Nabil Elshafeey
Publications by Year
Research Areas
Radiomics and Machine Learning in Medical Imaging, Medical Imaging Techniques and Applications, Glioma Diagnosis and Treatment, MRI in cancer diagnosis, Breast Cancer Treatment Studies
Most-Cited Works
- → Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma(2019)143 cited
- → A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models(2018)104 cited
- → MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer(2022)69 cited
- → Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers(2021)53 cited
- → Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI(2023)41 cited
- → Dynamic contrast-enhanced MRI detects acute radiotherapy-induced alterations in mandibular microvasculature: prospective assessment of imaging biomarkers of normal tissue injury(2016)40 cited
- → A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer(2022)36 cited
- → Functional Tumor Volume by Fast DynamicContrast‐Enhanced MRIfor Predicting Neoadjuvant Systemic Therapy Response inTriple‐NegativeBreast Cancer(2021)28 cited
- → A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer(2023)28 cited
- → Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer–weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study(2021)27 cited