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Updated: Jun 11, 2025

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

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基于机器学习的乳腺侵袭性微粒细胞癌的预后指数预测模型:基于SEER人口的SEER研究

Zirong Jiang1,2, Yushuai Yu1, Xin Yu1

  • 1Department of Breast Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350011, China.

BMC medical informatics and decision making
|September 28, 2024
PubMed
概括

这项研究开发了一种XGBoost机器学习模型,以预测侵袭性微毛细细胞癌 (IMPC) 患者的5年生存率,这是一种罕见的乳腺癌亚型. 该模型显示了改善临床决策和患者结果的前景.

关键词:
乳腺癌 乳腺癌 乳腺癌机器学习是机器学习.微毛囊性癌瘤是一种微毛囊性癌.预测 预后 预测 预测

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Cancer Survival Analysis01:21

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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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