Prediction of Bone Oligometastases in Breast Cancer using Models Based on Deep Learning Radiomics of PET/CT Imaging

Guoxiu Lu¹Hui rong Tian²Wei Yang³Yi jia Zhao⁴Jing Wen Chen⁵Jie Zi Xiang⁶Zhang Guoxu^7*Hu Shan Hao^7*

• ¹Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning,110016, China, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China, Shenyang, China
• ²School of Software, Shenyang University of Technology, Shenyang, Liaoning, 110870, China, Shenyang, China
• ³Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, China, Shenyang, China
• ⁴Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, China, Dalian, China
• ⁵Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Yongteng North Road, Haidian District, Beijing Beijing, 100089, China, Beijing, China
• ⁶Biomedical Engineering, Shenyang University of Technology, Shenyang, Liaoning, 111003, China, Shenyang, China
• ⁷Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning,110016, China, Shenyang, China

Objective: To develop a deep learning radiomics(DLR )model integrating PET/CT radiomics, deep learning features, and clinical parameters for early prediction of bone oligometastases (≤5 lesions) in breast cancer. Methods: We retrospectively analyzed 207 breast cancer patients with 312 bone lesions, comprising 107 benign and 205 malignant lesions, including 89 patients with confirmed bone metastases. Radiomic features were extracted from computed tomography (CT), positron emission tomography (PET), and fused PET/CT images using PyRadiomics embedded in the uAI Research Portal. Standardized feature extraction and feature selection were performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. We developed and validated three models: a radiomics-based model, a deep learning model using BasicNet, and a deep learning radiomics (DLR) model incorporating clinical and metabolic parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Statistical comparisons were conducted using the DeLong test.Results: Visual assessment of fused PET/CT images identified 227 (72.8%) abnormal lesions, demonstrating greater sensitivity than CT or PET alone. The complex radiomics model achieved a sensitivity of 98.9% [96.1%-99.4%], specificity of 98.2%[88.1%-99.6%], accuracy of 98.7% [89.6%-99.5%], and area under the curve (AUC) of 0.989. The BasicNet model outperformed other transfer learning models, achieving an AUC of 0.961. The DeLong test confirmed that the AUC of the BasicNet model was significantly higher than the traditional radiomics model. The DLR+Complex model with a random forest classifier achieved the highest overall performance, with an AUC of 0.990, sensitivity of 98.6%, specificity of 90.5%, and accuracy of 99.8%.The BasicNet model significantly outperformed traditional radiomics approaches in predicting bone oligometastases in breast cancer patients. The DLR+Complex model demonstrated the best predictive performance across all metrics. Future strategies for precise diagnosis and treatment should incorporate histologic subtype, advanced imaging, and molecular biomarkers.