AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
<|reference_start|>AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines: Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides 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. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing 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 for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. 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$\pm$7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1$\pm$2.1 out of 30. 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. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods.<|reference_end|>
@article{spaanderman2024ai,
    title={AI in radiological imaging of soft-tissue and bone tumours: a systematic
  review evaluating against CLAIM and FUTURE-AI guidelines},
    author={Douwe J. Spaanderman (1), Matthew Marzetti (2,3), Xinyi Wan (1),
  Andrew F. Scarsbrook (4,5), Philip Robinson (4), Edwin H.G. Oei (1), Jacob J.
  Visser (1), Robert Hemke (6), Kirsten van Langevelde (7), David F. Hanff (1),
  Geert J.L.H. van Leenders (8), Cornelis Verhoef (9), Dirk J. Gru"uhagen (9),
  Wiro J. Niessen (1,10), Stefan Klein (1), Martijn P.A. Starmans (1) ((1)
  Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute,
  University Medical Center Rotterdam, Rotterdam, the Netherlands, (2)
  Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, UK, (3)
  Leeds Biomedical Research Centre, University of Leeds, UK, (4) Department of
  Radiology, Leeds Teaching Hospitals NHS Trust, UK, (5) Leeds Institute of
  Medical Research, University of Leeds, UK, (6) Department of Radiology and
  Nuclear Medicine, Amsterdam UMC, Amsterdam, the Netherlands, (7) Department
  of Radiology, Leiden University Medical Center, Leiden, the Netherlands, (8)
  Department of Pathology, Erasmus MC Cancer Institute, University Medical
  Center Rotterdam, Rotterdam, the Netherlands, (9) Department of Surgical
  Oncology, Erasmus MC Cancer Institute, University Medical Center Rotterdam,
  Rotterdam, the Netherlands, (10) Faculty of Medical Sciences, University of
  Groningen, Groningen, the Netherlands)},
    journal={arXiv preprint arXiv:2408.12491},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12491},
    primaryClass={cs.AI cs.LG}
}