4 papers
Non-Invasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Using Computed Tomography Radiomics Features and Machine Learning
Mostafa Nazari, Isaac Shiri, Ghasem Hajianfar +4
Purpose: To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade. Methods…
Cardiac SPECT Radiomics Features Repeatability and Reproducibility: A Multi Scanner Phantom Study
Mohammad Edalat-Javid, Isaac Shiri, Ghasem Hajianfar +7
Background: The aim of this study was to assess the robustness of cardiac SPECT radiomics features against changes in imaging settings including acquisition and reconstruction sett…
Non-Invasive MGMT Status Prediction in GBM Cancer Using Magnetic Resonance Images (MRI) Radiomics Features: Univariate and Multivariate Machine Learning Radiogenomics Analysis
Ghasem Hajianfar, Isaac Shiri, Hassan Maleki +4
Background and aim: This study aimed to predict methylation status of the O-6 methyl guanine-DNA methyl transferase (MGMT) gene promoter status by using MRI radiomics features, as…
A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet
Shakiba Moradi, Mostafa Ghelich-Oghli, Azin Alizadehasl +5
Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D ec…