Radiomics in medical imaging—“how-to” guide and critical reflection
Open accessAbstract
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features. The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical “how-to” guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.
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A lack of standardised methodology is an important limitation of all radiomics studies and remains a major challenge in the field.
“Furthermore, a lack of standardised methodology is an important limitation of all radiomics studies and remains a major challenge in the field [18].”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [18]
There is currently no consensus on how to perform image pre-processing steps for MR images in radiomics, and it is one of the open challenges in radiomic processing.
“There is currently no consensus on how to perform these steps for MR images in radiomics, and is one of the many open challenges in radiomic processing [18, 44].”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [18]
Automated segmentations may be considered ideal because they minimise inter-observer variability.
“automated segmentations may be considered the ideal approach as it minimises the inter-observability between segmentations”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [18]
The applicability of trained algorithms to new datasets is currently limited and often results in failure of accurate segmentations.
“the applicability of trained algorithms to new datasets is currently limited and often results in failure of accurate segmentations [18]”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [18]
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Multimodal data fusion for cancer biomarker discovery with deep learning
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Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges
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The cases for and against artificial intelligence in the medical school curriculum
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Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal
Stanzione, A., Verde, F., Cuocolo, R., Romeo, V., Paolo Mainenti, P., Brunetti, A., & Maurea, S. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110497 · Cited by 22 · see citing works
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- Authors
- van Timmeren, J. E., Cester, D., Tanadini-Lang, S., Alkadhi, H., & Baessler, B.
- Affiliations
- University of Zurich (CH)
- Corresponding author
- Baessler, Bettina
- Venue
- Insights into Imaging
- Published
- 2020-08-12
- Volume
- 11
- Issue
- 1
- Pages
- 91-91
- ISSN
- 1869-4101 (ISSN-L)
- PMC
- PMC7423816
- OpenAlex
- W3048802680
- Cited by
- 1,316
- References
- 132
- Citations by year
- 2026: 169 · 2025: 255 · 2024: 274 · 2023: 295 · 2022: 220 · 2021: 96 · 2020: 6
- FWCI
- 22.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Insights into Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- 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 › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- radiomics, radiomic feature extraction, texture analysis, signal intensity distribution, artificial intelligence, clinical decision-making
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