ArticleJournal of bone oncology2025
Single Photon Emission Computed Tomography/X-ray Computed Tomography-based radiomics analysis for diagnosis of bone metastases in patients with breast cancer.
Article in Journal of bone oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- The SPECT/CT radiomics-based classification of skeletal metastases and benign bone lesions.Scientific reports · 2026Article
- Exploring the recurrence and metastasis of breast invasive ductal carcinoma based on machine learning and survival analysis.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Purpose: Bones are the most metastatic site for breast cancer (BC), which can cause complications such as pathologic osteolysis, seriously affecting the quality of life of patients. This study intended to investigate the efficacy of Single Photon Emission Computed Tomography/X-ray Computed Tomography (SPECT/CT) in diagnosing bone metastases in BC and to develop a model for predicting the diagnostic effectiveness. Methods: In this study, we enrolled 185 patients with BC who underwent SPECT/CT scanning. The region of interest (ROI) of each SPECT/CT image was demarcated, and the radiomics features were determined from the ROIs and screened for the optimal features signature to construct the radiomics model. Based on clinical characteristics, the clinical model was developed, and the independent predictive factors were discovered through univariate and multivariate COX regression analyses. Additionally, the radiomics nomogram was created through integrating the radiomics score and independent predictive factors. Thereafter, the receiver operating characteristic (ROC) was applied to determine the diagnostic performance of various models. Results: The radiomics model was constructed based on 29 optimal features. The N stage was an independent factor, and the radiomics nomogram was created through integrating the radiomics score and N stage. Among three models, the radiomics nomogram had the highest diagnostic value for BC bone metastasis (AUC: the training set: 0.956 (0.909-1.000); the validation set: 0.936 (0.866-1.000)). Conclusion: Radiomics analysis based on SPECT/CT can effectively diagnose bone metastasis in BC patients, establishing a theoretical foundation for the formulation of personalized treatment options in clinical practice.
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