Machine Learning and Radiomics in Nuclear Medicine and Molecular imaging: Part II.
Khalil, M. (2021). Machine Learning and Radiomics in Nuclear Medicine and Molecular imaging: Part II.. EKB Journal Management System, 23(2), 1-11. doi: 10.21608/egyjnm.2021.217667
Magdy M. Khalil. "Machine Learning and Radiomics in Nuclear Medicine and Molecular imaging: Part II.". EKB Journal Management System, 23, 2, 2021, 1-11. doi: 10.21608/egyjnm.2021.217667
Khalil, M. (2021). 'Machine Learning and Radiomics in Nuclear Medicine and Molecular imaging: Part II.', EKB Journal Management System, 23(2), pp. 1-11. doi: 10.21608/egyjnm.2021.217667
Khalil, M. Machine Learning and Radiomics in Nuclear Medicine and Molecular imaging: Part II.. EKB Journal Management System, 2021; 23(2): 1-11. doi: 10.21608/egyjnm.2021.217667

Machine Learning and Radiomics in Nuclear Medicine and Molecular imaging: Part II.

Egyptian Journal Nuclear Medicine
Article 1, Volume 23, Issue 2, December 2021, Page 1-11  XML PDF (643.36 K)
Document Type: Editorial
DOI: 10.21608/egyjnm.2021.217667
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Author
Magdy M. Khalilorcid 1, 2
1School of Biotechnology, Badr University. +
2Medical Biophysics, Department of Physics, Faculty of Science, Helwan University, Cairo, Egypt.
Abstract
Diagnostic imaging modalities are undergoing a paradigm shift in technological advances and this has significantly impacted patient diagnosis and treatment. The introduction of machine learning and Radiomics in data analysis with capabilities of creating new clinical models has recently caught the attention of clinicians and scientists. Radiomics is a high throughput technology able to derive many imaging features from the diagnostic data while machine learning is a computer science discipline able to provide new forms of “electronic observer” able to mimic human tasks performed by radiologists and nuclear medicine physicians in daily routines. These technologies could be used individually or in combination to facilitate as well as solving issues associated with initial patient diagnosis, image processing, data analysis, stratification, prognosis and management. In the last decade, there was a rapidly growing interest in using Radiomics in nuclear medicine and molecular imaging providing several solutions in reducing the injected radio activities, reducing imaging time, lesion segmentation, diagnosis, and many other applications that could potentially serve or replace current practices. The goal of this part of the machine learning and Radiomics in nuclear medicine series is to introduce the reader to these new technologies and open avenues on current status, potential and future promises.
Keywords
Radiomics; supervised learning; feature extraction; model training; validation
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