8 citations · 8 across the 1 of their papers we have counts for
7 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…
Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches
Isaac Shiri, Hassan Maleki, Ghasem Hajianfar +5
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 an…
Deep-Fill: Deep Learning Based Sinogram Domain Gap Filling in Positron Emission Tomography
Isaac Shiri, Peyman Sheikhzadeh, Mohammad Reza Ay
One of the major challenges in design and developing of PET, scanners are the presence of inactive areas between the detector blocks which degrade the image spatial resolution and…
PET/CT Radiomic Sequencer for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients
Isaac Shiri, Hassan Maleki, Ghasem Hajianfar +5
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 r…