Jinrong Qu1,2, Chen Shen2,3, Jianjun Qin4, Zhaoqi Wang1, Zhenyu Liu3, Jia Guo1, Hongkai Zhang1, Pengrui Gao1, Tianxia Bei1, Yingshu Wang1, Hui Liu1, Ihab R Kamel5, Jie Tian6,7, Hailiang Li8. 1. Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, 450003, Henan, China. 2. School of Life Science and Technology, XIDIAN University, Xi'an, 710126, Shaanxi, China. 3. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. 4. Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, 450003, Henan, China. 5. Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205-2196, USA. 6. School of Life Science and Technology, XIDIAN University, Xi'an, 710126, Shaanxi, China. jie.tian@ia.ac.cn. 7. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. jie.tian@ia.ac.cn. 8. Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, 450003, Henan, China. doctorhnchr@126.com.
Abstract
PURPOSE: To assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC). PATIENTS AND METHODS: A total of 181 EC patients were enrolled in this study between April 2015 and September 2017. Their LN metastases were pathologically confirmed. The first half of this cohort (90 patients) was set as the training cohort, and the second half (91 patients) was set as the validation cohort. A total of 1578 radiomic features were extracted from MR images (T2-TSE-BLADE and contrast-enhanced StarVIBE). The lasso and elastic net regression model was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to identify the radiomic signature of pathologically involved LNs. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC). The Mann-Whitney U test was adopted for testing the potential correlation of the radiomic signature and the LN status in both training and validation cohorts. RESULTS: Nine radiomic features were selected to create the radiomic signature significantly associated with LN metastasis (p < 0.001). AUC of radiomic signature performance in the training cohort was 0.821 (95% CI: 0.7042-0.9376) and in the validation cohort was 0.762 (95% CI: 0.7127-0.812). This model showed good discrimination between metastatic and non-metastatic lymph nodes. CONCLUSION: The present study showed MRI radiomic features that could potentially predict metastatic LN involvement in the preoperative evaluation of EC patients. KEY POINTS: • The role of MRI in preoperative staging of esophageal cancer patients is increasing. • MRI radiomic features showed the ability to predict LN metastasis in EC patients. • ICCs showed excellent interreader agreement of the extracted MR features.
PURPOSE: To assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC). PATIENTS AND METHODS: A total of 181 EC patients were enrolled in this study between April 2015 and September 2017. Their LN metastases were pathologically confirmed. The first half of this cohort (90 patients) was set as the training cohort, and the second half (91 patients) was set as the validation cohort. A total of 1578 radiomic features were extracted from MR images (T2-TSE-BLADE and contrast-enhanced StarVIBE). The lasso and elastic net regression model was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to identify the radiomic signature of pathologically involved LNs. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC). The Mann-Whitney U test was adopted for testing the potential correlation of the radiomic signature and the LN status in both training and validation cohorts. RESULTS: Nine radiomic features were selected to create the radiomic signature significantly associated with LN metastasis (p < 0.001). AUC of radiomic signature performance in the training cohort was 0.821 (95% CI: 0.7042-0.9376) and in the validation cohort was 0.762 (95% CI: 0.7127-0.812). This model showed good discrimination between metastatic and non-metastatic lymph nodes. CONCLUSION: The present study showed MRI radiomic features that could potentially predict metastatic LN involvement in the preoperative evaluation of EC patients. KEY POINTS: • The role of MRI in preoperative staging of esophageal cancerpatients is increasing. • MRI radiomic features showed the ability to predict LN metastasis in EC patients. • ICCs showed excellent interreader agreement of the extracted MR features.
Entities:
Keywords:
Esophageal cancer; Lymph nodes; Magnetic resonance imaging; Metastasis
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