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Updated: May 27, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis
Huimin Tao1,2, Xu Hui3,4, Zhihong Zhang1
1The First Clinical Medical College of Gansu, University of Chinese Medicine, Lanzhou, Gansu, 730000, China.
Background:
Bone metastases (BM) represent a prevalent complication of tumors. Early and accurate diagnosis, however, is a significant hurdle for radiologists. Recently, artificial intelligence (AI) has emerged as a valuable tool to assist radiologists in the detection of BM. This meta-analysis was undertaken to evaluate the AI diagnostic accuracy for BM.
Methods:
Two reviewers performed an exhaustive search of several databases, including Wei Pu (VIP) database, China National Knowledge Infrastructure (CNKI), Web of Science, Cochrane Library, Ovid-Embase, Ovid-Medline, Wan Fang database, and China Biology Medicine (CBM), from their inception to December 2024. This search focused on studies that developed and/or validated AI techniques for detecting BM in magnetic resonance imaging (MRI) or computed tomography (CT). A hierarchical model was used in the meta-analysis to calculate diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), area under the curve (AUC), specificity (SP), and pooled sensitivity (SE). The risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), while the Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis-artificial intelligence (TRIPOD-AI) was employed for evaluating the quality of evidence.
Result:
This review covered 20 articles, among them, 16 studies were included in the meta-analysis. The results revealed a pooled SE of 0.88 (0.82-0.92), a pooled SP of 0.89 (0.84-0.93), a pooled AUC of 0.95 (0.92-0.96), PLR of 8.1 (5.57-11.80), NLR of 0.14 (0.09-0.21) and DOR of 58 (31-109). When focusing on imaging algorithms. Based on ML, a pooled SE of 0.88 (0.77-0.92), SP 0.88 (0.82-0.92), and AUC 0.93 (0.91-0.95). Based on DL, a pooled SE of 0.89 (0.81-0.95), SP 0.89 (0.81-0.94), and AUC 0.95 (0.93-0.97).
Conclusion:
This meta-analysis underscores the substantial diagnostic value of AI in identifying BM. Nevertheless, in-depth large-scale prospective research should be carried out for confirming AI's clinical utility in BM management.
