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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT. | LitMetric

AI Article Synopsis

  • Breast cancer-related bone metastasis increases patient morbidity and mortality, making early detection essential for better outcomes, which this study aims to improve through radiomics analysis of CT images.
  • The research analyzed data from 106 breast cancer patients monitored over five years, utilizing machine learning to develop and assess models predicting bone metastasis risk from thoracic vertebrae images.
  • The study's best-performing model showed a high predictive performance (AUC of 0.929) and suggested that incorporating radiomics could significantly enhance early detection of bone metastasis compared to standard clinical methods.

Article Abstract

Unlabelled: Bone metastasis from breast cancer significantly elevates patient morbidity and mortality, making early detection crucial for improving outcomes. This study utilizes radiomics to analyze changes in the thoracic vertebral bone marrow microenvironment from chest computerized tomography (CT) images prior to bone metastasis in breast cancer, and constructs a model to predict metastasis.

Methods: This study retrospectively gathered data from breast cancer patients who were diagnosed and continuously monitored for five years from January 2013 to September 2023. Radiomic features were extracted from the bone marrow of thoracic vertebrae on non-contrast chest CT scans. Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. The effectiveness of this combined model was assessed through receiver operating characteristic (ROC) analysis as well as decision curve analysis (DCA).

Results: The study included a total of 106 patients diagnosed with breast cancer, among whom 37 developed bone metastases within five years. The radiomics model's area under the curve (AUC) for the test set, calculated using logistic regression, is 0.929, demonstrating superior predictive performance compared to alternative machine learning models. Furthermore, DCA demonstrated the potential of radiomics models in clinical application, with a greater clinical benefit in predicting bone metastasis than clinical model and nomogram.

Conclusion: CT-based radiomics can capture subtle changes in the thoracic vertebral bone marrow before breast cancer bone metastasis, offering a predictive tool for early detection of bone metastasis in breast cancer.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655691PMC
http://dx.doi.org/10.1016/j.jbo.2024.100653DOI Listing

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