Recurrence After Resection of Pancreatic Cancer: Can Radiomics Predict Patients at Greatest Risk of Liver Metastasis?

Abstract

Background: Liver metastasis (LM) after pancreatic ductal adenocarcinoma (PDAC) resection is common but difficult to predict and has grave prognosis. We combined preoperative clinicopathological variables and quantitative analysis of computed tomography (CT) imaging to predict early LM.

Methods: We retrospectively evaluated patients with PDAC submitted to resection between 2005 and 2014 and identified clinicopathological variables associated with early LM. We performed liver radiomic analysis on preoperative contrast-enhanced CT scans and developed a logistic regression classifier to predict early LM (< 6 months).

Results: In 688 resected PDAC patients, there were 516 recurrences (75%). The cumulative incidence of LM at 5 years was 41%, and patients who developed LM first (n = 194) had the lowest 1-year overall survival (OS) (34%), compared with 322 patients who developed extrahepatic recurrence first (61%). Independent predictors of time to LM included poor tumor differentiation (hazard ratio (HR) = 2.30; P < 0.001), large tumor size (HR = 1.17 per 2-cm increase; P = 0.048), lymphovascular invasion (HR = 1.50; P = 0.015), and liver Fibrosis-4 score (HR = 0.89 per 1-unit increase; P = 0.029) on multivariate analysis. A model using radiomic variables that reflect hepatic parenchymal heterogeneity identified patients at risk for early LM with an area under the receiver operating characteristic curve (AUC) of 0.71; the performance of the model was improved by incorporating preoperative clinicopathological variables (tumor size and differentiation status; AUC = 0.74, negative predictive value (NPV) = 0.86).

Conclusions: We confirm the adverse survival impact of early LM after resection of PDAC. We further show that a model using radiomic data from preoperative imaging combined with tumor-related variables has great potential for identifying patients at high risk for LM and may help guide treatment selection.

Figure 1.

Schematic representation of patient cohort and recurrence patterns. EH, extra-hepatic; NED, no evidence of disease recurrence.

Figure 2.

(a) Cumulative incidence of liver metastasis (LM). (b) Cumulative incidence of the 3 main recurrence patterns. (c) Overall survival of entire cohort. (d,) Overall survival (OS) after first recurrence, stratified by recurrence patterns. EH-F, extrahepatic first; LM-F, liver metastasis first; NED, no evidence of disease recurrence.

Figure 3.

(a) Patients with tumors of the body/tail of the pancreas (distal tumors) underwent distal pancreatectomy (n=135), whereas those with tumors of the uncinate process, head or neck of the pancreas (proximal tumors) underwent pancreaticoduodenectomy, central, or total pancreatectomy (n=553). The latter were classified based on the presence of biliary obstruction (BO) and biliary drainage (BD) procedures in 3 subgroups. (b) The 4 subgroups were compared for time to liver metastasis. Results of univariate analysis are presented as hazard ratios [95% confidence interval] for each column group (bold) compared to each row group.

Figure 4.

CT radiomic analysis for discrimination of patients at high risk of early liver metastasis. (a) Digital images acquired using standard pancreas protocol contrast-enhanced CT were imported to the PathFinder software and the liver parenchyma was segmented. Each pixel’s gray level intensity was expressed on a numerical scale, and pixels were compared either altogether or one by one based on their spatial relationship to develop 254 radiomic “features” for each patient. (b) Receiver operating curve (ROC) for detection of patients with early liver metastasis (LM <6 months after resection) using a radiomics-based prediction model with 6 features that differed significantly between patients with early LM (<6 months) vs. without LM at 6 months in the training set and a validation set. Performance of the imaging, clinical (using tumor size and differentiation; one patient was missing tumor differentiation status and was removed from this comparison), and combined imaging and clinical prediction models; sensitivity (Sens), specificity (Spec), positive predictive value (PPV), negative predictive value (NPV), and area under the ROC (AUC) are presented as with the 95% confidence interval.