Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes

Purpose: To evaluate a radiomics model for predicting lymph node (LN) metastasis in biliary tract cancers (BTCs) and to determine its prognostic value for disease-specific and recurrence-free survival. Materials and Methods: For this retrospective study, a radiomics model was developed on the basis of a primary cohort of 177 patients with BTC who underwent resection and LN dissection between June 2010 and December 2016. Radiomic features were extracted from portal venous CT scans. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator method. Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 70 consecutive patients with BTC between January 2017 and February 2018. Results: The radiomics signature, composed of three LN-status-related features, was associated with LN metastasis in primary and validation cohorts (P<.001). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status showed good calibration and discrimination in primary cohort (area under the curve, 0.81) and validation cohort (area under the curve, 0.80). Patients at high risk of LN metastasis portended lower disease-specific and recurrence-free survival than did those at low risk after surgery (both P<.001). High-risk LN metastasis was an independent preoperative predictor of disease-specific survival (hazard ratio, 3.37; P<.001) and recurrence-free survival (hazard ratio, 1.98; P=.003). Conclusion: A radiomics model derived from portal phase CT of the liver has good performance for predicting lymph node metastasis in biliary tract cancer and may help to improve clinical decision making. (c) RSNA, 2018