Lu Han1,2, Yongbei Zhu3, Zhenyu Liu3,4, Tao Yu1,2, Cuiju He1,2, Wenyan Jiang1,2, Yangyang Kan1,2, Di Dong5,6, Jie Tian3,4,7, Yahong Luo8,9. 1. Cancer Hospital of China Medical University, Shenyang, 110042, China. 2. Liaoning Cancer Hospital & Institute, Shenyang, 110042, China. 3. CAS Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China. 4. University of Chinese Academy of Sciences, Beijing, 100049, China. 5. CAS Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China. di.dong@ia.ac.cn. 6. University of Chinese Academy of Sciences, Beijing, 100049, China. di.dong@ia.ac.cn. 7. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China. 8. Cancer Hospital of China Medical University, Shenyang, 110042, China. Luoyahong8888@hotmail.com. 9. Liaoning Cancer Hospital & Institute, Shenyang, 110042, China. Luoyahong8888@hotmail.com.
Abstract
OBJECTIVE: To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients. METHODS: Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram. RESULTS: The radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79). CONCLUSIONS: We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients. KEY POINTS: • ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.
OBJECTIVE: To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancerpatients. METHODS: Preoperative magnetic resonance imaging data from 411 breast cancerpatients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram. RESULTS: The radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79). CONCLUSIONS: We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancerpatients. KEY POINTS: • ALNM is an important factor affecting breast cancerpatients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN 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