Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies

IF 3.4 2区医学 Q2 Medicine

Journal of Bone Oncology Pub Date : 2025-04-24 DOI:10.1016/j.jbo.2025.100682

Lotte R. van der Linden , Ioannis Vavliakis , Tom M. de Groot , Paul C. Jutte , Job N. Doornberg , Santiago A. Lozano-Calderon , Olivier Q. Groot

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引用次数: 0

Abstract

Background

The last decade has witnessed a surge in artificial intelligence (AI). With bone metastases becoming more prevalent, there is an increasing call for personalized treatment options, a domain where AI can greatly contribute. However, integrating AI into clinical settings has proven to be difficult. Therefore, we aimed to provide an overview of AI modalities for treating bone metastases and recommend implementation-worthy models based on TRIPOD, CLAIM, and UPM scores.

Methods

This systematic review included 92 studies on AI models in bone metastases between 2008 and 2024. Using three assessment tools we provided a reliable foundation for recommending AI modalities fit for clinical use (TRIPOD or CLAIM ≥ 70 % and UPM score ≥ 10).

Results

Most models focused on survival prediction (44/92;48%), followed by imaging studies (37/92;40%). Median TRIPOD completeness was 70% (IQR 64–81%), CLAIM completeness was 57% (IQR 48–67%), and UPM score was 7 (IQR 5–9). In total, 10% (9/92) AI modalities were deemed fit for clinical use.

Conclusion

Transparent reporting, utilizing the aforementioned three evaluation tools, is essential for effectively integrating AI models into clinical practice, as currently, only 10% of AI models for bone metastases are deemed fit for clinical use. Such transparency ensures that both patients and clinicians can benefit from clinically useful AI models, potentially enhancing AI-driven personalized cancer treatment.

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骨转移中的人工智能：92项研究指南依从性的系统回顾

过去十年见证了人工智能（AI）的激增。随着骨转移变得越来越普遍，人们越来越需要个性化的治疗方案，人工智能可以在这个领域做出巨大贡献。然而，将人工智能融入临床环境已被证明是困难的。因此，我们旨在概述治疗骨转移的人工智能模式，并基于TRIPOD、CLAIM和UPM评分推荐具有实施价值的模型。方法系统回顾了2008年至2024年间关于骨转移AI模型的92项研究。通过使用三种评估工具，我们为推荐适合临床使用的人工智能模式提供了可靠的基础（TRIPOD或CLAIM≥70%，UPM评分≥10）。结果大多数模型关注生存预测（44/92;48%），其次是影像学研究（37/92;40%）。中位TRIPOD完整性为70% (IQR 64-81%)， CLAIM完整性为57% (IQR 48-67%)， UPM评分为7 （IQR 5-9）。总共有10%（9/92）的人工智能模式被认为适合临床使用。利用上述三种评估工具进行透明报告，对于将人工智能模型有效地融入临床实践至关重要，因为目前只有10%的骨转移人工智能模型被认为适合临床使用。这种透明度确保了患者和临床医生都能从临床上有用的人工智能模型中受益，潜在地增强了人工智能驱动的个性化癌症治疗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Bone Oncology ONCOLOGY-

CiteScore

7.20

自引率

2.90%

发文量

审稿时长

34 days

期刊介绍： The Journal of Bone Oncology is a peer-reviewed international journal aimed at presenting basic, translational and clinical high-quality research related to bone and cancer. As the first journal dedicated to cancer induced bone diseases, JBO welcomes original research articles, review articles, editorials and opinion pieces. Case reports will only be considered in exceptional circumstances and only when accompanied by a comprehensive review of the subject. The areas covered by the journal include: Bone metastases (pathophysiology, epidemiology, diagnostics, clinical features, prevention, treatment) Preclinical models of metastasis Bone microenvironment in cancer (stem cell, bone cell and cancer interactions) Bone targeted therapy (pharmacology, therapeutic targets, drug development, clinical trials, side-effects, outcome research, health economics) Cancer treatment induced bone loss (epidemiology, pathophysiology, prevention and management) Bone imaging (clinical and animal, skeletal interventional radiology) Bone biomarkers (clinical and translational applications) Radiotherapy and radio-isotopes Skeletal complications Bone pain (mechanisms and management) Orthopaedic cancer surgery Primary bone tumours Clinical guidelines Multidisciplinary care Keywords: bisphosphonate, bone, breast cancer, cancer, CTIBL, denosumab, metastasis, myeloma, osteoblast, osteoclast, osteooncology, osteo-oncology, prostate cancer, skeleton, tumour.