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Updated: Sep 9, 2025

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小儿甲状腺癌复发的ML预测:使用XGBoost和SHAP进行MET队列分析

Antje Redlich1, Elisabeth Pfaehler2, Marina Kunstreich1,3

  • 1Department of Pediatrics, Pediatric Hematology/Oncology, Otto-von-Guericke-University, Leipziger Str. 44, Magdeburg D-39120, Germany.

The Journal of clinical endocrinology and metabolism
|September 1, 2025
PubMed
概括

这项研究开发了一种可解释的机器学习模型,用于预测儿科差异化甲状腺癌 (DTC) 的早期复发. 该模型准确地识别高风险患者,协助个性化治疗决策以获得更好的结果.

关键词:
儿童和青少年美国一个小小的故事.分化甲状腺癌预测情况

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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