医学
放射治疗
逻辑回归
人工智能
卷积神经网络
联合概率分布
深度学习
肺炎
内科学
数据集
机器学习
核医学
计算机科学
肺
统计
数学
作者
Sunan Cui,Randall K. Ten Haken,Issam El Naqa
标识
DOI:10.1016/j.ijrobp.2021.01.042
摘要
Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes-radiation pneumonitis (RP) and local control (LC)-in stage III non-small cell lung cancer (NSCLC) patients. Unlike normal tissue complication probability/tumor control probability models that use dosimetric information solely, our proposed models consider complex interactions among multiomics information including positron emission tomography (PET) radiomics, cytokines, and miRNAs. Additional time-to-event information is also used in the actuarial prediction.
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