The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer

• Published in: European radiology 2019-02, Vol.29 (2), p.906-914
• Main Authors: Qu, Jinrong, Shen, Chen, Qin, Jianjun, Wang, Zhaoqi, Liu, Zhenyu, Guo, Jia, Zhang, Hongkai, Gao, Pengrui, Bei, Tianxia, Wang, Yingshu, Liu, Hui, Kamel, Ihab R., Tian, Jie, Li, Hailiang
• Format: Article
• Language: English
• Subjects: Cancer; Case-Control Studies; Chest; Diagnostic Radiology; Esophageal cancer; Esophageal Neoplasms - diagnostic imaging; Esophageal Neoplasms - pathology; Esophagus; Feature extraction; Humans; Image contrast; Image enhancement; Image Interpretation, Computer-Assisted; Imaging; Internal Medicine; Interventional Radiology; Lymph nodes; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Lymphatic Metastasis; Lymphatic system; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Metastases; Metastasis; Middle Aged; Neuroradiology; Patients; Prospective Studies; Radiology; Radiomics; Regression analysis; Regression models; Retrospective Studies; ROC Curve; Tomography, X-Ray Computed - methods; Training; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-018-5583-z

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.