no code implementations • 26 Sep 2019 • Mostafa Nazari, Isaac Shiri, Ghasem Hajianfar, Niki Oveisi, Hamid Abdollahi, Mohammad Reza Deevband, Mehrdad Oveisi
The AUC was 0. 78 in logistic regression, 0. 62 in random forest, and 0. 83 in SVM model, respectively.
Computed Tomography (CT) Medical Physics Image and Video Processing Tissues and Organs
no code implementations • 8 Jul 2019 • Ghasem Hajianfar, Isaac Shiri, Hassan Maleki, Niki Oveisi, Abbass Haghparast, Hamid Abdollahi, Mehrdad Oveisi
Conclusion: This study showed that radiomics using machine learning algorithms is a feasible, noninvasive approach to predict MGMT methylation status in GBM cancer patients Keywords: Radiomics, Radiogenomics, GBM, MRI, MGMT
no code implementations • 3 Jul 2019 • Isaac Shiri, Hassan Maleki, Ghasem Hajianfar, Hamid Abdollahi, Saeed Ashrafinia, Mathieu Hatt, Mehrdad Oveisi, Arman Rahmim
Conclusion: We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters.
no code implementations • 25 Jun 2019 • Shakiba Moradi, Mostafa Ghelich-Oghli, Azin Alizadehasl, Isaac Shiri, Niki Oveisi, Mehrdad Oveisi, Majid Maleki, Jan Dhooge
Feature maps in all levels of the decoder path of U-net are concatenated, their depths are equalized, and up-sampled to a fixed dimension.
no code implementations • 15 Jun 2019 • Isaac Shiri, Hassan Maleki, Ghasem Hajianfar, Hamid Abdollahi, Saeed Ashrafinia, Mathieu Hatt, Mehrdad Oveisi, Arman Rahmim
The aim of this study was to develop radiomic models using PET/CT radiomic features with different machine learning approaches for finding best predictive epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS) mutation status.