A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer - Shan, Yan-na; Xu, Wen; Wang, Rong; Wang, Wei; Pang, Pei-pei; Shen, Qi-jun (Frontiers in Oncology, 2020)
Objective: To construct and validate a nomogram model integrated the MRI radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Material and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. The kinetic curve was computed using dynamic contrast-enhanced MR imaging (DCE-MRI) software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by ROC analysis and calibration curve. Result: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significant different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort (AUC=0.91, 95%CI 0.83-0.96) and external validation cohort (AUC=0.86, 95%CI 0.72-0.94), the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.