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肺癌风险分层成像生物标志物的频率排名使用混合弹性网方法

Mohamed Jaber1, Emmy Stevens2, Nezamoddin N Kachouie1,3

  • 1Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA.

Cancers
|February 27, 2026
PubMed
概括

放射性成像生物标志物,特别是纹理特征Busyness,与阶段和年龄等传统因素相比,显著改善了肺癌生存预测. 这为个性化癌症护理提供了一个有希望的非侵入性工具.

关键词:
忙碌的特征是忙碌的特征.在SMOTE平衡中使用SMOTE平衡.肺癌分层分化 肺癌分层分化机器学习在瘤学中的应用预测模型的预测模型.放射性生物标志物 放射性生物标志物生存分析,生存分析.

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