Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

doi:10.4240/wjgs.v16.i6.1571

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Jun 27, 2024 (publication date) through Jul 17, 2024

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World Journal of Gastrointestinal Surgery

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1948-9366

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastrointest Surg. Jun 27, 2024; 16(6): 1571-1581
Published online Jun 27, 2024. doi: 10.4240/wjgs.v16.i6.1571

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Figure 1 The flow chart of patient selection and data process. SOS: Sum of squares; IND: Inverse difference; MES: Mean sum; SUV: Sum variance; SUE: Sum entropy; DIV: Difference variance; DIE: Difference entropy; RFM: Random forest model; ANNM: Artificial neural network model; GLRM: Generalized linear regression model; SLM: Synchronous liver metastasis.

Citation: Yang KF, Li SJ, Xu J, Zheng YB. Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer. World J Gastrointest Surg 2024; 16(6): 1571-1581
URL: https://www.wjgnet.com/1948-9366/full/v16/i6/1571.htm
DOI: https://dx.doi.org/10.4240/wjgs.v16.i6.1571