Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of … A Safakish, L Sannachi, D DiCenzo, C Kolios, A Pejović-Milić, ... Frontiers in Oncology 13, 1258970, 2023 | 7 | 2023 |
Predicting head and neck cancer treatment outcomes using textural feature level fusion of quantitative ultrasound spectroscopic and computed tomography: A machine learning approach A Moslemi, A Safakish, L Sannchi, D Alberico, S Halstead, G Czarnota 2023 IEEE International Ultrasonics Symposium (IUS), 1-4, 2023 | 3 | 2023 |
Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach A Safakish, L Sannachi, A Moslemi, A Pejović-Milić, GJ Czarnota Radiation 4 (1), 50-68, 2024 | 2 | 2024 |
Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers A Safakish, A Moslemi, D Moore-Palhares, L Sannachi, I Poon, I Karam, ... Radiation 4 (2), 192-212, 2024 | | 2024 |
Results: It was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity … A Reznik, J Rodgers, S Shukla, O Bubon, GJ Czarnota, A Safakish, ... | | 2023 |
Investigating The Effects Of Pulsed Radiofrequency Therapy In The Blocking Of Action Potentials In Nerves A Safakish Toronto Metropolitan University, 2020 | | 2020 |
Predicting Head & Neck Cancer Treatment Outcomes with Quantitative Ultrasound Texture Features & Optimizing Machine Learning Classifiers with Novel Texture-of-Texture Features A Safakish, L Sannachi, D DiCenzo, C Kolios, A Pejović-Milić, ... Toronto Metropolitan University, 0 | | |
Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach A Safakish, L Sannachi, A Moslemi, A Pejović-Milić, GJ Czarnota Toronto Metropolitan University, 0 | | |