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Objective:To investigate the diagnostic accuracy of serological indicators and evaluate the diagnostic value of a new established combined serological model on identifying the minimal hepatic encephalopathy (MHE) in patients with compensated cirrhosis.Methods:This prospective multicenter study enrolled 263 compensated cirrhotic patients from 23 hospitals in 15 provinces, autonomous regions and municipalities of China between October 2021 and August 2022. Clinical data and laboratory test results were collected, and the model for end-stage liver disease (MELD) score was calculated. Ammonia level was corrected to the upper limit of normal (AMM-ULN) by the baseline blood ammonia measurements/upper limit of the normal reference value. MHE was diagnosed by combined abnormal number connection test-A and abnormal digit symbol test as suggested by Guidelines on the management of hepatic encephalopathy in cirrhosis. The patients were randomly divided (7∶3) into training set ( n=185) and validation set ( n=78) based on caret package of R language. Logistic regression was used to establish a combined model of MHE diagnosis. The diagnostic performance was evaluated by the area under the curve (AUC) of receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve. The internal verification was carried out by the Bootstrap method ( n=200). AUC comparisons were achieved using the Delong test. Results:In the training set, prevalence of MHE was 37.8% (70/185). There were statistically significant differences in AMM-ULN, albumin, platelet, alkaline phosphatase, international normalized ratio, MELD score and education between non-MHE group and MHE group (all P<0.05). Multivariate Logistic regression analysis showed that AMM-ULN [odds ratio ( OR)=1.78, 95% confidence interval ( CI) 1.05-3.14, P=0.038] and MELD score ( OR=1.11, 95% CI 1.04-1.20, P=0.002) were independent risk factors for MHE, and the AUC for predicting MHE were 0.663, 0.625, respectively. Compared with the use of blood AMM-ULN and MELD score alone, the AUC of the combined model of AMM-ULN, MELD score and education exhibited better predictive performance in determining the presence of MHE was 0.755, the specificity and sensitivity was 85.2% and 55.7%, respectively. Hosmer-Lemeshow test and calibration curve showed that the model had good calibration ( P=0.733). The AUC for internal validation of the combined model for diagnosing MHE was 0.752. In the validation set, the AUC of the combined model for diagnosing MHE was 0.794, and Hosmer-Lemeshow test showed good calibration ( P=0.841). Conclusion:Use of the combined model including AMM-ULN, MELD score and education could improve the predictive efficiency of MHE among patients with compensated cirrhosis.
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Objective:To investigate the clinical characteristics of patients with acute aortic dissection (AAD) through a retrospective and observational study, and to construct an early warning model of AAD that could be used in the emergency room.Methods:The data of 11 583 patients in the Emergency Chest Pain Center from January to December 2019 were retrospectively collected from the Chest Pain Database of Zhongshan Hospital Affiliated to Fudan University. Inclusion criteria: patients with chest pain who attended the Emergency Chest Pain Center between January and December 2019. Exclusion criteria were 1) younger than 18 years, 2) no chest/back pain, 3) patients with incomplete clinical information, and 4) patients with a previous definite diagnosis of aortic dissection who had or had not undergone surgery. The clinical data of 9668 patients with acute chest/back pain were finally collected, excluding 53 patients with previous definite diagnosis of AAD and/or without surgical aortic dissection. A total of 9 615 patients were enrolled as the modeling cohort for early diagnosis of AAD. The patients were divided into the AAD group and non-AAD group according to whether AAD was diagnosed. Risk factors were screened by univariate and multivariate logistic regression, the best fitting model was selected for inclusion in the study, and the early warning model was constructed and visualized based on the nomogram function in R software. The model performance was evaluated by accuracy, specificity, sensitivity, positive likelihood ratio and negative likelihood ratio. The model was validated by a validation cohort of 4808 patients who met the inclusion/exclusion criteria from January 2020 to June 2020 in the Emergency Chest Pain Center of the hospital. The effect of early diagnosis and early warning model was evaluated by calibration curve.Results:After multivariate analysis, the risk factors for AAD were male sex ( OR=0.241, P<0.001), cutting/tear-like pain ( OR=38.309, P<0.001), hypertension ( OR=1.943, P=0.007), high-risk medical history ( OR=12.773, P<0.001), high-risk signs ( OR=7.383, P=0.007), and the first D-dimer value ( OR=1.165, P<0.001), Protective factors include diabetes( OR=0.329, P=0.027) and coronary heart disease ( OR=0.121, P<0.001). The area under the ROC curve (AUC) of the early diagnosis and warning model constructed by combining the risk factors was 0.939(95 CI:0.909-0.969). Preliminary validation results showed that the AUC of the early diagnosis and warning model was 0.910(95 CI:0.870-0.949). Conclusions:Sex, cutting/tear-like pain, hypertension, high-risk medical history, high-risk signs, and first D-dimer value are independent risk factors for early diagnosis of AAD. The model constructed by these risk factors has a good effect on the early diagnosis and warning of AAD, which is helpful for the early clinical identification of AAD patients.
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Objective@#To explore the value of an oral squamous cell carcinoma (OSCC) diagnostic model constructed by using principal component analysis (PCA) to analyze a database of differentially expressed genes in OSCC and to provide a reference for clinical diagnosis and treatment.@*Methods@# RNA-seq expression data of OSCC and normal control samples were obtained from The Cancer Genome Atlas (TCGA) database, and then, normalized and differentially expressed genes (DEGs) were identified by R software. DEGs were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to identify their main biological characteristics. 70% of DEGs expression data in RNA-seq were randomly selected as the training set and 30% were selected as the test set. Then, the PCA method was applied to analyze the training set data and extract the principal components (PCs) related to the diagnosis of OSCC in order to construct a PCA model. Then, the receiver operating characteristic (ROC) curves of PCA models in the training set and the test set were respectively drawn, and the area under curve (AUC) was calculated to evaluate the accuracy of the PCA model in the diagnosis of OSCC.@*Results@#RNA-seq expression data of OSCC and normal control samples obtained from TCGA database included 330 samples and 32 samples, respectively. Using false discovery rate (FDR) <0.001 and |log2 fold change| (|log2FC|) >4 as the thresholds, a total of 159 downregulated and 248 upregulated DEGs were identified, which were mainly enriched in cellular components such as intermediate fiber and melanosomal membrane, pigment and salivation-related biological processes and mainly involved in salivary secretion and tyrosine metabolism pathways (P.adjust<0.05 and Q<0.05). The DEGs were proposed as tumor markers for OSCC, and PCA analysis of the training set showed that the cumulative ratio of variance of PC1, PC2 and PC3: [including submaxillary gland androgen regulated protein 3B (SMR3B), proline rich 27 (PRR27), histatin 3 (HTN3), statherin (STATH), cystatin D (CST5), BPI fold containing family A member 2 (BPIFA2), proline rich protein Hae Ⅲ subfamily 2 (PRH2), keratin 35(KRT35), histatin 1 (HTN1), amylase alpha 1B (AMY1B)] were 0.873, 0.100 and 0.023, respectively, and the total weight of the three was 0.996. The PCA diagnostic model of OSCC was further constructed by combining the eigenvectors of the above three components. The ROC curves of the training set and test set showed that the AUC values of the PCA model were 0.852 and 0.844, respectively, which were higher than those of other single genes.@*Conclusion @#The OSCC diagnostic model based on the expression levels of SMR3B, PRR27, HTN3, STATH, CST5, BPIFA2, PRH2, KRT35, HTN1 and AMY1B constructed with the PCA method and DEGs has a high diagnostic advantage. This study provides a theoretical basis for the early genetic diagnosis of OSCC and the application of the PCA model in clinical diagnosis.
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Objective:To analyze the liver pathological characteristics of chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) and negative hepatitis B e antigen (HBeAg), and to evaluate the diagnostic value of different serological models for liver fibrosis.Methods:Retrospective analysis was conducted on the patients with HBeAg-negative CHB who had normal ALT and underwent liver biopsy from August 2016 to December 2019 in the Department of Infectious Diseases, Henan Provincial People′s Hospital. The clinical data, serum indicators of hepatitis B virus (HBV) and HBV DNA were collected. The liver fibrosis stages (S) was assessed by pathological examination. The diagnostic efficacies of gamma-glutamyl transpeptidase to platelet ratio (GPR), fibrosis 4 score (FIB-4), S index, aspartate aminotransferase to platelet ratio index (APRI) and gamma-glutamyl transpeptidase to albumin ratio (γ-GT/ALB) for liver pathological fibrosis were analyzed by the receiver operating characteristic curves. Two variable correlation test was used to explore the relationship between the different models and pathological fibrosis of liver tissue. Chi-square test was used for statistical comparison.Results:The age of 448 patients was (37.98±9.82) years, and the male to female ratio was 1.286 ∶1. The proportions of S≥2 in patients with age>30 years, hepatitis B surface antigen (HBsAg)<2 000 IU/mL and HBV DNA≥2 000 IU/mL were higher than those in patients with age ≤30 years, HBsAg ≥2 000 IU/mL and HBV DNA<2 000 IU/mL, respectively, and the differences were all statistically significant ( χ2=7.68, P=0.006; χ2=11.44, P=0.001; χ2=9.12, P=0.003, respectively). There were 250 cases with pathological fibrosis stage S<2, 162 cases with S=2 and 36 cases with S≥3. FIB-4 (correlation coefficient 0.250), APRI (correlation coefficient 0.218), GPR (correlation coefficient 0.186), S index (correlation coefficient 0.184) and γ-GT/ALB (correlation coefficient 0.127) were positively correlated with the severity of liver fibrosis (all P<0.050). S index had the highest sensitivity (64.1%) in the diagnosis of significant liver fibrosis (S≥2), while γ-GT/ALB had the highest specificity (80.8%). In the diagnosis of severe liver fibrosis (S≥3), γ-GT/ALB had the highest sensitivity (77.8%), while APRI had the highest specificity (78.6%). Conclusions:The incidence of liver fibrosis in CHB patients with normal ALT and negative HBeAg is relatively high. The current serological diagnostic models are not suitable for the evaluation of liver fibrosis in these patients, and timely liver puncture is still necessary.
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【Objective】 To evaluate the performance of SPINK1/SPP1 in diagnosis of hepatocellular carcinoma (HCC) alone or in combination. 【Methods】 A total of 419 serum samples were collected and divided into four groups: normal control (n=93), chronic hepatitis B (CHB) (n=72), HBC related liver cirrhosis (LC) (n=77), and hepatocellular carcinoma (HCC) (n=177). Serum concentrations of SPINK1 and SPP1 were determined by ELISA kits. All parameters were first analyzed by significance tests among the groups. To distinguish tumors from non-tumors, a combination model was generated by multivariable binary Logistic regression using a comprehensive control group which consisted of the normal, the CHB and the LC groups. The performance of each indicator was judged by comparison of AUC, sensitivity, specificity and accuracy. 【Results】 The serum levels of SPINK1 and SPP1 were both significantly higher in HCC group than in all the others (P0.05), was lower than that of the combination model (P=0.042 9 and P0.05). 【Conclusion】 The data identified SPINK1 and SPP1 as novel tumor biomarkers with greater robust efficiency than the currently used AFP for detection of hepatocellular carcinoma, alone or in combination.
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Objective:To analyze the MRI findings of solid pseudopapilloma of the pancreas (SPTs) and nonfunctional pancreatic neuroendocrine tumors (PNETs), and to establish and verify the prediction model of SPTs and PNETs.Methods:The clinical and MRI data of 142 patients with SPTs and 137 patients with PNETs who underwent surgical resection and were confirmed by pathology in the First Affiliated Hospital of Naval Medical University from January 2013 to December 2020 were collected continuously. Age, gender, body mass index (BMI), lesion size, location, shape, boundary, cystic change, T 1WI signal, T 2WI signal, enhancement peak phase, whether the enhancement degree was higher than that of pancreatic parenchyma in the enhancement peak phase, enhancement pattern, whether pancreatic duct and common bile duct were dilated, whether the pancreas shrank, and whether it invaded adjacent organs and vessels were recorded. According to the international consensus on prediction model modeling, patients were divided into training set (106 SPTs and 100 PNETs between January 2013 and December 2018), and validation set (36 SPTs and 37 PNETs between January 2019 and December 2020). The above characteristics of patients in training and validation set were analyzed by univariate and multivariate logistic regression, and a prediction model was established to distinguish SPTs and PNETs, and then visualized as a nomogram. The receiver operating characteristic curve (ROC) of the nomogram of training set and verification set was drawn, and the area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the prediction efficiency of the model, and the clinical application value of the prediction model was evaluated by decision curve analysis (DCA). Results:Univariate regression analysis showed that there were significant differences on age, gender, lesion size, shape, cystic change, T 1WI signal, peak phase of enhancement, degree of enhancement in peak phase, pattern of enhancement and invasion of adjacent organs between SPTs group and PNETs group (all P value <0.05). Multivariate regression analysis showed that the older age, male patients, the smaller lesion, no high signal on T 1WI, the enhancement peak phase located in arterial phase or venous phase, and the enhancement degree in peak phase higher than that of pancreatic parenchyma were the six independent predictors of PNETs. The prediction model was established by using these six factors and visualized as a nomogram. The formula for predicting PNETs probability was 4.31+ 1.13×age+ 1.31×tumor size-1.29×female-4.18×high T 1WI signal+ 1.28×the enhancement degree higher than that of pancreatic parenchyma -4.69 ×enhancement peak in delay phase. The prediction model was visualized as a nomogram. The AUC values in the training set and validation set were 0.99(95% CI0.977-1.000) and 0.97 (95% CI 0.926-1.000), respectively. The sensitivity, specificity and accuracy in the training set are 98.00%, 94.34% and 96.12% and in the validation set were 86.49%, 97.22% and 91.78% respectively. The results of decision curve analysis show that the prediction model can accurately diagnose SPTs and PNETs. Conclusions:The prediction model established in this study can accurately differentiate SPTs from PNETs, and can provide important information for clinical decision and prognosis.
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Objective:To analyze the clinical features of latent autoimmune diabetes (LADA) in adults among newly diagnosed type 2 diabetes mellitus (T2DM), and to explore whether LADA diagnostic models can be established based on this.Methods:From May 2016 to January 2017, 302 patients with newly diagnosed T2DM in the outpatient and inpatient department of metabolism and endocrinology of Yueyang Central Hospital were analyzed. All of them were tested for glutamic acid decarboxylase antibody (GADA). According to the consensus of the Chinese Medical Association Diabetes Association (CDS) LADA diagnosis and treatment, they were divided into LADA group (18 cases) and T2DM group (284 cases). The general clinical data and clinical biochemical indexes of the two groups were analyzed; Multiple linear regression method was used to evaluate the feasibility of establishing LADA diagnostic model.Results:⑴ Compared with patients in the T2DM group, the patients in the LADA group had a younger age of onset, and " three more and one less" symptoms were more common ( P<0.05); the weight, body mass index (BMI), waist circumference, waist-to-hip ratio (WHR), triglycerides (TG), fasting C peptide (FCP), postprandial 2 h C peptide (2 h-CP), modified islet function index HOMA-islet (CP-DM), and modified insulin resistance index HOMA-IR (CP) in the LADA group were all lower, while high-density lipoprotein cholesterol (HDL-C) and HbA1c were higher ( P<0.05). ⑵ the linear regression method was used to analyze the multicollinearity of patients in LADA group and T2DM group. The biochemical indexes with statistically significant difference were selected as independent variables through correlation analysis, and the GADA value was used as dependent variable. The statistical results showed that the independent variables could not fully meet the conditions of multicollinearity regression analysis. Conclusions:⑴ Related clinical features and glucose metabolism indicators have differential diagnosis significance for LADA, but this study cannot be used for multiple linear regression analysis, and it is difficult to establish a diagnostic model for LADA. ⑵ LADA diagnosis is a comprehensive diagnosis, which should be combined with the results of islet autoantibody and clinical features.
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Objective: To compare the qualitative diagnostic value of different Methods: based on PET/CT for solitary pulmonary nodules (SPN). Methods: Data of 161 SPN patients who underwent PET/CT were collected. Clinical information, high resolution CT (HRCT) signs and maximum standard uptake value (SUVmax) were compared between benign and malignant SPN patients. The mathematical diagnostic model of SPN was constructed by using binary Logistic regression, and the diagnostic efficiencies were compared among diagnostic model, PET/CT and HRCT. Results: Among 161 patients, malignant SPN were pathologically diagnosed in 131 cases and benign in 30 cases. The sensitivity, specificity and accuracy of PET/CT in diagnosing malignant SPN was 98.47% (129/131), 76.67% (23/30) and 94.41% (152/161), of HRCT was 59.54% (78/131), 83.33% (25/30) and 63.98%(103/161), respectively. After univariate and multivariate analysis, SUVmax, patient's age, calcification and tracheal vascular bundles were incorporated into the regression equation, and a model was then established. The sensitivity, specificity and accuracy of the model for diagnosing malignant SPN was 82.44% (108/131), 86.67% (26/30) and 83.23% (134/161), respectively. The AUC of the model, PET/CT and HRCT for diagnosis of malignant SPN was 0.909, 0.876 and 0.714, respectively. The AUC of the model and PET/CT were both higher than HRCT (all P<0.001). There was no significant difference between the model and PET/CT (P=0.468). Conclusion: PET/CT-based Logistic regression model and PET/CT are better than HRCT in qualitative diagnosis of SPN, and the specificity of the model is higher than that of PET/CT.
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@#Objective To investigate the characteristic volatile organic compounds (VOCs) in exhaled breath and their diagnostic value in patients with early stage lung cancer. Methods Solid-phase micro-extraction combined with gas chromatography mass spectrometry was used to analyze exhaled breath VOCs of 117 patients with early stage lung cancer (54 males and 63 females, with an average age of 61.9±6.8 years) and 130 healthy subjects (79 males and 51 females, with an average age of 63.3±6.6 years. The characteristic VOCs of early stage lung cancer were identified, and a diagnostic model was established. Results Ten characteristic VOCs of early stage lung cancer were identified, including acetic acid, n-butanol, dimethylsilanol, toluene, 2,3,4-trimethylheptane, 3,4-dimethylbenzoic acid, 5-methyl-3-hexene-2-ketone, n-hexanol, methyl 2-oxoglutarate and 4-methoxyphenol. Gender and the 10 characteristic VOCs were included in the diagnostic model, with a sensitivity of 83.8% and a specificity of 96.2%. Conclusion Analysis of exhaled breath VOCs is expected to be one of the potential methods used for early stage lung cancer diagnosis.
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Objective@#To establish and evaluate diagnostic efficacy and applicability of serum Golgi protein (GP) 73 based non-invasive diagnostic model with other conventional serological indicators for compensated stage hepatitis B cirrhosis.@*Methods@#666 cases with chronic hepatitis B (CHB) who had visited to the Fifth Medical Center of People’s Liberation Army General Hospital from January 2010 to December 2017 were selected as the study subjects, and were classified according to compensated stage cirrhosis into clinical and pathological diagnosis group based on whether or not the liver histological examination was performed. A diagnostic model of compensated stage hepatitis B cirrhosis in the clinical diagnosis group was established. The current clinically used diagnostic model of liver cirrhosis, aspartate aminotransferase/platelet ratio index (APRI), fibrosis index (FIB)-4 and liver stiffness measurement (LSM) were compared. Eventually, the diagnostic model was verified step by step by pathological diagnosis group.@*Results@#The area under the receiver operating characteristic curve (AUC) of GP73 and APRI, FIB-4, and LSM for cirrhosis patients in the clinical diagnosis group were 0.842, 0.857, 0.864, and 0.832, respectively. The diagnostic efficiency of the four indicators were of similar (P value > 0.05). A diagnostic model of compensated stage hepatitis B cirrhosis (GAPA) using logistic regression analysis was established: LogitP = 1/ [1 + exp (1.614-0.054 × GP73-0.045 × Age + 0.030 × PLT-0.015 × ALP)]. The AUC of the model was as high as 0.940 and the optimal cut-off value were 0.41. The corresponding diagnostic sensitivity and specificity were 0.92 and 0.82, respectively. The diagnostic efficiency was better than that of APRI, FIB-4, LSM and GP73 alone (P < 0.05). The AUC of GAPA was 0.877 in the pathological diagnosis group, which was similar to the diagnostic efficacy of LSM (0.891) and FIB-4 (0.847) (P > 0.1), but still superior to that of APRI (0.811) and GP73 alone (0.780) (P < 0.001).@*Conclusion@#GAPA, a diagnostic model for compensated stage hepatitis B cirrhosis established in this study, has a good diagnostic efficacy in both the clinical and pathological diagnosis group, and has certain auxiliary diagnostic value in the areas where resources are relatively scarce or where LSM has not been developed.
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Individual dental age is used as an index of chronological age estimation and is an important indicator of the child's growth stage. Dental age does change greatly over time, but it changes constantly. And updating information about this change is important. The purpose of this study was to provide information about tooth eruption stage using diagnostic model analysis and to investigate tooth eruption sequence and estimate chronological age based on this information.Tooth eruption stages were measured on a diagnostic model from 488 patients in 5 – 13 year old children. Based on the information on eruption stage, eruption sequence in maxilla was first permanent molar, central incisor, lateral incisor, first premolar, canine, second premolar and second permanent molar. Eruption sequence in mandible was first permanent molar, central incisor, lateral incisor, canine, first premolar, second premolar and second permanent molar. There were significant differences between males and females in the eruption stage of canine, first and second premolar, and second molar at several ages. The chronological age of male and female was estimated by the coefficient of determination of 0.816, 0.826 respectively.
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Child , Female , Humans , Male , Bicuspid , Incisor , Mandible , Maxilla , Molar , Tooth Eruption , ToothABSTRACT
Objective To use the liquid protein combined with MALDI-TOF-MS for screening the serum differential peptides markers in lung adenocarcinoma patients and to establish the lung adenocarcinoma diag-nosed prediction model for founding the potential markers for the diagnosis of lung adenocarcinoma.Methods 37 patients with lung adenocarcinoma and 33 healthy subjects and benign lung disease which were made up in control group were collected,in the two groups the age and the sex were matched.The two groups were ran-domly divided into training group(30 cases of lung adenocarcinoma,26 cases of control)and test group(7 ca-ses of lung adenocarcinoma,7 cases of control)according to 3:1.T he differential diagnosis of lung adenocarci-noma and control group was performed by liquid chip-time-of-flight mass spectrometry and software ClinPro-Tools 3.0 to establish a prediction model of lung adenocarcinoma.The diagnostic model was validated by using serum samples from the test group to assess the diagnostic efficacy of the model.Results Nine peptide peaks with significant differences(P<0.05)were obtained by ClinProTools 3.0 software analysis.The up-regulated peaks in lung adenocarcinoma(m/z)were 8 976.5,4 469.05,4 966.78,8 925.5,4 531.05,and the down-reg-ulated m/z were 3 304.44,8 594.76,3 266.82,3 195.52.According to the genetic algorithm(GA),the lung ad-enocarcinoma diagnosis and prediction model was established.The overall recognition ability of the model was 94.49%.The model was evaluated by the test group.The results showed that the sensitivity of the model was 100.0% and the specificity was 85.7%.Conclusion Among lung adenocarcinoma patients,serum benign lung disease and healthy,there are differences in the serum peptide.T he use of differential peptide peaks to estab-lish lung adenocarcinoma diagnostic prediction model for the early diagnosis of lung adenocarcinoma provides a new method.
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Objective A BP neural network model for diagnosing type 2 diabetic nephropathy based on laboratory tests was developed and evaluated. Methods Patients with type 2 diabetic nephropathy from 5 hospitals of Chongqing,Guizhou and Sichuan Provinces from January 2016 to December 2016 were collected in the study. Totally 89 parameters were analyzed by univariate analysis to identify significant variables by SPSS 19. 0 and MATLAB 2014a. The diagnostic performance of the two methods were compared. Results A total of 477 patients with type 2 diabetic nephropathy and 449 patients of control group were included. Univariate analysis showed that 42 variables had significant difference. Logistic regression analysis showed that 12 variables were included in the optimal regression equation. This BP neural network had 42 input layer nodes,15 hidden layer nodes and 1 output layer nodes. The Youden index of logistic regression analysis and BP neural network(training set and test set) were 0.76,0.89 and 0.83. The accurately diagnosed were 88.12%,94.24%,and 91.34%,the AUC were 0.95,0.98,and 0.96. Conclusion A BP neural network model was developed,which has important accessory diagnostic value for diagnosis of type 2 diabetic nephropathy. But all these conclusions need further validation in clinic.
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Objective To investigate the discriminant analysis model of gray-scale ultrasound (GSUS),contrast-enhanced ultrasound (CEUS) and the combination of them in the differential diagnosis of benign and malignant thyroid lesions and the diagnostic values.Methods Ultrasound images of 211 thyroid lesions confirmed by pathology were synthetically reviewed by scoring 5 GSUS indicators including shape (X1),orientation (X2),interior echogenicity (X3),halo sign (X4),and microcalcification (X5),as well as 6 CEUS indicators including relative arrival time of microbubhles in the periphery (X6) and interior (X7),peak periphery (X8) and interior (X9)echogenicity,peripheral ring-enhancement (X10),homogeneity of enhancement (X11).The diagnostic models with their values of GSUS,CEUS and the combination of them were explored by discriminant analysis.Results The discriminant analysis function of GSUS in the diagnosis of thyroid benign and malignant lesions was g1 (X) =0.715 X1+0.276X2 + 1.028X3 +1.197X4 +0.923X5-2.202 with the diagnostic value 86.3％,the discriminant analysis function of CEUS was g2(X) =-0.392X6 +0.541X7-0.117X8 +0.562X9 + 1.173X10 +2.200X11-1.956 with the diagnostic value 89.1％,and the discriminant analysis function of the combination of GSUS and CEUS was g3 (X) =0.418X1 + 0.173X2 + 0.626X3 + 0.558X4 + 0.183X5-0.476X6 + 0.474X7-0.071X8 + 0.399X9 + 0.985X10 +1.639X11-2.530 with the diagnostic value 91.0％.Conclusions GSUS and CEUS were valuable in the differential diagnosis of benign and malignant thyroid lesions,and the combination of GSUS and CEUS was most valuable.
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Objective To explore the application of serum SELDI proteomic patterns to distinguish breast cancer patients from healthy individuals.Methods All serum samples from 101 breast cancer patients and 45 healthy individuals were analyzed by surface-enhanced laser desorption/ionization time-of-fight mass spectrometry (SELDI-TOF-MS).The spectra were generated on weak cation exchange (WCX2) chips,and protein peaks clustering and classification analysis were made using Biomaker Wizard software and Biomarker Pattern software (BPS).Then the pattern was evaluated by blinded test.Results 49 different proteins were found to have statistically differential expression levels between breast cancer and normal control sera (P ＜ 0.05).A diagnostic model consisting of three protein peaks (M/Z 5627,8124 and 2864) could do the best in the diagnosis between breast cancer and healthy individual.When the diagnostic model was tested with the blinded test set,it yielded a positive value of 95 ％ (139/146),a sensitivity of 97 ％ (98/101) and a specificity of 91％ (41/45).Conclusion These results suggested that serum SELDI protein profiling can distinguish breast cancer patients from normal subjects with relatively high sensitivity and specificity.SELDI-TOF-MS plays a valuable role in the diagnosis of breast cancer and the discovery of new tumor-specific protein biomarkers.
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To develop glaucoma diagnostic model for early detection of glaucoma and estimate its efficacy in Korea, we investigated the results of 98 ocular examinations of our patients at the department of ophthalmology, Kangnam St. Mary's hospital, the Catholic university of Korea, school of medicine from 1998 to 1999.Factors for glaucoma diagnostic model function were age of patients, sex, family history of glaucoma, history of hypertension, cardiovascular diseases and diabetes mellitus, refractive error, intraocular pressure, vertical cupdisc ratio and its asymmetry using ophthalmoscope, Heidelberg retina tomograph(HRT)and high frequency pattern VEP, ERG and visual field analysis. We calculated the diagnostic model function and its reliability compared to the results of visual field analysis using statistical package for social science(SPSS, version 7.5)program. The calculated glaucoma diagnostic model function was discriminant score=-8.0 8+0.25(age)+2.52(vertical C/D ratio)+0.11(C/D ratio asymmetry)-0.7(HRT classification)+0.003(intraocular pressure)+1.59(DM)-0.07(P100)+0.12(retino-cortical time)+0.04(sex). The sensitivity, specificity and diagnostic precision of the model function were 77.4%, 83.7%, 81.1%, respectively. With this comprehensive model using all known glaucoma factors, we could get better results about 15 to 20%than those of each individualmodel using the factors one by one. In conclusion, we think that the comprehensive glaucoma diagnostic model using all known risk factors may help us in early detection of glaucoma.
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Humans , Cardiovascular Diseases , Diabetes Mellitus , Glaucoma , Hypertension , Intraocular Pressure , Korea , Ophthalmology , Ophthalmoscopes , Refractive Errors , Retina , Risk Factors , Sensitivity and Specificity , Visual FieldsABSTRACT
Objective:To examine the serum proteomic spectra of human esophagial carcinoma by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS),so as to set up a diagnostic model of esophagial carcinoma and to investigate its clinical value. Methods:Thirty-two esophagial carcinoma patients and 28 healthy controls were obtained from Fourth Affiliated Hospital of Hebei Medical University during May to September of 2008. Serum protein was extracted by weak cation exchange (WCX) protein chip system,and proteomic spectra was examined by MALDI-TOF MS. The obtained data were analyzed by ZUCI-protein chip data analyze system (ZUCI-PCDAS) and an esophagial carcinoma diagnostic model was established by genetic arithmetic (GA) combined support vector machine (SVM). The above 60 samples were randomly divided into training set and blinding test set,with training set including 21 esophagial carcinoma patients and 19 healthy controls and blinding test set including 11 esophagial carcinoma patients and 9 healthy controls,so as to examine the specificity and sensitivity of this diagnostic model. Results:Serum proteomic spectra of esophagial carcinoma patients and healthy controls were obtained by MALDI-TOF MS,and m/z (mass to charge) peaks of 44 differential proteins were obtained after analyzed by ZUCI-PCDAS software package (P