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

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.

Keywords: Artificial Intelligence; Guidelines; Machine Learning; Metastatic bone disease; Systematic Review.

Fig. 1

PRISMA flowchart of study inclusions and exclusions * The search was conducted on February 29th 2024.

Fig. 2

Overview of growing amount of studies published on AI in bone metastases.

Fig. 3

Overview of used AI techniques in all 92 models. * Other refers to: Elastic Net Penalized Logistic Regression (48%; 16/33), Logistic Regression (15%; 5/33), Lasso Regularization Logistic Regression (6%; 2/33), Computer aided detection (3%; 1/33), OG-Domain Diffeomorphic Demons Algorithm (3%; 1/33), Multi View-Attention-Guided Network (3% 1/33), Bayes Point Machine (3%; 1/33), Naive Bayes (3%; 1/33), Ensemble Prediction (3%; 1/33), Masked Thresholding (3%; 1/33), Radiomics (3%; 1/33), CatBoost Classifier (3%; 1/33), and Bayesian Classifier (3%; 1/33).