Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact
- PMID: 39472389
- DOI: 10.1007/s11547-024-01904-w
Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact
Abstract
Radiomics represents the science of extracting and analyzing a multitude of quantitative features from medical imaging, revealing the quantitative potential of radiologic images. This scientific review aims to provide radiologists with a comprehensive understanding of radiomics, emphasizing its principles, applications, challenges, limits, and prospects. The limitations of standardization in current scientific production are analyzed, along with possible solutions proposed by some of the referenced papers. As the continuous evolution of medical imaging is ongoing, radiologists must be aware of new perspectives to play a central role in patient management.
Keywords: Abdominal imaging; Artificial Intelligence; Diagnostic radiology; New technologies in radiology; Radiology; Radiomics.
© 2024. Italian Society of Medical Radiology.
Conflict of interest statement
Declarations. Conflict of interest: The authors have no relevant financial or non-financial interests to disclose. Riccardo Ferrari, Margherita Trinci, Emanuele Neri, Lorenzo Faggioni and Damiano Caruso declare to be part of scientific editorial board of Radiologia Medica. Ethical approval: This is a review of literature. No ethical approval is required.
References
-
- Martín Noguerol T, Paulano-Godino F, Martín-Valdivia MT, Menias CO, Luna A (2019) Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am Coll Radiol 16(9):1239–1247. https://doi.org/10.1016/j.jacr.2019.05.047 - DOI - PubMed
-
- Schöneck M, Lennartz S, Zopfs D, Sonnabend K, Wawer Matos Reimer R, Rinneburger M, Graffe J, Persigehl T, Hentschke C, Baeßler B, Lourenco Caldeira L, Große Hokamp N (2024) Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT. Eur J Radiol 26(175):111447. https://doi.org/10.1016/j.ejrad.2024.111447 - DOI
-
- Shakir H, Deng Y, Rasheed H, Khan TMR (2019) Radiomics based likelihood functions for cancer diagnosis. Sci Rep 9(1):9501. https://doi.org/10.1038/s41598-019-45053-x - DOI - PubMed - PMC
-
- Shakir H, Khan T, Rasheed H, Deng Y (2021) Radiomics based bayesian inversion method for prediction of cancer and pathological stage. IEEE J Transl Eng Health Med 30(9):4300208. https://doi.org/10.1109/JTEHM.2021.3108390 - DOI
-
- Li Wen Y, Leech M (2020) Review of the role of radiomics in tumour risk classification and prognosis of cancer. Anticancer Res 40(7):3605–3618. https://doi.org/10.21873/anticanres.14350 - DOI - PubMed
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