Preoperative CT findings for prediction of resectability in patients with gallbladder cancer.

Abstract

To predict residual tumor (R) classification in patients with a surgery for gallbladder (GB) cancer, using preoperative CT. One hundred seventy-three patients with GB cancer who underwent CT and subsequent surgery were included. Two radiologists assessed CT findings, including tumor morphology, location, T stage, adjacent organ invasion, hepatic artery (HA) invasion, portal vein invasion, lymph node metastasis, metastasis, resectability, gallstone, and combined cholecystitis. The R classification was categorized as no residual tumor (R0) and residual tumor (R1 or R2). We analyzed the correlation between CT findings and R classification. We also followed up the patients as long as five years and analyzed the relationship between the R classification and the overall survival (OS). There were 134 patients with R0 and 39 patients with R1/R2. On multivariable analysis, liver invasion (Exp(B) = 3.19, p = 0.010), bile duct invasion (Exp(B) = 3.69, p = 0.031), and HA invasion (Exp(B) = 3.74, p = 0.039) were independent, significant predictors for residual tumor. When two of these three criteria were combined, the accuracy for predicting a positive resection margin was 83.38% with a specificity of 93.28%. The OS and the median patient survival time differed significantly according to the resection margin, i.e., 56.0% and 134.4months in the R0 resection and 5.1% and 10.8months in the R1/R2 resection group (p < 0.001). Preoperative CT findings could aid in planning surgery and determining the resectability using the high-risk findings of residual tumor, including liver invasion, bile duct invasion, and HA invasion. • Liver invasion, bile duct invasion, and HA invasion were significant preoperative CT predictors for residual tumor in GB cancer. • HA invasion showed the highest OR on multivariate analysis and the highest predictor point on a nomogram for predicting a positive resection margin. • Association of two factors can predict positive resection margin with an accuracy of 83.38% and a specificity of 93.28%.