Predictive Value of Nomogram Based on Kyoto Classification of Gastritis to Diagnosis of Gastric Cancer

2021 ◽  
Author(s):  
Jiejun Lin ◽  
Huang Su ◽  
Yaqi Guan ◽  
Qingjie Zhou ◽  
Jie Pan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Multivariate Analysis ◽  
Calibration Curve ◽  
Predictive Value ◽  
Predictive Accuracy ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Training Set ◽  
Validation Set

Abstract Background and Aim. It is of importance to predict the risk of gastric cancer (GC) for endoscopists because early detection of GC determines the determines the selection of best treatment strategy and the prognosis of patients. The aim of the study was to evaluate the utility of a predictive nomogram based on Kyoto classification of gastritis for GC. Methods. It was a retrospective study that included 2639 patients who received esophagogastroduodenoscopy and serum pepsinogen (PG) assay from January 2020 to November 2020 at the Endoscopy Center of the Department of Gastroenterology, Wenzhou Central Hospital. Routine biopsy was conducted to determine the benign and malignant lesions pathologically. All cases were randomly divided into the training set (70%) and the validation set (30%) by using bootstrap method. A nomogram was formulated according to multivariate analysis of training set. The predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), area under the curve (AUC) of receiver operating characteristic curve (ROC) as well as calibration curve and were validated by validation set.Results. Multivariate analysis indicated that age, sex, PG I/II ratio and Kyoto classification scores were independent predictive variables for GC. The C-index of the nomogram of the training set was 0.79 (95% CI: 0.74 to 0.84) and the AUC of ROC is 0.79. The calibration curve of the nomogram demonstrated an optimal agreement between predicted probability and observed probability of the risk of GC. In the validation set, the C-index was 0.86 (95% CI: 0.79 to 0.94) with a calibration curve of better concurrence.Conclusion. The nomogram formulated was proven to be of high predictive value for GC.

scholarly journals A novel nomogram based on cardia invasion and chemotherapy to predict postoperative overall survival of gastric cancer patients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hanjun Mo ◽  
Pengfei Li ◽  
Sunfang Jiang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Multivariate Analysis ◽  
Surgical Resection ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Calibration Plot ◽  
Training Set ◽  
Validation Set

Abstract Background We aimed to establish and externally validate a nomogram to predict the 3- and 5-year overall survival (OS) of gastric cancer (GC) patients after surgical resection. Methods A total of 6543 patients diagnosed with primary GC during 2004–2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. We grouped patients diagnosed during 2004–2012 into a training set (n = 4528) and those diagnosed during 2013–2016 into an external validation set (n = 2015). A nomogram was constructed after univariate and multivariate analysis. Performance was evaluated by Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plot. Results The multivariate analysis identified age, race, location, tumor size, T stage, N stage, M stage, and chemotherapy as independent prognostic factors. In multivariate analysis, the hazard ratio (HR) of non-cardia invasion was 0.762 (P < 0.001) and that of chemotherapy was 0.556 (P < 0.001). Our nomogram was found to exhibit excellent discrimination: in the training set, Harrell’s C-index was superior to that of the 8th American Joint Committee on Cancer (AJCC) TNM classification (0.736 vs 0.699, P < 0.001); the C-index was also better in the validation set (0.748 vs 0.707, P < 0.001). The AUCs for 3- and 5-year OS were 0.806 and 0.815 in the training set and 0.775 and 0.783 in the validation set, respectively. The DCA and calibration plot of the model also shows good performance. Conclusions We established a well-designed nomogram to accurately predict the OS of primary GC patients after surgical resection. We also further confirmed the prognostic value of cardia invasion and chemotherapy in predicting the survival rate of GC patients.

scholarly journals A novel nomogram based on cardia and chemotherapy to predict postoperative overall survival of gastric cancer patients

2021 ◽  
Author(s):  
Hanjun Mo ◽  
Pengfei Li ◽  
Sunfang Jiang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Multivariate Analysis ◽  
Surgical Resection ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Calibration Plot ◽  
Training Set ◽  
Validation Set

Abstract Background: We aimed to establish and externally validate a nomogram to predict the 3- and 5-year overall survival (OS) of gastric cancer (GC) patients after surgical resection and explored the roles of cardia and chemotherapy.Methods: A total of 6543 patients diagnosed with primary GC during 2004-2016 were collected from the Surveillance, Epidemiology and End Results (SEER) database. We grouped patients diagnosed during 2004-2012 into a training set (n=4528) and those diagnosed during 2013-2016 into an external validation set (n=2015). A nomogram was constructed after univariate and multivariate analysis. Performance was evaluated by Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plot.Results: The multivariate analysis identified age, race, location, tumor size, T stage, N stage, M stage, and chemotherapy as independent prognostic factors. In multivariate analysis, the hazard ratio (HR) of non-cardia was 0.762 (P<0.001), and that of chemotherapy was 0.556 (P<0.001). Our nomogram was found to exhibit excellent discrimination: in the training set, Harrell’s C-index was superior to that of the 8th American Joint Committee on Cancer (AJCC) TNM classification (0.736 vs 0.699, P<0.001); the C-index was also better in the validation set (0.748 vs 0.707, P<0.001). The AUCs for 3- and 5-year OS were 0.806 and 0.815 in the training set and 0.775 and 0.783 in the validation set, respectively. The DCA and calibration plot of the model also shows good performance.Conclusions: We established a well-designed nomogram to accurately predict the OS of primary GC patients after surgical resection. We also further confirmed the prognostic value of cardia and chemotherapy in predicting the survival rate of GC patients.

Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

2020 ◽  
Vol 163 (6) ◽  
pp. 1156-1165
Author(s):  
Juan Xiao ◽  
Qiang Xiao ◽  
Wei Cong ◽  
Ting Li ◽  
Shouluan Ding ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Study ◽  
Diagnostic Efficiency ◽  
Training Set ◽  
Multivariable Logistic Regression Model ◽  
Predictive Values ◽  
Validation Set ◽  
Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

scholarly journals Predictive value of risk score using Kyoto classification of gastritis a few years prior to diagnosis of early gastric cancer

JGH Open ◽  
2020 ◽  
Author(s):  
Yo Fujimoto ◽  
Yasumi Katayama ◽  
Yoshinori Gyotoku ◽  
Ryosuke Oura ◽  
Ikuhiro Kobori ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Risk Score ◽  
Predictive Value

Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features

2021 ◽  
pp. 1-12
Author(s):  
Zongqiong Sun ◽  
Linfang Jin ◽  
Shuai Zhang ◽  
Shaofeng Duan ◽  
Wei Xing ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Clinical Features ◽  
Calibration Curve ◽  
Prediction Models ◽  
Ct Images ◽  
Clinical Findings ◽  
Training Set ◽  
Prediction Probability ◽  
Radiomics Signature ◽  
Testing Set

PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS: In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p <  0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p >  0.05). CONCLUSION: The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

scholarly journals A novel combination of serum microRNAs for the detection of early gastric cancer

2021 ◽  
Author(s):  
Seiichiro Abe ◽  
Juntaro Matsuzaki ◽  
Kazuki Sudo ◽  
Ichiro Oda ◽  
Hitoshi Katai ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Cancer Center ◽  
Serum Samples ◽  
Retrospective Case ◽  
Serum Mirnas ◽  
Validation Set

Abstract Background The aim of this study was to identify serum miRNAs that discriminate early gastric cancer (EGC) samples from non-cancer controls using a large cohort. Methods This retrospective case–control study included 1417 serum samples from patients with EGC (seen at the National Cancer Center Hospital in Tokyo between 2008 and 2012) and 1417 age- and gender-matched non-cancer controls. The samples were randomly assigned to discovery and validation sets and the miRNA expression profiles of whole serum samples were comprehensively evaluated using a highly sensitive DNA chip (3D-Gene®) designed to detect 2565 miRNA sequences. Diagnostic models were constructed using the levels of several miRNAs in the discovery set, and the diagnostic performance of the model was evaluated in the validation set. Results The discovery set consisted of 708 samples from EGC patients and 709 samples from non-cancer controls, and the validation set consisted of 709 samples from EGC patients and 708 samples from non-cancer controls. The diagnostic EGC index was constructed using four miRNAs (miR-4257, miR-6785-5p, miR-187-5p, and miR-5739). In the discovery set, a receiver operating characteristic curve analysis of the EGC index revealed that the area under the curve (AUC) was 0.996 with a sensitivity of 0.983 and a specificity of 0.977. In the validation set, the AUC for the EGC index was 0.998 with a sensitivity of 0.996 and a specificity of 0.953. Conclusions A novel combination of four serum miRNAs could be a useful non-invasive diagnostic biomarker to detect EGC with high accuracy. A multicenter prospective study is ongoing to confirm the present observations.

Abstract WP158: Validation of a Preliminary Prediction Model with MR Plaque Imaging to Estimate Risk for New Ischemic Brain Lesions after Carotid Endarterectomy or Stenting

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Kohkichi Hosoda ◽  
Nobuyuki Akutsu ◽  
Atsushi Fujita ◽  
Eiji Kohmura
Keyword(s):  
Prediction Model ◽  
Carotid Stenosis ◽  
Predictive Value ◽  
Signal Intensity ◽  
Carotid Plaque ◽  
Brain Lesions ◽  
Black Blood ◽  
Training Set ◽  
Ischemic Brain ◽  
Validation Set

[Objective] Recently, we reported a preliminary prediction model with carotid plaque MRI to estimate risk for new ischaemic brain lesions after CEA or CAS. The objective of this study was to validate this model in new set of patients with carotid stenosis. [Methods] One hundred four patients with carotid stenosis undergoing treatment (63 CEA, 41 CAS) were used as a training set for construction of a preliminary prediction model to estimate risk for new ischemic brain lesions after CEA or CAS. T1 and T2 signal intensity of carotid plaque were measured on black-blood MRI. Associations among MRI findings, treatment, clinical factors, and occurrence of new ischemic lesions on DWI 1 day after treatment were studied by logistic regression. The validity of the prediction model was examined using a new set of patients with carotid stenosis (n = 43) as a validation set. [Results] In the training set, new DWI lesions after treatment were observed in 25 patients (24%). The model demonstrated that T1-signal intensity and CAS were positively associated with new lesions on post-treatment DWI scans, and T2 signal intensity was negatively associated (Fig. 1). The C-index was 0.79, which indicated some predictive value. In the validation set, new DWI lesions after treatment were observed in 10 patients (23%). However, C-index was 0.6 and positive predictive value was 33% (Fig. 2), which suggested overfitting of our model and/or differences in case-mix between the training set and validation set. [Conclusions] Our preliminary prediction model may provide some useful information for decision-making regarding treatment strategy, but needs further collection of patients to improve its predictive value.

Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

2013 ◽  
pp. 322-427
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Classification Models ◽  
Training Set ◽  
Linear Discriminant ◽  
Oecd Principles ◽  
Qsar Models ◽  
Validation Set ◽  
Global Accuracy

In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

scholarly journals Prediction Model of HBsAg Seroclearance in Patients with Chronic HBV Infection

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jing Cao ◽  
Jiao Gong ◽  
Christ-Jonathan Tsia Hin Fong ◽  
Cuicui Xiao ◽  
Guoli Lin ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Hepatitis B ◽  
Cox Regression ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Hbsag Loss ◽  
Actual Observation ◽  
Hbsag Seroclearance ◽  
Low Incidence ◽  
Validation Set

Background. Prediction of HBsAg seroclearance, defined as the loss of circulating HBsAg with or without development of antibodies for HBsAg in patients with chronic hepatitis B (CHB), is highly difficult and challenging due to its low incidence. This study is aimed at developing and validating a nomogram for prediction of HBsAg loss in CHB patients. Methods. We analyzed a total of 1398 patients with CHB. Two-thirds of the patients were randomly assigned to the training set (n=918), and one-third were assigned to the validation set (n=480). Univariate and multivariate analysis by Cox regression analysis was performed using the training set, and the nomogram was constructed. Discrimination and calibration were performed using the training set and validation set. Results. On multivariate analysis of the training set, independent factors for HBsAg loss including BMI, HBeAg status, HBsAg titer (quantitative HBsAg), and baseline hepatitis B virus (HBV) DNA level were incorporated into the nomogram. The HBsAg seroclearance calibration curve showed an optimal agreement between predictions by the nomogram and actual observation. The concordance index (C-index) of nomogram was 0.913, with confirmation in the validation set where the C-index was 0.886. Conclusions. We established and validated a novel nomogram that can individually predict HBsAg seroclearance and non-seroclearance for CHB patients, which is clinically unprecedented. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

scholarly journals The role of red cell distribution width in the differential diagnosis of iron deficiency anemia and non-transfusion dependent thalassemia patients

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Pokpong Piriyakhuntorn ◽  
Adisak Tantiworawit ◽  
Thanawat Rattanathammethee ◽  
Chatree Chai-Adisaksopha ◽  
Ekarat Rattarittamrong ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Microcytic Anemia ◽  
Deficiency Anemia ◽  
Operating Characteristics ◽  
Training Set ◽  
Distribution Width ◽  
Validation Set ◽  
Sensitivity Specificity

This study aims to find the cut-off value and diagnostic accuracy of the use of RDW as initial investigation in enabling the differentiation between IDA and NTDT patients. Patients with microcytic anemia were enrolled in the training set and used to plot a receiving operating characteristics (ROC) curve to obtain the cut-off value of RDW. A second set of patients were included in the validation set and used to analyze the diagnostic accuracy. We recruited 94 IDA and 64 NTDT patients into the training set. The area under the curve of the ROC in the training set was 0.803. The best cut-off value of RDW in the diagnosis of NTDT was 21.0% with a sensitivity and specificity of 81.3% and 55.3% respectively. In the validation set, there were 34 IDA and 58 NTDT patients using the cut-off value of >21.0% to validate. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 70.6%, 83.1% and 72.7% respectively. We can therefore conclude that RDW >21.0% is useful in differentiating between IDA and NTDT patients with high diagnostic accuracy

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