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Updated: Sep 16, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

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基于机器学习的生存分析的开发和验证,以预测辅助化疗的胃癌的结果:一个多中心的纵向队列研究

Yan Pan1, Linbin Lu1,2, Xianchun Gao1

  • 1State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an 710032, China.

Chinese journal of cancer research = Chung-kuo yen cheng yen chiu
|July 11, 2025
PubMed
概括

一个新的动态生存决策树模型 (dySDT) 结合了临床因素和胃癌 (GC) 患者的瘤-炎症-营养得分. 该模型准确评估复发风险,并有助于在胃切除术后的辅助化疗决策.

关键词:
胃癌是什么?胃癌是什么?在HI-GC中获得的分数.机器学习是机器学习.预测模型是一个预测模型.

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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