Translating radiomics into clinical practice: A step-by-step guide to study design and evaluation

Radiomics, the high-throughput extraction of quantitative features from medical images, holds immense potential to advance precision medicine. Despite its growing popularity in research, radiomics sees few cases of successful translation into clinical practice, attributable in part to the technical complexity of its workflow; clinicians lacking formal computational training might find it daunting to conduct or evaluate radiomics studies. This practical guide aims to address this by providing a step-by-step approach to plan or appraise a radiomics study, for clinicians or others new to the field. Emphasis is placed on critical aspects such as data preprocessing, feature reproducibility, bias minimisation, and model validation, the areas frequently overlooked in early stage works. By equipping readers with accessible, pragmatic advice, this guide seeks to broaden access to radiomics, facilitating high-quality study design, and accelerating its integration into patient care.

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Publication Details

Journal
Clinical Radiology
Published
2025-08-22
DOI
https://doi.org/10.1016/j.crad.2025.107053
Citations
7
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
9.52
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article

Translating radiomics into clinical practice: A step-by-step guide to study design and evaluation

Susan J. Copley, Mitchell Chen, Kristofer Linton‐Reid, Eric O. Aboagye
7 citations
Clinical Radiology
Radiomics and Machine Learning in Medical Imaging
9.52
article

Translating radiomics into clinical practice: A step-by-step guide to study design and evaluation

Susan J. Copley, Mitchell Chen, Kristofer Linton‐Reid, Eric O. Aboagye
article en
7 citations

Abstract

Radiomics, the high-throughput extraction of quantitative features from medical images, holds immense potential to advance precision medicine. Despite its growing popularity in research, radiomics sees few cases of successful translation into clinical practice, attributable in part to the technical complexity of its workflow; clinicians lacking formal computational training might find it daunting to conduct or evaluate radiomics studies. This practical guide aims to address this by providing a step-by-step approach to plan or appraise a radiomics study, for clinicians or others new to the field. Emphasis is placed on critical aspects such as data preprocessing, feature reproducibility, bias minimisation, and model validation, the areas frequently overlooked in early stage works. By equipping readers with accessible, pragmatic advice, this guide seeks to broaden access to radiomics, facilitating high-quality study design, and accelerating its integration into patient care.

Clinical RadiologyVol. 90
Imperial College Healthcare NHS Trust (GB), Charing Cross Hospital (GB), Imperial College London (GB)
Openalex Percentile: Top 2%
Radiomics and Machine Learning in Medical Imaging
9.52
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