Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography

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World Journal of Gastrointestinal Oncology

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1948-5204

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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 Oncol. Dec 15, 2025; 17(12): 112873
Published online Dec 15, 2025. doi: 10.4251/wjgo.v17.i12.112873

Table 3 The features selected from feature subset 2 using least absolute shrinkage and selection operator, with the Cox regression analysis

Covariate	HR (95%CI)	P value
clinical_M	2.13 (1.26-3.60)	0.005
clinical_T	1.91 (1.23-2.95)	0.004
MUSCLE_shape_Sphericity0	0.00 (0.00-0.01)	0.014
VAT_firstorder_Mean0	1.02 (0.99-1.06)	0.016
VAT_glcm_ClusterShade0	1.17 (1.00-1.37)	0.047
VAT _shape_Elongation0	33.31 (2.14-519.51)	0.012
VAT_shape_Sphericity0	0.00 (0.00-0.00)	0.001
VAT_shape_SurfaceVolumeRatio0	1.76 (0.97-3.21)	0.045

HR: Hazard ratio; CI: Confidence interval; VAT: Visceral adipose tissue; glcm: Gray-level co-occurrence matrix; 0: Pretreatment.

Citation: Liu MC, Cheng YY, Lin SC, Lin CH, Chuang CY, Chen WH, Liao CH, Hsieh CH, Hsieh MF, Liu YJ. Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography. World J Gastrointest Oncol 2025; 17(12): 112873
URL: https://www.wjgnet.com/1948-5204/full/v17/i12/112873.htm
DOI: https://dx.doi.org/10.4251/wjgo.v17.i12.112873

Liu MC, Cheng YY, Lin SC, Lin CH, Chuang CY, Chen WH, Liao CH, Hsieh CH, Hsieh MF, Liu YJ. Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography. World J Gastrointest Oncol 2025; 17(12): 112873 [PMID: 41480227 DOI: 10.4251/wjgo.v17.i12.112873]

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