3 papers
physics.med-ph2020
A Physics-Guided Modular Deep-Learning Based Automated Framework for Tumor Segmentation in PET Images
Kevin H. Leung, Wael Marashdeh, Rick Wray +4
The objective of this study was to develop a PET tumor-segmentation framework that addresses the challenges of limited spatial resolution, high image noise, and lack of clinical tr…
physics.med-ph2019
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…
physics.med-ph2019
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…