Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges
Open accessAbstract
Imaging plays a crucial role in the management of oncologic patients, from the initial diagnosis to staging and treatment response monitoring. Recently, it has been suggested that its importance could be further increased by accessing a new layer of previously hidden quantitative data at the pixel level. Using a multi-step process, radiomics extracts potential biomarkers from medical images that could power decision support tools. Despite the growing interest and rising number of research articles being published, radiomics is still far from fulfilling its promise of guiding oncologic imaging toward personalized medicine. This is, at least partly, due to the heterogeneous methodological quality in radiomic research, caused by the complexity of the analysis pipelines. In this review, we aim to disentangle this complexity with a stepwise approach. Specifically, we focus on challenges to face during image preprocessing and segmentation, how to handle imbalanced classes and avoid information leaks, as well as strategies for the proper validation of findings.
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Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works
“29 Therefore, potential bias present in any of the algorithms will propagate down the pipeline and be inherited by the final model or even amplified in it, resulting in propagation bias.”
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Self-citation
- Authors
- Stanzione, A., Cuocolo, R., Ugga, L., Verde, F., Romeo, V., Brunetti, A., & Maurea, S.
- Affiliations
- University of Naples Federico II (IT)
- University of Salerno (IT)
- Corresponding author
- Cuocolo, Renato
- ORCID
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2022-10-05
- Volume
- 14
- Issue
- 19
- Pages
- 4871
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC9562166
- OpenAlex
- W4303622088
- Cited by
- 64
- References
- 78
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 19 · 2023: 19 · 2022: 2
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics, tumor imaging, radiomic biomarkers, image preprocessing, image segmentation, class imbalance, information leakage, decision support, personalized medicine, methodological quality
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