Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis.

PURPOSE

To develop a predictive model for liver metastases in patients with pancreatic ductal adenocarcinoma (PDAC) based on textural features of the primary tumor extracted by computed tomography (CT) images.

MATERIALS AND METHODS

Patients with a pathologically proved PDAC who underwent CT between December 2020 and January 2022 were retrospectively identified. Treatment-naïve patients were included. Sex, age, tumor size, vascular infiltration and 39 arterial and portal phase textural features were analyzed. The variables significantly correlated to tumor size according to the Pearson's product-moment correlation test were excluded from analysis; the remaining variables were compared between metastatic (M +) and non-metastatic (M-) patients using Fisher's or Mann-Whitney test. The features with a significant difference between groups were entered into a binomial logistic regression test to develop a predictive model for liver metastases.

RESULTS

This study included 220 patients. Eight variables (tumor size, arterial HU_MAX, arterial GLRLM_LRLGE, arterial GLZLM_SZHGE, arterial GLZLM_LZLGE, portal GLCM_CORRELATION, portal GLRLM_LRLGE, and portal GLZLM_SZHGE) were significantly different between groups. The logistic regression model was statistically significant (χ = 81.6, p < .001) and correctly classified 80.9% of cases. Sensitivity, specificity, positive and negative predictive values of the model were 58.6%, 91.3%, 75.9% and 82.5%, respectively. The area under the ROC curve of the model was 0.850 (95% CI, 0.793-0.907). Tumor size, arterial HU_MAX, arterial GLZLM_SZHGE and portal GLCM_CORRELATION were significant predictors of the likelihood of liver metastases, with odds ratios of 1.1, 0.9, 1, and 1.49, respectively.

CONCLUSIONS

CT texture analysis of PDAC can identify features that may predict the likelihood of liver metastases.