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

A deep neural network improves endoscopic detection of early gastric cancer without blind spots

Endoscopy ◽  
2019 ◽  
Vol 51 (06) ◽  
pp. 522-531 ◽  
Author(s):  
Lianlian Wu ◽  
Wei Zhou ◽  
Xinyue Wan ◽  
Jun Zhang ◽  
Lei Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Predictive Value ◽  
The Third ◽  
Visual Tasks ◽  
Deep Convolution Neural Network ◽  
Different Parts ◽  
Better Than ◽  
Blind Spots

Abstract Background Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early gastric cancer (EGC) without blind spots during esophagogastroduodenoscopy (EGD). Methods 3170 gastric cancer and 5981 benign images were collected to train the DCNN to detect EGC. A total of 24549 images from different parts of stomach were collected to train the DCNN to monitor blind spots. Class activation maps were developed to automatically cover suspicious cancerous regions. A grid model for the stomach was used to indicate the existence of blind spots in unprocessed EGD videos. Results The DCNN identified EGC from non-malignancy with an accuracy of 92.5 %, a sensitivity of 94.0 %, a specificity of 91.0 %, a positive predictive value of 91.3 %, and a negative predictive value of 93.8 %, outperforming all levels of endoscopists. In the task of classifying gastric locations into 10 or 26 parts, the DCNN achieved an accuracy of 90 % or 65.9 %, on a par with the performance of experts. In real-time unprocessed EGD videos, the DCNN achieved automated performance for detecting EGC and monitoring blind spots. Conclusions We developed a system based on a DCNN to accurately detect EGC and recognize gastric locations better than endoscopists, and proactively track suspicious cancerous lesions and monitor blind spots during EGD.

scholarly journals A Lesion-Based Convolutional Neural Network Improves Endoscopic Detection and Depth Prediction of Early Gastric Cancer

2019 ◽  
Vol 8 (9) ◽  
pp. 1310 ◽  
Author(s):  
Hong Jin Yoon ◽  
Seunghyup Kim ◽  
Jie-Hyun Kim ◽  
Ji-Soo Keum ◽  
Sang-Il Oh ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Treatment Method ◽  
Factors Affecting ◽  
Receiver Operating Characteristic Curves ◽  
Endoscopic Images ◽  
Depth Prediction ◽  
Undifferentiated Type ◽  
Histologic Differentiation

In early gastric cancer (EGC), tumor invasion depth is an important factor for determining the treatment method. However, as endoscopic ultrasonography has limitations when measuring the exact depth in a clinical setting as endoscopists often depend on gross findings and personal experience. The present study aimed to develop a model optimized for EGC detection and depth prediction, and we investigated factors affecting artificial intelligence (AI) diagnosis. We employed a visual geometry group(VGG)-16 model for the classification of endoscopic images as EGC (T1a or T1b) or non-EGC. To induce the model to activate EGC regions during training, we proposed a novel loss function that simultaneously measured classification and localization errors. We experimented with 11,539 endoscopic images (896 T1a-EGC, 809 T1b-EGC, and 9834 non-EGC). The areas under the curves of receiver operating characteristic curves for EGC detection and depth prediction were 0.981 and 0.851, respectively. Among the factors affecting AI prediction of tumor depth, only histologic differentiation was significantly associated, where undifferentiated-type histology exhibited a lower AI accuracy. Thus, the lesion-based model is an appropriate training method for AI in EGC. However, further improvements and validation are required, especially for undifferentiated-type histology.

Reevaluation of Classification of “Early Gastric Cancer”

Endoscopy ◽  
1974 ◽  
Vol 6 (04) ◽  
pp. 209-213 ◽  
Author(s):  
K. Murakami ◽  
F. Misaki ◽  
K. Shimamoto ◽  
K. Kawai
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer

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.

Sa1639 - Kyoto Classification of Gastritis is Useful for Detect the Patients with High Risk for Early Gastric Cancer in Japan

2018 ◽  
Vol 154 (6) ◽  
pp. S-338
Author(s):  
Tomoari Kamada ◽  
Ken Haruma ◽  
Kazuhiko Inoue ◽  
Noriaki Manabe ◽  
Jiro Hata ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer
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