TY - JOUR
T1 - AI in radiological imaging of soft-tissue and bone tumours
T2 - a systematic review evaluating against CLAIM and FUTURE-AI guidelines
AU - Spaanderman, Douwe J.
AU - Marzetti, Matthew
AU - Wan, Xinyi
AU - Scarsbrook, Andrew F.
AU - Robinson, Philip
AU - Oei, Edwin H.G.
AU - Visser, Jacob J.
AU - Hemke, Robert
AU - van Langevelde, Kirsten
AU - Hanff, David F.
AU - van Leenders, Geert J.L.H.
AU - Verhoef, Cornelis
AU - Grünhagen, Dirk J.
AU - Niessen, Wiro J.
AU - Klein, Stefan
AU - Starmans, Martijn P.A.
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2025/4
Y1 - 2025/4
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - CLAIM
KW - FUTURE-AI
KW - Medical image analysis
KW - Radiological imaging
KW - Soft-tissue and bone tumours
KW - Systematic review
UR - https://www.scopus.com/pages/publications/105000440499
U2 - 10.1016/j.ebiom.2025.105642
DO - 10.1016/j.ebiom.2025.105642
M3 - Article
C2 - 40118007
AN - SCOPUS:105000440499
SN - 2352-3964
VL - 114
JO - EBioMedicine
JF - EBioMedicine
M1 - 105642
ER -