A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans

Yun Zhang, Jiao Li, Qiuxia Yang, Shaohan Yin, Jing Hou, Xiaohuan Cao, Shanshan Ma, Bin Wang, Ma Luo, Fan Zhou, Jiahui Xu, Shiyuan Wang, Yi Wu, Jian Zhang, Xiao Luo, Zehong Yang, Weimei Ma, Daiying Lin, Yiqiang Zhan, Xiang Sean Zhou, Xiaoping Yu, Dinggang Shen (), Rong Zhang () and Chuanmiao Xie ()
Additional contact information
Yun Zhang: Sun Yat-sen University Cancer Center
Jiao Li: Sun Yat-sen University Cancer Center
Qiuxia Yang: Sun Yat-sen University Cancer Center
Shaohan Yin: Sun Yat-sen University Cancer Center
Jing Hou: Central South University
Xiaohuan Cao: Shanghai United Imaging Intelligence Co. Ltd.
Shanshan Ma: Shanghai United Imaging Intelligence Co. Ltd.
Bin Wang: Shanghai United Imaging Intelligence Co. Ltd.
Ma Luo: Sun Yat-sen University Cancer Center
Fan Zhou: Sun Yat-sen University Cancer Center
Jiahui Xu: Sun Yat-sen University Cancer Center
Shiyuan Wang: Sun Yat-sen University Cancer Center
Yi Wu: Shantou Central Hospital
Jian Zhang: The First Affiliated Hospital of Guangzhou Medical University
Xiao Luo: Sun Yat-sen University Cancer Center
Zehong Yang: Sun Yat-Sen University
Weimei Ma: The Eighth Affiliated Hospital of Sun Yat-sen University
Daiying Lin: Shantou Central Hospital
Yiqiang Zhan: Shanghai United Imaging Intelligence Co. Ltd.
Xiang Sean Zhou: Shanghai United Imaging Intelligence Co. Ltd.
Xiaoping Yu: Central South University
Dinggang Shen: Shanghai United Imaging Intelligence Co. Ltd.
Rong Zhang: Sun Yat-sen University Cancer Center
Chuanmiao Xie: Sun Yat-sen University Cancer Center

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Manual interpretation of CT images for bone metastasis (BM) detection in primary cancer remains challenging. We present an automated Bone Lesion Detection System (BLDS) developed using CT scans from 2518 patients (9177 BMs; 12,824 non-BM lesions) across five hospitals. The system, developed on 1271 patients and tested on 1247 multicenter cases, demonstrates 89.1% lesion-wise sensitivity (1.40 false-positives/case [FPPC]) in detecting bone lesions on non-contrast CT scans, with 92.3% and 91.1% accuracy in classifying BM/non-BM lesions for internal and external test sets, respectively. Outperforming radiologists in lesion detection (40.5% sensitivity; 0.65 FPPC), BLDS shows lower BM detection sensitivity than junior radiologists, though comparable to trainees. BLDS improves radiologists’ lesion-wise sensitivity by 22.2% in BM detection and reduces reading time by 26.4%, while maintaining 90.2% patient-wise sensitivity and 98.2% negative predictive value in real-world validation (n = 54,610). The system demonstrates significant potential to enhance CT-based BM interpretation, particularly benefiting trainees.

Date: 2025
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DOI: 10.1038/s41467-025-59433-7

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