The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework
Open accessAbstract
The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.
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Cites
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Cited by
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A Guide to ComBat Harmonization of Imaging Biomarkers in Multicenter Studies
Orlhac, F., Eertink, J. J., Cottereau, A.-S., Zijlstra, J. M., Thieblemont, C., Meignan, M., Boellaard, R., & Buvat, I. (2022) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.262464 · Cited by 348
Mechanistic study of cancer drug delivery: Current techniques, limitations, and future prospects
Imtiaz, S., Ferdous, U. T., Nizela, A., Hasan, A., Shakoor, A., Zia, A. W., & Uddin, S. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.117535 · Cited by 119
Brancato, V., Esposito, G., Coppola, L., Cavaliere, C., Mirabelli, P., Scapicchio, C., Borgheresi, R., Neri, E., Salvatore, M., & Aiello, M. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04891-8 · Cited by 106
Metadata
- Authors
- Ibrahim, A., Primakov, S., Beuque, M., Woodruff, H. C., Halilaj, I., Wu, G., Refaee, T., Granzier, R., Widaatalla, Y., Hustinx, R., Mottaghy, F. M., & Lambin, P.
- Venue
- Methods
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 188
- Pages
- 20-29
- ISSN
- 1046-2023 (ISSN-L)
- OpenAlex
- W3032938107
- Cited by
- 265
- References
- 69
- FWCI
- 14.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ibrahim, A., Primakov, S., Beuque, M., Woodruff, H. C., Halilaj, I., Wu, G., Refaee, T., Granzier, R., Widaatalla, Y., Hustinx, R., Mottaghy, F. M., & Lambin, P. (2021). Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods. https://doi.org/10.1016/j.ymeth.2020.05.022
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
