activity
20192025
most citedNext Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches

167 citations · 171 across the 4 of their papers we have counts for

collaborators

4 papers

physics.comp-ph2025

Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0

Arman Gorji, Nima Sanati, Amir Hossein Pouria +4

Radiomics-based AI models show promise for breast cancer diagnosis but often lack interpretability, limiting clinical adoption. This study addresses the gap between radiomic featur…

cs.CV2023

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses

Shadab Ahamed, Yixi Xu, Sara Kurkowska +12

This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep learning has been a…

physics.med-ph2019★ 167 cited

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★ 4 cited

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…