Development and validation of a clinical prediction model to estimate the probability of malignancy in solid pancreatic lesions and explore its value in the atypical diagnostic category after endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNA)

Abstract

Background: The diagnosis of solid pancreatic lesions is still a thorny problem for clinicians, even endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNA) still face problems like false negative. The present study aimed to first establish a model to predict the malignancy in solid pancreatic lesions and then explored its validity with atypical diagnostic category diagnosed by cytologists after EUS-FNA.

Methods: Clinical information of 360 cases diagnosed with solid pancreatic lesions between June 2013 and July 2019, and another 20 cases with atypical diagnostic category were collected retrospectively. These cases were divided into group A and group B according to the order of admission. Using the data of group A, multivariate logistic regression analysis was performed to construct a malignancy prediction model which was then verified using group B. Furthermore, the characteristics of the malignancy between the group with atypical diagnostic category and group A were compared in order to evaluate the rationality of the model used in the atypical diagnostic category group, and its predictive ability in these lesions.

Results: Multivariate logistic regression analysis revealed that age, density, CA19-9 and carcinoembryonic antigen (CEA) grade, pancreatic duct, swollen lymph nodes, pancreas calcification, and weight loss were independent factors in predicting malignancy (P<0.05). The verification results showed that the area under the receiver operating characteristic (ROC) curve was 0.854±0.042; 95% CI: 0.771-0.936. Univariate analysis showed no significant difference between the malignancy in atypical diagnostic category group and that in group A. For the atypical diagnostic category group, the sensitivity of this model was 83.33%, specificity 100%, positive predictive value (PPV) 100%, negative predictive value (NPV) 40%.

Conclusions: Advanced age, low density of lesions, high CA19-9 and CEA grade, dilatation of pancreatic duct, swollen lymph nodes and weight loss were risk factors for malignancy, while calcification was a protective factor. The model had a relatively high predictive ability on malignancy in both solid pancreatic lesions and atypical diagnostic category group.

Keywords: Solid pancreatic lesions; atypical diagnostic category; logistic regression analysis; prediction model.