Spaanderman Douwe J, Marzetti Matthew, Wan Xinyi, Scarsbrook Andrew F, Robinson Philip, Oei Edwin H G, Visser Jacob J, Hemke Robert, van Langevelde Kirsten, Hanff David F, van Leenders Geert J L H, Verhoef Cornelis, Grünhagen Dirk J, Niessen Wiro J, Klein Stefan, Starmans Martijn P A
Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, UK; Leeds Biomedical Research Centre, University of Leeds, UK.
EBioMedicine. 2025 Apr;114:105642. doi: 10.1016/j.ebiom.2025.105642. Epub 2025 Mar 20.
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.
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).
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.
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.
Hanarth Fonds, ICAI Lab, NIHR, EuCanImage.
软组织和骨肿瘤（STBT）较为罕见，在诊断上具有挑战性，其临床行为和治疗方法各不相同。本系统评价旨在概述利用放射影像学的人工智能（AI）方法对这些肿瘤进行诊断和预后评估，突出临床转化中的挑战，并根据医学影像人工智能清单（CLAIM）以及关于可信且可部署人工智能的FUTURE-AI国际共识指南评估研究的一致性，以促进AI方法的临床转化。
该系统评价从多个文献数据库中检索文献，涵盖2024年7月17日前发表的论文。纳入发表在同行评审期刊上的原创研究，其重点是基于放射学的AI用于原发性STBT的诊断或预后评估。排除标准为动物、尸体或实验室研究以及非英文论文。由三名独立评审员中的两名筛选摘要以确定是否符合纳入标准。纳入的论文由三名独立评审员中的一名根据这两项指南进行评估。该评价方案已在PROSPERO（CRD42023467970）注册。
检索到15,015篇摘要，从中纳入325篇文章进行评估。大多数研究在CLAIM上表现中等，在53分中平均得分为28.9±7.5，但在FUTURE-AI上表现较差，在30分中平均得分为5.1±2.1。
用于STBT的影像AI工具仍处于概念验证阶段，表明有很大的改进空间。AI开发者未来的工作应集中在设计（例如确定未满足的临床需求、预期的临床环境以及AI将如何融入临床工作流程）、开发（例如在先前工作的基础上进行构建、使用反映实际应用的数据进行训练、可解释性）、评估（例如确保对偏差进行评估和处理、根据当前最佳实践评估AI）以及对数据可重复性和可用性的认识（公开提供有记录的代码和数据）。遵循这些建议可改善AI方法的临床转化。
哈纳特基金、ICAI实验室、英国国家卫生研究院、欧洲癌症影像联盟。
