Issam El Naqa
Papers
2
Total Citations
55
H-Index
2
About
Issam El Naqa is a leading figure in the integration of artificial intelligence with radiation oncology, whose work is reshaping how we predict and personalize cancer treatment. His research focuses on the intersection of radiomics, deep learning, and biologically effective dose modeling to improve therapeutic outcomes. A key contribution is his pioneering use of machine learning to fuse pre-treatment CT imaging features with dosimetric data, as demonstrated in his highly cited 2021 study. This work showed that combining computed tomography and biologically effective dose in radiomics and deep learning significantly improves the prediction of tumor response to robotic lung stereotactic body radiation therapy (SBRT), achieving 34 citations for its translational impact. He has also advanced the field by comparing local control and distant metastasis rates between CyberKnife and conventional SBRT in NSCLC patients, a 2020 study with 21 citations that informs clinical decision-making. Through these efforts, El Naqa is not only enhancing the precision of radiotherapy but also establishing a framework for data-driven, individualized cancer care that promises to reduce toxicity and improve survival.
Research Focus
Key Achievements
Top Papers
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