Predictive value of nomogram based on Kyoto classification of gastritis to diagnosis of gastric cancer

Author(s):  
Jiejun Lin ◽  
Huang Su ◽  
Qingjie Zhou ◽  
Jie Pan ◽  
Leying Zhou
JGH Open ◽  
2020 ◽  
Author(s):  
Yo Fujimoto ◽  
Yasumi Katayama ◽  
Yoshinori Gyotoku ◽  
Ryosuke Oura ◽  
Ikuhiro Kobori ◽  
...  

2021 ◽  
Author(s):  
Jiejun Lin ◽  
Huang Su ◽  
Yaqi Guan ◽  
Qingjie Zhou ◽  
Jie Pan ◽  
...  

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.


2018 ◽  
Vol 5 (1) ◽  
pp. 13-23
Author(s):  
Nikolai S. Grachev ◽  
Elena V. Feoktistova ◽  
Igor N. Vorozhtsov ◽  
Natalia V. Babaskina ◽  
Ekaterina Yu. Iaremenko ◽  
...  

Background.Ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) is the gold standard in diagnosing the pathological nature of undetermined thyroid nodules. However, in some instances limitations and shortcomings arise, making it insufficient for determining a specific diagnosis.Objective.Our aim was to evaluate the effectiveness of ACR TI-RADS classification of neck ultrasound as a first-line diagnostic approach for thyroid neoplasms in pediatric patients.Methods.A retrospective analysis was made of FNA and US protocols in 70 patients who underwent the examination and treatment at Dmitry Rogachev National Research Center between January 2012 and August 2017. In the retrospective series 70% (49/70) of patients undergone FNA and 43% (30/70) of them undergone repeated FNA. All US protocols were interpreted according to ACR TI-RADS system by the two independent experts. The clinical judgment was assessed using the concordance test and the reliability of preoperative diagnostic methods was analized.Results.According to histologic examination protocols, benign nodules reported greater multimorbidity 29% (20/70), compared with thyroid cancer 17% (12/70), complicating FNA procedure. A statistically significant predictor of thyroid cancer with a tumor size ACR TI-RADS showed a significant advantage of ACR TI-RADS due to higher sensitivity (97.6 vs 60%), specificity (78.6 vs 53.8%), positive predictive value (87.2 vs 71.4%), and negative predictive value (95.7 vs 41.2%). Concordance on the interpreted US protocols according to ACR TI-RADS classification between two experts was high, excluding accidental coincidence.Conclusion.The data support the feasibility of US corresponding to the ACR TI-RADS classification as a first-line diagnostic approach for thyroid neoplasm reducing the number of unnecessary biopsies for thyroid nodules.


Cancer ◽  
1993 ◽  
Vol 72 (6) ◽  
pp. 1836-1840 ◽  
Author(s):  
Ikuo Takahashi ◽  
Yoshihiko Maehara ◽  
Tetsuya Kusumoto ◽  
Motofumi Yoshida ◽  
Yoshihiro Kakeji ◽  
...  

Oncology ◽  
1994 ◽  
Vol 51 (3) ◽  
pp. 234-237 ◽  
Author(s):  
Yoshihiko Maehara ◽  
Tetsuya Kusumoto ◽  
Ikuo Takahashi ◽  
Yoshihiro Kakeji ◽  
Hideo Baba ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zexin Li ◽  
Kaiji Yang ◽  
Lili Zhang ◽  
Chiju Wei ◽  
Peixuan Yang ◽  
...  

Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. Results. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638–0.931], accuracy of 92.9% [92.7–93.0%], sensitivity of 98.6% [95.9–101.3%], specificity of 58.3% [30.4–86.2%], positive likelihood ratio of 2.367 [1.211–4.625], and negative likelihood ratio of 0.024 [0.003–0.177]. In the cancer prevalence range of 20–40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37–61%, and the range of positive predictive value was 98–99%. Conclusion. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use.


2012 ◽  
Vol 16 (3) ◽  
pp. 324-328 ◽  
Author(s):  
Nozomu Fuse ◽  
Emiko Nagahisa-Oku ◽  
Toshihiko Doi ◽  
Takahide Sasaki ◽  
Shogo Nomura ◽  
...  

Author(s):  
Mackenzie A Hamilton ◽  
Andrew Calzavara ◽  
Scott D Emerson ◽  
Jeffrey C Kwong

Objective: Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10PthP revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada. Study Design and Setting: Influenza and RSV laboratory data from the 2014-15 through to 2017-18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms. Results: 83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%). Conclusion: We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.


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