Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia

doi:10.4240/wjgs.v17.i6.106155

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Jun 27, 2025 (publication date) through Jun 2, 2025

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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, 2025; 17(6): 106155
Published online Jun 27, 2025. doi: 10.4240/wjgs.v17.i6.106155

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Figure 1 Workflow of the radiomic analysis. CT: Computed tomography; 3D: Three-dimensional; LASSO: Least absolute shrinkage and selection operator; ROC: Receiver operating characteristic; AUC: Area under the curve; DCA: Decision curve analysis.

Citation: Li DL, Zhu L, Liu SL, Wang ZB, Liu JN, Zhou XM, Hu JL, Liu RQ. Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia. World J Gastrointest Surg 2025; 17(6): 106155
URL: https://www.wjgnet.com/1948-9366/full/v17/i6/106155.htm
DOI: https://dx.doi.org/10.4240/wjgs.v17.i6.106155