (HREV) in cirrhotic patients. We retrospectively selected cirrhotic patients with esophageal varices from two hospitals from September 2019 to September 2023. Patients underwent non-contrast and contrast-enhanced CT scans and were categorized into HREV and non-HREV groups based on endoscopic evaluations. Radiomics features were extracted from spleen CT images in non-contrast, arterial, and portal venous phases, with feature selection via lasso regression and Pearson's correlation. Ten machine learning models were developed to diagnose HREV, evaluated by area under the curve (AUC). The AUC values of the three groups of models were statistically compared by the Kruskal-Wallis H test and Bonferroni-corrected Mann-Whitney U test. A p-value less than 0.05 was considered statistically significant. Among 233 patients, 11, 6, and 11 features were selected from non-contrast, arterial, and portal venous phases, respectively. Significant differences in AUC values were observed across phases (p < 0.05), and the arterial phase models showed the highest AUC values. The best model in arterial phase was the logical regression model, whose AUC value was 0.85, sensitivity was 83.3%, specificity was 80% and F1 score was 0.81. Radiomics models based on spleen CT, especially the arterial phase models, demonstrate high diagnostic accuracy for HREV, offering the potential for early detection and intervention.

Identification and validation of plasma AGRN as a novel diagnostic biomarker of hepatitis B Virus-related chronic hepatitis and liver fibrosis/cirrhosis.

The aim of this study was to find novel biomarkers and develop a non-invasive, effective diagnostic model for hepatitis B Virus-related chronic hepatitis and liver fibrosis/cirrhosis. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to assess the expression of differentially expressed genes (AGRN, JAG1, CCL5, ID3, CCND1, and CAPN2) in peripheral blood mononuclear cells (PBMCs) from healthy subjects, chronic hepatitis B (CHB), and liver fibrosis/cirrhosis (LF/LC) patients. The molecular mechanisms underlying AGRN-regulated CHB were further explored and verified in LX2 cells, in which small interfering RNA (siRNA) was used to block AGRN gene expression. Finally, enzyme-linked Immunosorbent Assay (ELISA) was used to measure AGRN protein expression in 100 healthy volunteers, 100 CHB patients, and 100 LF/LC patients, and the efficacy of the diagnostic model was assessed by the Area Under the Curve (AUC). AGRN mRNA displayed a steady rise in the PBMCs of normal, CHB, and LF/LC patients. Besides, AGRN expression was markedly elevated in activated LX2 cells, whereas the expression of COL1 and α-SMA decreased when AGRN was inhibited using siRNA. In addition, downregulation of AGRN can reduce the gene expression of β-catenin and c-MYC while upregulating the expression of GSK-3β. Furthermore, PLT and AGRN were used to develop a non-invasive diagnostic model (PA). To identify CHB patients from healthy subjects, the AUC of the PA model was 0.951, with a sensitivity of 87.0% and a specificity of 91.0%. The AUC of the PA model was 0.922 with a sensitivity of 82.0% and a specificity of 90.0% when differentiating between LF/LC and CHB patients. The current study indicated that AGRN could be a potential plasma biomarker and the established PA model could improve the diagnostic accuracy for HBV-related liver diseases.

Biliary atresia and cholestasis plasma non-targeted metabolomics unravels perturbed metabolic pathways and unveils a diagnostic model for biliary atresia

The clinical diagnosis of biliary atresia (BA) poses challenges, particularly in distinguishing it from cholestasis (CS). Moreover, the prognosis for BA is unfavorable and there is a dearth of effective non-invasive diagnostic models for detection. Therefore, the aim of this study is to elucidate the metabolic disparities among children with BA, CS, and normal controls (NC) without any hepatic abnormalities through comprehensive metabolomics analysis. Additionally, our objective is to develop an advanced diagnostic model that enables identification of BA. The plasma samples from 90 children with BA, 48 children with CS, and 47 NC without any liver abnormalities children were subjected to metabolomics analysis, revealing significant differences in metabolite profiles among the 3 groups, particularly between BA and CS. A total of 238 differential metabolites were identified in the positive mode, while 89 differential metabolites were detected in the negative mode. Enrichment analysis revealed 10 distinct metabolic pathways that differed, such as lysine degradation, bile acid biosynthesis. A total of 18 biomarkers were identified through biomarker analysis, and in combination with the exploration of 3 additional biomarkers (LysoPC(18:2(9Z,12Z)), PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0), and Biliverdin-IX-α), a diagnostic model for BA was constructed using logistic regression analysis. The resulting ROC area under the curve was determined to be 0.968. This study presents an innovative and pioneering approach that utilizes metabolomics analysis to develop a diagnostic model for BA, thereby reducing the need for unnecessary invasive examinations and contributing to advancements in diagnosis and prognosis for patients with BA.

Large-scale, comprehensive plasma metabolomic analyses reveal potential biomarkers for the diagnosis of early-stage coronary atherosclerosis

BackgroundCoronary atherosclerosis (CAS) is a prevalent and chronic life-threatening disease. However, the detection of CAS at an early stage is difficult because of the lack of effective noninvasive diagnostic methods. The present study aimed to characterize the plasma metabolome of early-stage CAS patients to discover metabolomic biomarkers, develop a novel metabolite-based model for accurate noninvasive diagnosis of early-stage CAS, and explore the underlying metabolic mechanisms involved. MethodsA total of 100 patients with early-stage CAS and 120 age- and sex-matched control subjects were recruited from the Chinese Han population and further randomly divided into training (n = 120) and test sets (n = 100). The metabolomic profiles of the plasma samples were analyzed by an integrated untargeted liquid chromatography–mass spectrometry approach, including two separation modes and two ionization modes. Univariate and multivariate statistical analyses were employed to identify potential biomarkers and construct an early-stage CAS diagnostic model. ResultsThe integrated analytical method established herein improved metabolite coverage compared with single chromatographic separation and MS ionization mode. A total of 80 metabolites were identified as potential biomarkers of early-stage CAS, and these metabolites were mainly involved in glycerophospholipid, fatty acid, sphingolipid, and amino acid metabolism. An effective diagnostic model for early-stage CAS was established, incorporating 11 metabolites and achieving areas under the receiver operating characteristic curve (AUCs) of 0.984 and 0.908 in the training and test sets, respectively. ConclusionsOur study not only successfully developed an effective noninvasive diagnostic model for identifying early-stage CAS but also provided novel insights into the pathogenesis of CAS.

Adipokine (adiponectin-rs1501299) Gene Variant and Patient Characteristics in Relation to Metabolic-associated Fatty Liver Disease

BackgroundSeveral genetic and metabolic variables, most notably the variation in the adipokine gene rs1501298, have been linked to metabolic-associated fatty liver disease etiopathogenesis (MAFLD). Liver biopsy, the gold standard for diagnosing MAFLD, is an invasive procedure; therefore, alternative diagnostic methods are required. Consequently, the integration of these metabolic variables with some of the patients’ characteristics may facilitate the development of non-invasive diagnostic methods that aid in the early detection of MAFLD, identification of at-risk individuals, and planning of management strategies. MethodsThis study included 224 Egyptians (107 healthy individuals and 117 MAFLD patients). Age, sex, BMI, clinical and laboratory characteristics, and rs1501299 adipokine gene polymorphisms were examined. The Rs1501299 variant, insulin resistance, hypertension, obesity, blood pressure, lipid profile, hemoglobin A1C level, and hepatic fibrosis predictors were evaluated for MAFLD risk. The feasibility and effectiveness of developing non-invasive MAFLD diagnostic models will be investigated. ResultsThe +276G/T (rs1501299) polymorphism (GG vs GT/TT) was linked with MAFLD (OR: 0.43, CI: 0.26-0.69, p=0.002). The GG variants had lower MAFLD rates than those of the GT and TT variants. In addition to altered lipid profiles, patients with MAFLD showed increased gamma-glutamyl transferase levels (GGT: 56 IU/L vs. 36 IU/L). Genetic diversity also affects the accuracy of hepatic fibrosis and steatosis prediction. Hepatic fibrosis and steatosis predictors had ROC AUCs of 0.529%, 0.846%, and 0.700–0.825%, respectively. We examined a diagnostic model based on these variables, and demonstrated its effectiveness. ConclusionThe Adipokine variant rs1501299 increased the risk of MAFLD. Identifying and genotyping this variation and other metabolic variables allows for a non-invasive diagnostic model for early MAFLD diagnosis and identification of those at risk. This study illuminates the prevention and management of MAFLD. Further research with more participants is needed to verify these models and to prove their MAFLD diagnostic efficacy.

TNF-α is a predictive marker in distinguishing myeloproliferative neoplasm and idiopathic erythrocytosis/thrombocytosis: development and validation of a non-invasive diagnostic model.

Philadelphia-chromosome negative myeloproliferative neoplasms (MPN) exhibit phenotypic similarities with JAK/STAT-unmutated idiopathic erythrocytosis and thrombocytosis (IE/IT). We aimed to develop a clinical diagnostic model to discern MPN and IE/IT. A retrospective study was performed on 77 MPN patients and 32 IE/IT patients in our center from January 2018 to December 2023. We investigated the role of hemogram, cytokine and spleen size in differentiating MPN and IE/IT among newly onset erythrocytosis and thrombocytosis patients. Independent influencing factors were integrated into a nomogram for individualized risk prediction. The calibration and discrimination ability of the model were evaluated by concordance index (C-index), calibration curve. MPN had significantly higher TNF-α level than IE/IT, and the TNF-α level is correlated with MF-grade. Multivariable analyses revealed that TNF-α, PLT count, age, size of spleen were independent diagnostic factors in differentiating MPN and IE/IT. Nomograms integrated the above 4 factors for differentiating MPN and IE/IT was internally validated and had good performance, the C-index of the model is 0.979. The elevation of serum TNF-α in MPN patients is of diagnostic significance and is correlated with the severity of myelofibrosis. The nomogram incorporating TNF-α with age, PLT count and spleen size presents a noteworthy tool in the preliminary discrimination of MPN patients and those with idiopathic erythrocytosis or thrombocytosis. This highlights the potential of cytokines as biomarkers in hematologic disorders.

Value of constructing a non-invasive diagnostic model based on serum heme oxygenase-1 and glucose regulatory protein 78 for non-alcoholic fatty liver disease

Objective: To analyze the clinical application value of serum heme oxygenase (HO)-1expression level in non-alcoholic fatty liver disease (NAFLD) and, based on that, establish a diagnostic model combined with glucose regulatory protein 78 (GRP78) so as to clarify its diagnostic effectiveness and application value. Methods: A total of 210 NAFLD patients diagnosed by abdominal B-ultrasound and liver elastography were included, and at the same time, 170 healthy controls were enrolled. The general clinical data, peripheral blood cell counts, and biochemical indicators of the research subjects were collected. The expression levels of HO-1 and GRP78 were detected using an enzyme-linked immunosorbent assay. Multivariate analysis was used to screen independent risk factors for NAFLD. Visual output was performed through nomogram diagrams, and the diagnostic model was constructed. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the diagnostic effectiveness of NAFLD. Measurement data were analyzed using a t-test or Mann-Whitney U rank sum test to detect data differences between groups. Enumeration data were analyzed using the Fisher's exact probability test or the Pearson χ(2) test. Results: Compared with the healthy control group, the white blood cell count, aspartate aminotransferase (AST), alanine aminotransferase, gamma-glutamyl transferase (GTT), fasting blood glucose (Glu), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), serum HO-1, and GRP78 levels were significantly increased in the NAFLD group patients (P < 0.05). Binary logistic analysis results showed that AST, TG, LDL-C, serum HO-1, and GRP78 were independent risk factors for NAFLD (P < 0.05). A nomogram clinical predictive model HGATL was established using HO-1 (H), GRP78 (G) combined with AST (A), TG (T), and LDL-C (L), with the formula P=-21.469+3.621×HO-1+0.116 ×GRP78+0.674×AST+6.250×TG+4.122 ×LDL-C. The results confirmed that the area under the ROC curve of the HGATL model was 0.965 8, with an optimal cutoff value of 81.69, a sensitivity of 87.06%, a specificity of 92.82%, a P < 0.05, and the diagnostic effectiveness significantly higher than that of a single indicator. The calibration curve and DCA both showed that the model had good diagnostic performance. Conclusion: The HGATL model can be used as a novel, non-invasive diagnosis model for NAFLD and has a positive application value in NAFLD diagnosis and therapeutic effect evaluation. Therefore, it should be explored and promoted in clinical applications.

Progress in the diagnosis and treatment of graft fibrosis after liver transplantation

AbstractLiver transplantation (LT) is considered one of the best treatments for patients with end‐stage liver diseases. However, some patients with no significant clinical manifestations or abnormal laboratory tests still experience graft fibrosis during postoperative follow‐up, which is often recognized by graft histopathology. Graft fibrosis can lead to graft dysfunction, thereby reducing the survival time of the recipient and even requiring re‐transplantation. Currently, noninvasive methods are widely applied in the assessment of hepatic and allograft fibrosis. Although both noninvasive diagnostic models based on laboratory examination indicators and elastography technology that can quantify liver stiffness have some value in the evaluation of fibrosis, the diagnostic accuracy and characteristics of these various methods vary and cannot replace liver biopsy completely. In recent years, some liver‐protective drugs and proprietary Chinese traditional medicines have been proven to delay or reverse chronic liver fibrosis. Nevertheless, their efficacy and safety for LT recipients need to be further verified. This article reviews the diagnosis and treatment of graft fibrosis after LT to provide a reference for improving the overall survival rate of LT recipients.

Gut microbiome-based noninvasive diagnostic model to predict acute coronary syndromes.

Previous studies have shown that alterations in the gut microbiota are closely associated with Acute Coronary Syndrome (ACS) development. However, the value of gut microbiota for early diagnosis of ACS remains understudied. We recruited 66 volunteers, including 29 patients with a first diagnosis of ACS and 37 healthy volunteers during the same period, collected their fecal samples, and sequenced the V4 region of the 16S rRNA gene. Functional prediction of the microbiota was performed using PICRUSt2. Subsequently, we constructed a nomogram and corresponding webpage based on microbial markers to assist in the diagnosis of ACS. The diagnostic performance and usefulness of the model were analyzed using boostrap internal validation, calibration curves, and decision curve analysis (DCA). Compared to that of healthy controls, the diversity and composition of microbial community of patients with ACS was markedly abnormal. Potentially pathogenic genera such as Streptococcus and Acinetobacter were significantly increased in the ACS group, whereas certain SCFA-producing genera such as Blautia and Agathobacter were depleted. In addition, in the correlation analysis with clinical indicators, the microbiota was observed to be associated with the level of inflammation and severity of coronary atherosclerosis. Finally, a diagnostic model for ACS based on gut microbiota and clinical variables was developed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.963 (95% CI: 0.925-1) and an AUC value of 0.948 (95% CI: 0.549-0.641) for bootstrap internal validation. The calibration curves of the model show good consistency between the actual and predicted probabilities. The DCA showed that the model had a high net clinical benefit for clinical applications. Our study is the first to characterize the composition and function of the gut microbiota in patients with ACS and healthy populations in Southwest China and demonstrates the potential effect of the microbiota as a non-invasive marker for the early diagnosis of ACS.

Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images.

To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images. Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD. A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set. The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.

1248P Development of a high performance and noninvasive diagnostic model using blood cell-free microRNAs for multi-cancer early detection

1248P Development of a high performance and noninvasive diagnostic model using blood cell-free microRNAs for multi-cancer early detection

Validation and comparison of diabetic retinopathy-based diagnostic models for diabetic nephropathy

To validate and compare the efficacy of two noninvasive diagnostic models for diabetic nephropathy (DN) based on diabetic retinopathy (DR). A total of 565 patients with type 2 diabetes undergoing kidney biopsy in the Department of Nephrology, PLA General Hospital from January, 1993 to December, 2014 were studied. The patients were divided into DN group and non-diabetic nephropathy (NDRD) group according to renal pathological diagnosis. The data from the 22-year period were divided into 3 stages based on chronological order: early stage (from 1993 to 2003), middle stage (from 2004 to April, 2012), and late stage (from May, 2012 to December, 2014). The changes in clinical features and pathological diagnosis of the patients with renal biopsy over the 22 years were analyzed. The published DNT model and JDB model, both based on DR, were validated and compared for diagnostic effectiveness of DN, and the characteristics of the misdiagnosed cases were analyzed. The incidences of hypertension and DR and levels of glycosylated hemoglobin (HbA1c), creatinine and 24-h urinary protein were all significantly higher, while hemoglobin and triglyceride levels were lower in DN group than in NDRD group (P<0.05). The proportion of NDRD cases increased gradually over time, with IgA nephropathy and membranous nephropathy as the main pathological types. The AUC of JDB model was 0.946, similar to that of NDT model (0.925; P=0.198). The disease course of diabetes, hematuria and incidence of DR were important clinical features affecting the diagnostic accuracy of the models. The clinical features and pathological diagnosis of DR change over time. The non-invasive diagnostic models based on DR have good diagnostic efficacy for DN.

Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States.

Liver fibrosis is closely related to abnormal liver function and liver cancer. Accurate noninvasive assessment of liver fibrosis is of great significance for preventing disease progression and treatment decisions. The purpose of this study was to develop and validate a non-invasive predictive model for the asses`sment of significant fibrosis in patients with non-alcoholic fatty liver disease. Information on all participants for 2017-2018 was extracted from the NHANES database. The eligible patients with significant fibrosis (n=123) and non-significant fibrosis (n=898) were selected to form the original dataset. Variable selection was performed using least absolute shrinkage and selection operator (Lasso) regression, and multivariate logistic regression analysis was used to develop a prediction model. The utility of the model is assessed in terms of its discrimination, calibration and clinical usability. Bootstrap-resampling internal validation was used to measure the accuracy of the prediction model. This study established a new model consisting of 9 common clinical indicators and developed an online calculator to show the model. Compared with the previously proposed liver fibrosis scoring system, this model showed the best discrimination and predictive performance in the training cohort (0.812,95%CI 0.769-0.855) and the validation cohort (0.805,95%CI 0.762-0.847), with the highest area under curve. Specificity(0.823), sensitivity(0.699), positive likelihood ratio(3.949) and negative likelihood ratio(0.366) were equally excellent. The calibration plot of the predicted probability and the actual occurrence probability of significant fibrosis shows excellent consistency, indicating that the model calibration is outstanding. Combined with decision curve analysis, this model has a great benefit in the range of 0.1-0.8 threshold probability, and has a good application value for the diagnosis of clinical significant fibrosis. This study proposes a new non-invasive diagnostic model that combines clinical indicators to provide an accurate and convenient individualized diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease.

Refinement and validation of a comprehensive clinical diagnostic model (GAMAD) based on gender, age, multitarget circulating tumour DNA methylation signature and commonly used serological biomarkers for early detection of hepatocellular carcinoma: a multicentre, prospective observational study protocol

IntroductionPrompt detection of hepatocellular carcinoma (HCC) in patients with chronic liver diseases is critical for enhancing prognosis. Existing imaging techniques and serum markers fall short of clinical needs. This study...

New Diagnostic and Prognostic Models for the Development of Alcoholic Cirrhosis Based on Genetic Predisposition and Alcohol History.

Liver cirrhosis development is a multifactorial process resulting from a combination of environmental and genetic factors. The aim of the study was to develop accurate non-invasive diagnostic and prognostic models for alcoholic cirrhosis. Consecutive subjects with at-risk alcohol intake were retrospectively enrolled (110 cirrhotic patients and 411 non-cirrhotics). At enrollment, the data about lifetime drinking history were collected and all patients were tested for Patatin-like phospholipase domain-containing protein 3 (PNPLA3) rs738409, Transmembrane 6 Superfamily 2 (TM6SF2) rs58542926, and hydroxysteroid 17-beta dehydrogenase 13 (HSD17B13) rs72613567 variants. In cross-sectional analyses, models for the diagnosis of cirrhosis were developed using multivariate logistic regression. A predictive score for cirrhosis development over 24 years was built by evaluating time-dependent AUC curves. The best diagnostic accuracy was demonstrated by the model, which also includes daily alcohol consumption, duration of hazardous alcohol use, and genetic variants, with AUCs of 0.951 (95% CI 0.925-0.977) and 0.887 (95% CI 0.925-0.977) for cirrhosis and compensated cirrhosis, respectively. The predictive model for future cirrhosis development (AUC of 0.836 95% CI: 0.769-0.904) accounted for age at onset of at-risk alcohol consumption and the number of PNPLA3 and HSD17B13 variant alleles. We have developed accurate genetic and alcohol consumption models for the diagnosis of alcoholic cirrhosis and the prediction of its future risk.

Association of Non-Invasive Markers with Significant Fibrosis in Patients with Nonalcoholic Fatty Liver Disease: A Cross-Sectional Study.

The identification of significant fibrosis is critical for predicting the prognosis of non-alcoholic fatty liver disease (NAFLD). This study aimed to compare the predictive value of chitinase-3-like protein 1 (CHl3L1) and other non-invasive biomarkers, as well as to establish a novel non-invasive diagnostic model for assessing the risk of significant fibrosis in NAFLD. A total of 71 patients with confirmed NAFLD based on liver biopsy were included in this study. Serum CHI3L1 levels and other non-invasive fibrosis assessment measures were determined. The aspartate aminotransferase-to-platelet ratio index (APRI) and Fibrosis-4 Index (FIB-4) were calculated to assess the diagnostic superiority of serum CHI3L1 compared to other non-invasive fibrosis assessment measures. Multivariate logistic regression analysis was conducted to identify relevant variables for constructing a diagnostic model. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of each index, including the area under ROC curve (AUC), sensitivity, and specificity. A nomogram was established based on the logistic regression model. Serum CHI3LI levels were found to be higher in NAFLD patients with significant fibrosis compared to those without significant fibrosis. Multivariate logistic regression analysis revealed that aspartate aminotransferase (AST), type IV collagen (IV-C), CHI3L1, and liver stiffness measurement (LSM) were identified as potential independent risk factors associated with significant fibrosis in patients. The AUC of CHI3L1 for diagnosing significant liver fibrosis was 0.716 (0.596,0.836), with the optimal cut-off point of 125.315. The nomogram incorporating CHI3LI, AST, IV-C, and LSM further improved the potential predictive value, with an AUC for diagnosing significant fibrosis of 0.864 (0.766,0.962). This was superior to IV-C, CHI3L1, LSM, and APRI (all p < 0.05). The diagnostic model constructed by CHI3L1 combined with the existing non-invasive markers AST, IV-C, and LSM can help assess the risk of significant liver fibrosis in NAFLD.

P-326 Noninvasive diagnostic models based on CT scans for differentiating solitary pulmonary metastasis in colorectal cancer patients by artificial intelligence: A multicenter study

P-326 Noninvasive diagnostic models based on CT scans for differentiating solitary pulmonary metastasis in colorectal cancer patients by artificial intelligence: A multicenter study