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©The Author(s) 2024.
World J Gastrointest Oncol. Apr 15, 2024; 16(4): 1296-1308
Published online Apr 15, 2024. doi: 10.4251/wjgo.v16.i4.1296
Published online Apr 15, 2024. doi: 10.4251/wjgo.v16.i4.1296
Figure 1 Study workflow.
GIST: Gastrointestinal stromal tumor; CT: Computed tomography.
Figure 2 The discrimination ability of the radiomics model and its decision curve analysis for prediction of KIT exon 11 mutation.
A-D: The discrimination ability of three models in the training data (A) and the validation cohort (B). The decision curve analysis for the radiomics models in the training data (C) and the validation cohort (D). AUC: Area under the curve.
Figure 3 The discrimination ability of the radiomics model and its decision curve analysis for prediction of KIT exon 11 codons 557-558 deletions.
A-D: The discrimination ability of three models in the training data (A) and the validation cohort (B). The decision curve analysis for the radiomics models in the training data (C) and the validation cohort (D). AUC: Area under the curve.
- Citation: Yin XN, Wang ZH, Zou L, Yang CW, Shen CY, Liu BK, Yin Y, Liu XJ, Zhang B. Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor. World J Gastrointest Oncol 2024; 16(4): 1296-1308
- URL: https://www.wjgnet.com/1948-5204/full/v16/i4/1296.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v16.i4.1296