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Updated: Feb 10, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
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一个基于CT成像的深度学习模型,用于预测非小细胞肺癌中的EGFR和KRAS突变:朝着个性化治疗方法

Junxian Li1, Yuchen Xing2, Ximin Gao2

  • 1Department of Blood Transfusion, Key Laboratory of Cancer Prevention and Therapy in Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.

Translational lung cancer research
|February 9, 2026
PubMed
概括

一种新的深度学习模型使用CT扫描和临床数据准确预测非小细胞肺癌 (NSCLC) 的EGFR和KRAS突变. 这种非侵入性方法有助于在NSCLC中个性化治疗决策.

关键词:
基尔斯顿大鼠肉瘤病毒性瘤基因同类突变 (KRAS突变)非小细胞肺癌 (NSCLC) 是一种非小细胞肺癌.深度学习 (DL) 是指深度学习.表皮生长因子受体突变 (EGFR突变)精准医学是一门精准医学.

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