Douwe J Spaanderman, Matthew Marzetti, Xinyi Wan, Andrew F Scarsbrook, Philip Robinson, Edwin H G Oei, Jacob J Visser, Robert Hemke, Kirsten van Langevelde, David F Hanff, Geert J L H van Leenders, Cornelis Verhoef, Dirk J Grünhagen, Wiro J Niessen, Stefan Klein, Martijn P A Starmans
{"title":"人工智能在软组织和骨肿瘤放射成像中的应用：对CLAIM和FUTURE-AI指南的系统评价","authors":"Douwe J Spaanderman, Matthew Marzetti, Xinyi Wan, Andrew F Scarsbrook, Philip Robinson, Edwin H G Oei, Jacob J Visser, Robert Hemke, Kirsten van Langevelde, David F Hanff, Geert J L H van Leenders, Cornelis Verhoef, Dirk J Grünhagen, Wiro J Niessen, Stefan Klein, Martijn P A Starmans","doi":"10.1016/j.ebiom.2025.105642","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Findings: </strong>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.</p><p><strong>Interpretation: </strong>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.</p><p><strong>Funding: </strong>Hanarth Fonds, ICAI Lab, NIHR, EuCanImage.</p>","PeriodicalId":11494,"journal":{"name":"EBioMedicine","volume":"114 ","pages":"105642"},"PeriodicalIF":9.7000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines.\",\"authors\":\"Douwe J Spaanderman, Matthew Marzetti, Xinyi Wan, Andrew F Scarsbrook, Philip Robinson, Edwin H G Oei, Jacob J Visser, Robert Hemke, Kirsten van Langevelde, David F Hanff, Geert J L H van Leenders, Cornelis Verhoef, Dirk J Grünhagen, Wiro J Niessen, Stefan Klein, Martijn P A Starmans\",\"doi\":\"10.1016/j.ebiom.2025.105642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Findings: </strong>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.</p><p><strong>Interpretation: </strong>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). 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AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines.
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.
EBioMedicineBiochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍：
eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.
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