Applications of Radiomics and Deep Learning in Medical Image Analysis—2nd Edition

Dear Colleagues,

This Special Issue is devoted to the applications and harmonization of handcrafted radiomics and deep learning methods in medical imaging analysis. Many studies have shown that radiomics has the potential to decode clinical information. However, many studies have also reported on the limitations of radiomics techniques due to the heterogenous nature of medical imaging data. Variations in factors such as imaging vendors, imaging parameters, and patient characteristics have been reported to significantly affect the reproducibility and generalizability of image-derived quantitative features. Therefore, in this Special Issue, we would like to focus on different methods, especially novel ones, to improve the reproducibility of image-derived quantitative features across data acquired heterogeneously, and the generalizability of developed signatures. Particularly, we welcome clinical and phantom studies that assess the impacts on reproducibility and generalizability, as well as those promoting new techniques. The overarching goal is to provide additional evidence of the potential of these methods for robust clinical applications.

Dr. Abdalla Ibrahim
Dr. Cecilia Di Ruberto
Guest Editors

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• radiomics harmonization
• image harmonization
• deep learning
• clinical radiomics