Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy

Cui, Sunan; Ten Haken, Randall K.; El Naqa, Issam

doi:10.1016/j.ijrobp.2021.01.042

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Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy

Cui, Sunan·Ten Haken, Randall K.·El Naqa, Issam

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Abstract

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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62 RADIOLOGY AND NUCLEAR MEDICINE ANIMAL TISSUES LUNGS LYMPHOKINES MACHINE LEARNING NEOPLASMS NEURAL NETWORKS PATIENTS PNEUMONITIS POSITRON COMPUTED TOMOGRAPHY RADIOMICS RADIOTHERAPY

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Cui, Sunan, Ten Haken, Randall K., El Naqa, Issam. 2021-07-15. Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy. https://doi.org/10.1016/j.ijrobp.2021.01.042

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