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©The Author(s) 2022.
World J Gastroenterol. Sep 28, 2022; 28(36): 5338-5350
Published online Sep 28, 2022. doi: 10.3748/wjg.v28.i36.5338
Published online Sep 28, 2022. doi: 10.3748/wjg.v28.i36.5338
Figure 1 Flowchart of patient selection and data processing.
UEGC: Undifferentiated early gastric cancer; EMR: Endoscopic mucosal resection; ESD: Endoscopic submucosal dissection; RFC: Random forest classifier; SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network; XGboost: Extreme gradient boosting; ROC: Receiver operating characteristic; DCA: Decision curve analysis; CIC: Clinical impact curve; LNM: Lymph node metastasis.
- Citation: Wei X, Yan XJ, Guo YY, Zhang J, Wang GR, Fayyaz A, Yu J. Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer. World J Gastroenterol 2022; 28(36): 5338-5350
- URL: https://www.wjgnet.com/1007-9327/full/v28/i36/5338.htm
- DOI: https://dx.doi.org/10.3748/wjg.v28.i36.5338