Hongna Tan1, Fuwen Gan2, Yaping Wu1, Jing Zhou1, Jie Tian3, Yusong Lin2, Meiyun Wang4. 1. Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, 7 Road, Weiwu Road, Jinshui District, Zhengzhou 450003, Henan, China. 2. Collaborative Innovation Center for Internet Healthcare & School of Information Engineering, Zhengzhou University, Zhengzhou 450052, Henan, China. 3. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. 4. Department of Radiology, Henan Provincial People's Hospital & Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province & People's Hospital of Zhengzhou University, 7 Road, Weiwu Road, Jinshui District, Zhengzhou 450003, Henan, China. Electronic address: mywang@ha.edu.cn.
Abstract
RATIONALE AND OBJECTIVES: To investigate the value of radiomics method based on the fat-suppressed T2 sequence for preoperative predicting axillary lymph node (ALN) metastasis in breast carcinoma. MATERIALS AND METHODS: The data of 329 invasive breast cancer patients were divided into the primary cohort (n = 269) and validation cohort (n = 60). Radiomics features were extracted from the fat-suppressed T2-weighted images on breast MRI, and ALN metastasis-related radiomics feature selection was performed using Mann-Whitney U-test and support vector machines with recursive feature elimination; then a radiomics signature was constructed by linear support vector machine. The predictive models were constructed using a linear regression model based on the clinicopathologic factors and radiomics signature, and nomogram was used for a visual prediction of the combined model. The predictive performances are evaluated with the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. RESULTS: A total of 647 radiomics features were extracted from each patient. About 23 ALN metastasis-related radiomics features were selected to construct the radiomics signature, including 17 texture features, 5 first-order statistical features, and one shape feature; patient age, tumor size, HER2 status, and vascular cancer thrombus accompanied or not were selected to construct the cilinicopathologic feature model. The sensitivity, specificity, accuracy, and are under the curve value of radiomics signature, clinicopathologic feature model, and the nomogram were 65.22%, 81.08%, 75.00%, and 0.819 (95% confidence interval [CI]: 0.776-0.861), 30.44%, 81.08%, 61.67%, and 0.605 (95% CI: 0.571-0.624) and 60.87%, 89.19%, 78.33%, and 0.810 (95% CI: 0.761-0.855), respectively. CONCLUSION: Radiomics methods based on the fat-suppressed T2 sequence and the nomogram are helpful for preoperative accurate predicting ALN metastasis.
RATIONALE AND OBJECTIVES: To investigate the value of radiomics method based on the fat-suppressed T2 sequence for preoperative predicting axillary lymph node (ALN) metastasis in breast carcinoma. MATERIALS AND METHODS: The data of 329 invasive breast cancerpatients were divided into the primary cohort (n = 269) and validation cohort (n = 60). Radiomics features were extracted from the fat-suppressed T2-weighted images on breast MRI, and ALN metastasis-related radiomics feature selection was performed using Mann-Whitney U-test and support vector machines with recursive feature elimination; then a radiomics signature was constructed by linear support vector machine. The predictive models were constructed using a linear regression model based on the clinicopathologic factors and radiomics signature, and nomogram was used for a visual prediction of the combined model. The predictive performances are evaluated with the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. RESULTS: A total of 647 radiomics features were extracted from each patient. About 23 ALN metastasis-related radiomics features were selected to construct the radiomics signature, including 17 texture features, 5 first-order statistical features, and one shape feature; patient age, tumor size, HER2 status, and vascular cancer thrombus accompanied or not were selected to construct the cilinicopathologic feature model. The sensitivity, specificity, accuracy, and are under the curve value of radiomics signature, clinicopathologic feature model, and the nomogram were 65.22%, 81.08%, 75.00%, and 0.819 (95% confidence interval [CI]: 0.776-0.861), 30.44%, 81.08%, 61.67%, and 0.605 (95% CI: 0.571-0.624) and 60.87%, 89.19%, 78.33%, and 0.810 (95% CI: 0.761-0.855), respectively. CONCLUSION: Radiomics methods based on the fat-suppressed T2 sequence and the nomogram are helpful for preoperative accurate predicting ALN metastasis.
Authors: Simon J Doran; Santosh Kumar; Matthew Orton; James d'Arcy; Fenna Kwaks; Elizabeth O'Flynn; Zaki Ahmed; Kate Downey; Mitch Dowsett; Nicholas Turner; Christina Messiou; Dow-Mu Koh Journal: Cancer Imaging Date: 2021-05-20 Impact factor: 3.909