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Autor:
Hamid Abdollahi, Habib Zaidi, Mathieu Hatt, Arman Rahmim, Isaac Shiri, Ghasem Hajianfar, Mehrdad Oveisi, Hasan Maleki, Saeed Ashrafinia
Publikováno v:
Molecular Imaging and Biology, Vol. 22, No 4 (2020) pp. 1132-1148
Shiri, I, Maleki, H, Hajianfar, G, Abdollahi, H, Ashrafinia, S, Hatt, M, Zaidi, H, Oveisi, M & Rahmim, A 2020, ' Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms ', Molecular Imaging and Biology, vol. 22, no. 4, pp. 1132-1148 . https://doi.org/10.1007/s11307-020-01487-8
Molecular Imaging and Biology, 22(4), 1132-1148. SPRINGER
Shiri, I, Maleki, H, Hajianfar, G, Abdollahi, H, Ashrafinia, S, Hatt, M, Zaidi, H, Oveisi, M & Rahmim, A 2020, ' Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms ', Molecular Imaging and Biology, vol. 22, no. 4, pp. 1132-1148 . https://doi.org/10.1007/s11307-020-01487-8
Molecular Imaging and Biology, 22(4), 1132-1148. SPRINGER
Aim: In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on PET and CT multi-modalities radiomic features and machine learning (ML) algo