A new machine learning framework combined with a nomogram has been developed to predict recurrence after radical gastrectomy in patients with non-metastatic gastric cancer and “double invasion.” Among the 559 patients studied, the random survival forests model achieved the highest c-index of 0.791, outperforming other machine learning approaches. This hybrid model effectively integrates multiple variables to enhance predictive accuracy and guide postoperative care, marking a significant improvement over conventional prognostic models.
Journal Article by Hao Z, Wang Z (…) Zhang W et 2 al. in Eur J Med Res
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