Non-invasive Diagnostic Model Research Articles

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.

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.

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.

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

Treatment of Liver Fibrosis after Hepatitis B with TCM Combined with NAs Evaluated by Noninvasive Diagnostic Methods: A Retrospective Study.

Objective. Chronic hepatitis B liver fibrosis is an important intermediate link in the development of liver cirrhosis. A retrospective cohort study was conducted in Longhua Hospital affiliated to the Shanghai University of Traditional Chinese Medicine in order to prove whether integrated traditional Chinese and Western medicine could improve the incidence of CHB complications and clinical prognosis. There are 130 patients with hepatitis B liver fibrosis (being treated from 2011-2021) included in the study, and the patients were divided into 64 TCM users (NAs combined with TCM) and 66 TCM nonusers (NAs antiviral therapy). The serum noninvasive diagnostic model (APRI, FIB-4) and LSM value were used to classify the stages of fibrosis. The results showed that the LSM value was decreased significantly in TCM users compared with TCM nonusers (40.63% versus 28.79%). Indicators of FIB-4 and APRI of TCM users have improved significantly compared with that of TCM nonusers (32.81% versus 10.61% and 35.94% versus 24.24%). The AST, TBIL, and HBsAg levels in TCM users were lower than those in TCM nonusers, and the HBsAg level was inversely correlated with the CD3+, CD4+, and CD8+ in TCM users. The PLT and spleen thickness of TCM users also were improved considerably. The incidence rate of end-point events (decompensated cirrhosis/liver cancer) in TCM nonusers was higher than that of TCM users (16.67% versus 1.56%). The long course of the disease and a family history of hepatitis B were the risk factors for disease progression, and long-term oral administration of TCM was the protective factor. As a result, the serum noninvasive fibrosis index and imaging parameters in TCM users were lower than those of TCM nonusers. Patients in the treatment of NAs combined with TCM had better prognoses such as a lower HBsAg level, a more stable lymphocyte function, and a lower incidence of end-point events. The present findings suggest the effect of TCM combined with NAs in the treatment of chronic hepatitis B liver fibrosis is better than that of single drug treatment.

Developing a non-invasive diagnostic model for pediatric Crohn's disease using RNA-seq analysis.

Introduction: Pediatric Crohn's disease is a chronic inflammatory condition that affects the digestive system in children and adolescents. It is characterized by symptoms such as abdominal pain, diarrhea, weight loss, and malnutrition, and can also cause complications like growth delays and delayed puberty. However, diagnosing pediatric Crohn's disease can be difficult, especially when it comes to non-invasive methods. Methods: In this study, we developed a diagnostic model using RNA-seq to analyze gene expression in ileal biopsy samples from children with Crohn's disease and non-pediatric Crohn's controls. Results: Our results showed that pediatric Crohn's disease is associated with altered expression of genes involved in immune response, inflammation, and tissue repair. We validated our findings using two independent datasets from the Gene Expression Omnibus (GEO) database, as well as through one prospective independent dataset, and found that our model had a high accuracy rate. Discussion: These findings suggest the possibility of non-invasive diagnosis for pediatric Crohn's disease and may inform the development of targeted therapies for this condition.

A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers.

To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC)preoperatively. A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort. We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.

Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors

BackgroundGastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors.MethodsThis retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models’ performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation.ResultsThis study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer.ConclusionsThe results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.

Clinical value of thyroid related hormones combined with neutrophil to lymphocyte ratio in patients with nonalcoholic fatty liver disease

In sufferers with nonalcoholic fatty liver disease (NAFLD), the differences of thyroid associated hormones and neutrophil to lymphocyte ratio (NLR) in different liver pathological groups have been compared. Patients with NAFLD diagnosed by liver biopsy in our hospital from July 2012 to February 2019 were selected. All subjects were divided into nonalcoholic steatohepatitis (NASH) team and non-NASH group, no/mild fibrosis group (F0-1) and significant fibrosis group (F2-4). The differences of thyroid related hormones and NLR in these groups were in contrast, respectively. For the TSH, we conducted further evaluation based on gender. The TSH and NLR in NASH patients were significantly higher than non-NASH patients, but there was no considerable difference in free triiodothyronine (FT3) and free thyroxine (FT4) between the 2 groups. In the gender-based subgroup analysis, the variations of TSH between the 2 groups were nonetheless statistically significant (P < .05). The TSH and NLR in the significant fibrosis group were higher than these in the non/mild liver fibrosis group, and the differences were statistically significant (P < .05), but there was no large difference in FT3 and FT4 between the 2 groups (P > .05). In addition, in the gender-based subgroup analysis and further multivariable analysis, the variations of TSH between the 2 groups were still statistically significant (P < .05). In this study, we found that serum thyroid stimulating hormone (TSH) and neutrophil to lymphocyte ratio (NLR) were closely associated to the severity of NAFLD, suggesting that this simple available laboratory index may additionally be incorporated into the future noninvasive diagnostic scoring model to predict the incidence of NASH and the degree of fibrosis.