Ning Mao1, Ping Yin2, Qin Li3, Qinglin Wang1, Meijie Liu1, Heng Ma1, Jianjun Dong1, Kaili Che1, Zhongyi Wang1, Shaofeng Duan4, Xuexi Zhang4, Nan Hong5, Haizhu Xie6. 1. Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, 264000, Shandong, People's Republic of China. 2. Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Beijing, 100044, People's Republic of China. 3. Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200000, People's Republic of China. 4. GE Healthcare, Shanghai, 200000, People's Republic of China. 5. Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Beijing, 100044, People's Republic of China. hongnan@bjmu.edu.cn. 6. Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, No. 20 Yuhuangding East Street, Yantai, 264000, Shandong, People's Republic of China. xhz000417@sina.com.
Abstract
OBJECTIVE: This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer. METHODS: This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets. RESULTS: The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689-0.858), 0.767 (95% CI 0.583-0.857), and 0.79 (95% CI 0.63-0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as - 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%. CONCLUSIONS: The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer. KEY POINTS: • The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer. • The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics. • The nomogram has good application prospects in assisting clinical decision makers.
OBJECTIVE: This study aims to establish and validate a radiomics nomogram based on contrast-enhanced spectral mammography (CESM) for prediction of axillary lymph node (ALN) metastasis in breast cancer. METHODS: This retrospective study included 394 patients with breast cancer who underwent CESM examination in two hospitals. The least absolute shrinkage and selection operator (LASSO) logistic regression was established for feature selection and utilized to construct radiomics signature. The nomogram model included the radiomics signature and independent clinical factors. The receiver operating characteristic (ROC) curves were used to confirm the performance of the nomogram in training and validation sets. RESULTS: The nomogram model, which includes the radiomics signature and the CESM-reported lymph node status, has areas under the ROC curves of 0.774 (95% confidence interval (CI) 0.689-0.858), 0.767 (95% CI 0.583-0.857), and 0.79 (95% CI 0.63-0.94) in the training, internal validation, and external validation sets, respectively. We identified the cutoff score in the radiomics nomogram as - 1.49, which corresponded to a total point of 49 that could diagnose ALN metastasis with a sensitivity of > 95%. CONCLUSIONS: The CESM-based radiomics nomogram is a noninvasive predictive tool that shows good application prospects in the preoperative prediction of ALN metastasis in breast cancer. KEY POINTS: • The CESM-based radiomics nomogram shows good performance in predicting ALN metastasis in breast cancer. • The application of radiomics nomogram in this study provides a new approach for establishing a prediction model with multiple characteristics. • The nomogram has good application prospects in assisting clinical decision makers.
Entities:
Keywords:
Breast cancer; Lymphatic metastasis; Mammography; Nomogram; Radiomics
Authors: Adrien Depeursinge; Antonio Foncubierta-Rodriguez; Dimitri Van De Ville; Henning Müller Journal: Med Image Anal Date: 2013-10-22 Impact factor: 8.545
Authors: Ning Mao; Yi Dai; Fan Lin; Heng Ma; Shaofeng Duan; Haizhu Xie; Wenlei Zhao; Nan Hong Journal: Front Oncol Date: 2020-10-27 Impact factor: 6.244