Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20+ B cells in patients with pancreatic ductal adenocarcinoma

Abstract

Purpose: To develop and validate a machine-learning classifier based on contrast-enhanced computed tomography (CT) for the preoperative prediction of CD20⁺ B lymphocyte expression in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods: Overall, 189 patients with PDAC (n = 132 and n = 57 in the training and validation sets, respectively) underwent immunohistochemistry and radiomics feature extraction. The X-tile software was used to stratify them into groups with 'high' and 'low' CD20⁺ B lymphocyte expression levels. For each patient, 1409 radiomic features were extracted from volumes of interest and reduced using variance analysis and Spearman correlation analysis. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. Model performance was determined by its discriminative ability, calibration, and clinical utility.

Results: A log-rank test showed that the patients with high CD20⁺ B expression had significantly longer survival than those with low CD20⁺ B expression. The prediction model showed good discrimination in both the training and validation sets. For the training set, the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.82 (95% CI 0.74-0.89), 92.42%, 57.58%, 0.75, 0.69, and 0.88, respectively; whereas these values for the validation set were 0.84 (95% CI 0.72-0.93), 86.21%, 78.57%, 0.83, 0.81, and 0.85, respectively.

Conclusion: The MLP network classifier based on contrast-enhanced CT can accurately predict CD20⁺ B expression in patients with PDAC.

Keywords: B-lymphocytes; Contrast-enhanced computed tomography; Pancreatic ductal adenocarcinoma; Prognosis; Radiomics.