Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma

doi:10.4329/wjr.v18.i2.116462

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World Journal of Radiology

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1949-8470

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

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©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.

World J Radiol. Feb 28, 2026; 18(2): 116462
Published online Feb 28, 2026. doi: 10.4329/wjr.v18.i2.116462

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Figure 4 Calibration curves for the nomogram presenting agreement between predicted and observational survival probabilities of overall survival for patients with soft-tissue sarcomas receiving surgical resection. The gray line of Y = X represents a perfect predictive power by an ideal model. The fit goodness with this diagonal line coincided with the model’s predictive performance. A: Calibration plot for comparison between nomogram predicted 3-year survival rates and actual observation for overall survival (OS) in the training cohort; B: Calibration plot for comparison between nomogram predicted 5-year survival rates and actual observation for OS in the training cohort; C: Calibration plot for comparison between nomogram predicted 3-year survival rates and actual observation for OS in the testing cohort; D: Calibration plot for comparison between nomogram predicted 5-year survival rates and actual observation for OS in the testing cohort. RSF: Random survival forest.

Citation: Yang YH. Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma. World J Radiol 2026; 18(2): 116462
URL: https://www.wjgnet.com/1949-8470/full/v18/i2/116462.htm
DOI: https://dx.doi.org/10.4329/wjr.v18.i2.116462

Yang YH. Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma. World J Radiol 2026; 18(2): 116462 [PMID: 41809710 DOI: 10.4329/wjr.v18.i2.116462]

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