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COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms:Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning

Autor: Shiri, Isaac, Salimi, Yazdan, Pakbin, Masoumeh, Hajianfar, Ghasem, Avval, Atlas Haddadi, Sanaat, Amirhossein, Mostafaei, Shayan, Akhavanallaf, Azadeh, Saberi, Abdollah, Mansouri, Zahra, Askari, Dariush, Ghasemian, Mohammadreza, Sharifipour, Ehsan, Sandoughdaran, Saleh, Sohrabi, Ahmad, Sadati, Elham, Livani, Somayeh, Iranpour, Pooya, Kolahi, Shahriar, Khateri, Maziar, Bijari, Salar, Atashzar, Mohammad Reza, Shayesteh, Sajad P., Khosravi, Bardia, Babaei, Mohammad Reza, Jenabi, Elnaz, Hasanian, Mohammad, Shahhamzeh, Alireza, Foroghi Ghomi, Seyaed Yaser, Mozafari, Abolfazl, Teimouri, Arash, Movaseghi, Fatemeh, Ahmari, Azin, Goharpey, Neda, Bozorgmehr, Rama, Shirzad-Aski, Hesamaddin, Mortazavi, Roozbeh, Karimi, Jalal, Mortazavi, Nazanin, Besharat, Sima, Afsharpad, Mandana, Abdollahi, Hamid, Geramifar, Parham, Radmard, Amir Reza, Arabi, Hossein, Rezaei-Kalantari, Kiara, Oveisi, Mehrdad, Rahmim, Arman, Zaidi, Habib

Publikováno v: Shiri, I, Salimi, Y, Pakbin, M, Hajianfar, G, Avval, A H, Sanaat, A, Mostafaei, S, Akhavanallaf, A, Saberi, A, Mansouri, Z, Askari, D, Ghasemian, M, Sharifipour, E, Sandoughdaran, S, Sohrabi, A, Sadati, E, Livani, S, Iranpour, P, Kolahi, S, Khateri, M, Bijari, S, Atashzar, M R, Shayesteh, S P, Khosravi, B, Babaei, M R, Jenabi, E, Hasanian, M, Shahhamzeh, A, Foroghi Ghomi, S Y, Mozafari, A, Teimouri, A, Movaseghi, F, Ahmari, A, Goharpey, N, Bozorgmehr, R, Shirzad-Aski, H, Mortazavi, R, Karimi, J, Mortazavi, N, Besharat, S, Afsharpad, M, Abdollahi, H, Geramifar, P, Radmard, A R, Arabi, H, Rezaei-Kalantari, K, Oveisi, M, Rahmim, A & Zaidi, H 2022, ' COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms : Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning ', Computers in Biology and Medicine, vol. 145, 105467 . https://doi.org/10.1016/j.compbiomed.2022.105467
Computers in biology and medicine, 145:105467. PERGAMON-ELSEVIER SCIENCE LTD

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and te

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3aae6e827a8027d18f1b2b0beb8f5b0e
https://findresearcher.sdu.dk:8443/ws/files/201695531/1_s2.0_S0010482522002591_main.pdf

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COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.

Autor: Shiri, Isaac, Arabi, Hossein, Salimi, Yazdan, Sanaat, Amirhossein, Akhavanallaf, Azadeh, Hajianfar, Ghasem, Askari, Dariush, Moradi, Shakiba, Mansouri, Zahra, Pakbin, Masoumeh, Sandoughdaran, Saleh, Abdollahi, Hamid, Radmard, Amir Reza, Rezaei‐Kalantari, Kiara, Ghelich Oghli, Mostafa, Zaidi, Habib

Publikováno v: International Journal of Imaging Systems & Technology; Jan2022, Vol. 32 Issue 1, p12-25, 14p