Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics.

ABSTRACT

BACKGROUND: Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Currently, there are few quantitative assessments carried out between muscle biomarkers and distant metastasis using existing methods. PURPOSE: To assess the predictive value of the pectoralis muscle for BC distant metastasis, we developed a deep learning radiomics nomogram model (DLR-N) in this study. MATERIAL AND METHODS: A total of 493 patients with pathologically confirmed BC were registered. Image features were extracted from computed tomography (CT) images for each patient. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors for distant metastasis. The DLR-N was built based on independent prognostic factors and CT images to predict distant metastases. The model was assessed in terms of overall performance, discrimination, calibration, and clinical value. Finally, the predictive performance of the model was validated using the testing cohort. RESULTS: The developed DLR-N combined multiple radiomic features and clinicopathological factors and demonstrated excellent predictive performance. The C-index of the training cohort was 0.983 (95% confidence interval [CI] = 0.969-0.998) and the C-index of the testing cohort was 0.948 (95% CI = 0.917-0.979). Decision curve analysis (DCA) showed that patients could benefit more from incorporating multimodal radiomic features into clinicopathological models. CONCLUSIONS: DLR-N verified that there were biomarkers at the level of the fourth thoracic vertebra (T4) that affected distant metastasis. Multimodal prediction models based on deep learning could be a potential method to aid in the prediction of distant metastases in patients with BC.