Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

doi:10.1007/s11547-024-01904-w

Review

. 2024 Dec;129(12):1751-1765.

doi: 10.1007/s11547-024-01904-w. Epub 2024 Oct 30.

Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

Affiliations

¹ Emergency Radiology Department, San Camillo-Forlanini Hospital, Rome, Italy. ferraririccardo@gmail.com.
² Dipartimento Di Radiologia, P.O. Colline Dell'Albegna, Orbetello, Grosseto, Italy.
³ Diagnostic Imaging Department, Sandro Pertini Hospital, Rome, Italy.
⁴ Department of Radiology, Careggi University Hospital, Florence, Italy.
⁵ Emergency Radiology Department, San Camillo-Forlanini Hospital, Rome, Italy.
⁶ Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant'Andrea University Hospital, Rome, Italy.
⁷ Department of Translational Research on New Technologies in Medicine e Surgery, Pisa University, Pisa, Italy.

PMID: 39472389
DOI: 10.1007/s11547-024-01904-w

Review

Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

Riccardo Ferrari et al. Radiol Med. 2024 Dec.

. 2024 Dec;129(12):1751-1765.

doi: 10.1007/s11547-024-01904-w. Epub 2024 Oct 30.

Authors

Affiliations

¹ Emergency Radiology Department, San Camillo-Forlanini Hospital, Rome, Italy. ferraririccardo@gmail.com.
² Dipartimento Di Radiologia, P.O. Colline Dell'Albegna, Orbetello, Grosseto, Italy.
³ Diagnostic Imaging Department, Sandro Pertini Hospital, Rome, Italy.
⁴ Department of Radiology, Careggi University Hospital, Florence, Italy.
⁵ Emergency Radiology Department, San Camillo-Forlanini Hospital, Rome, Italy.
⁶ Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant'Andrea University Hospital, Rome, Italy.
⁷ Department of Translational Research on New Technologies in Medicine e Surgery, Pisa University, Pisa, Italy.

PMID: 39472389
DOI: 10.1007/s11547-024-01904-w

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.

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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.

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References

1. 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
1. 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
1. 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
1. 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
1. 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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[1] 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

[2] 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

[3] 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

[4] 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

[5] 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

[6] 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

[7] 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

[8] 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

[9] 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

[10] 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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Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

Affiliations

Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

Authors

Affiliations

Abstract

Conflict of interest statement

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References

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Medical

Research Materials

Abstract

Conflict of interest statement

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Cited by

References

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Medical

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