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©The Author(s) 2024.
World J Gastrointest Surg. Feb 27, 2024; 16(2): 345-356
Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.345
Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.345
Figure 6 Performance of the random survival forest model.
A: The area under the curve of random survival forest model with and without the overall survival-associated computed tomography image radiomics score; B: Calibration curve of random survival forest model for 3-year overall survival prediction; C: Calibration curve for 5-year overall survival; D: Feature importance analysis of all the variables included in the random survival forest model. OACRS: Overall survival-associated computed tomography image radiomics score; SMI: Skeletal muscle index; SMD: Skeletal muscle density; TD: Tumor deposition; ASA: American Association of Anesthesiologists score; CEA: Carcinoembryonic antigen; OS: Overall survival; AUC: Area under the curve.
- Citation: Xiang YH, Mou H, Qu B, Sun HR. Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study. World J Gastrointest Surg 2024; 16(2): 345-356
- URL: https://www.wjgnet.com/1948-9366/full/v16/i2/345.htm
- DOI: https://dx.doi.org/10.4240/wjgs.v16.i2.345