AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
Open accessAbstract
BACKGROUND: Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review aims to provide an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. METHODS: The systematic review identified literature from several bibliographic databases, covering papers published before 17/07/2024. Original research published in peer-reviewed journals, focused on radiology-based AI for diagnosis or prognosis of primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers to determine eligibility. Included papers were assessed against the two guidelines by one of three independent reviewers. The review protocol was registered with PROSPERO (CRD42023467970). FINDINGS: The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9 ± 7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1 ± 2.1 out of 30. INTERPRETATION: Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. defining unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. building on previous work, training with data that reflect real-world usage, explainability), evaluation (e.g. ensuring biases are evaluated and addressed, evaluating AI against current best practices), and the awareness of data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods. FUNDING: Hanarth Fonds, ICAI Lab, NIHR, EuCanImage.
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SHAP values can rank the importance of different clinical factors and show whether a prognosis was driven primarily by tumor size, patient age, or biomarker status.
“For tabular data, SHAP values can rank the importance of different clinical factors and show whether a prognosis was driven primarily by tumor size, patient age, or biomarker status [162,163,164,165].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [164]
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- Authors
- Spaanderman, D. J., Marzetti, M., Wan, X., Scarsbrook, A. F., Robinson, P., Oei, E. H. G., Visser, J. J., Hemke, R., van Langevelde, K., Hanff, D. F., van Leenders, G. J. L. H., Verhoef, C., Grünhagen, D. J., Niessen, W. J., Klein, S., & Starmans, M. P. A.
- Affiliations
- Erasmus MC (NL)
- University of Leeds (GB)
- Leeds Teaching Hospitals NHS Trust (GB)
- Amsterdam University Medical Centers (NL)
- University of Groningen (NL)
- Corresponding author
- Spaanderman, Douwe J.
- ORCID
- Venue
- eBioMedicine
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 114
- Pages
- 105642
- ISSN
- 2352-3964 (ISSN-L)
- PMC
- PMC11976239
- OpenAlex
- W4408683699
- Cited by
- 26
- References
- 53
- Citations by year
- 2026: 17 · 2025: 9
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EBioMedicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (acceptedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- Pure Amsterdam UMC (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Institute for Health and Care Research
- Dutch Research Council
- Hanarth Fund Foundation
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence, soft tissue tumor, bone tumors, radiological imaging, trustworthy AI, clinical translation of AI, data reproducibility, algorithmic bias, explainability
- MeSH terms
- Artificial Intelligence, Bone Neoplasms, Humans, Soft Tissue Neoplasms, Practice Guidelines as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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