Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease

doi:10.3748/wjg.v30.i25.3155

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Jul 7, 2024 (publication date) through Nov 26, 2024

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World Journal of Gastroenterology

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1007-9327

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

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World J Gastroenterol. Jul 7, 2024; 30(25): 3155-3165
Published online Jul 7, 2024. doi: 10.3748/wjg.v30.i25.3155

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Figure 2 The nomogram of combined model. Gender: 1-Male; 2-Female; Enhanced pattern: 1-Homogeneous enhancement; 2-Inhomogeneous enhancement; 3-Hierarchical enhancement; 4-Mucosal enhancement. PIL: Primary intestinal lymphoma; OSV: Original shape voxel volume; LSGL: Log sigma 3-0-mm-3D glcm lmc2; WLLHGR: Wavelet LLH glrlm run percentage; WHLLFM: Wavelet HLL firstorder minimum; WHHHGL: Wavelet HHH glrlm long run high gray level emphasis; WLLLGR: Wavelet LLL glrlm run percentage; WLLLGA: Wavelet LLL glcm autocorrelation; N: Non-contrast enhanced; A: Arterial; V: Venous.

Citation: Xiao MJ, Pan YT, Tan JH, Li HO, Wang HY. Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease. World J Gastroenterol 2024; 30(25): 3155-3165
URL: https://www.wjgnet.com/1007-9327/full/v30/i25/3155.htm
DOI: https://dx.doi.org/10.3748/wjg.v30.i25.3155