预测非小细胞肺癌的癌基因突变状态:系统综述和荟萃分析,特别关注基于人工智能的方法

Almudena Fuster-Matanzo, Alfonso Picó Peris, Fuensanta Bellvís Bataller, Ana Jimenez-Pastor, Glen J. Weiss, Luis Martí-Bonmatí, Antonio Lázaro Sánchez, Giuseppe L. Banna, Alfredo Addeo, Ángel Alberich-Bayarri
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引用次数: 0

摘要

背景 在非小细胞肺癌(NSCLC)中,确定患者癌基因突变状态的替代策略对于克服现有方法的一些弊端至关重要。我们旨在回顾放射组学单独使用或与临床数据结合使用的情况,并评估基于人工智能(AI)的模型在预测癌基因突变状态方面的性能。
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Prediction of oncogene mutation status in non-small cell lung cancer: A systematic review and meta-analysis with a special focus on artificial-intelligence-based methods
Background In non-small cell lung cancer (NSCLC), alternative strategies to determine patient oncogene mutation status are essential to overcome some of the drawbacks associated with current methods. We aimed to review the use of radiomics alone or in combination with clinical data and to evaluate the performance of artificial intelligence (AI)-based models on the prediction of oncogene mutation status.
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