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Updated: May 12, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Artificial Intelligence in bone Metastases: A systematic review in guideline adherence of 92 studies
Lotte R van der Linden1,2, Ioannis Vavliakis1, Tom M de Groot1,2
1Department of Orthopaedic Surgery, University Medical Center Groningen, Groningen, the Netherlands.
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.
