Retrospective Analysis of the Value of Enhanced CT Radiomics Analysis in the Differential Diagnosis Between Pancreatic Cancer and Chronic Pancreatitis

Abstract

To investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis. Methods and materials:The CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features. The radiomics score was calculated, and the nomogram was established. The discrimination of each model was analyzed by the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was used to evaluate clinical utility. The precision recall curve (PRC) was used to evaluate whether the model is affected by data imbalance. The Delong test was adopted to compare the diagnostic efficiency of each model. Results: Significant differences were observed in the distribution of gender (P = 0.034), carbohydrate antigen 19-9 (P < 0.001), and carcinoembryonic antigen (P < 0.001) in patients with PC and chronic pancreatitis. The area under the ROC curve (AUC) value of AP multivariate regression model, VP multivariate regression model, AP combined with VP features model (Radiomics), clinical feature model, and radiomics combined with clinical feature model (COMB) was 0.905, 0.941, 0.941, 0.822, and 0.980, respectively. The sensitivity and specificity of the COMB model were 0.947 and 0.917, respectively. The results of DCA showed that the COMB model exhibited net clinical benefits and PRC shows that COMB model have good precision and recall (sensitivity). Conclusion:The COMB model could be a potential tool to distinguish PC from chronic pancreatitis and aid in clinical decisions.

Prospective assessment of pancreatic ductal adenocarcinoma diagnosis from endoscopic ultrasonography images with the assistance of deep learning

“…Although EUS has enhanced the diagnosis of pancreatic lesions to some extent, it is extremely dependent on the endosonographer's Currently, the feasibility of machine learning radiomics in diagnosing PDAC has been demonstrated using CT, MRI, PET-CT, and US images. [29][30][31][32][33][34][35] an AUC of 0.96. 34 Although growing numbers of studies have shown that machine learning-based radiomics models achieve good performance in identifying PDAC and benign pancreatic lesions, some common limitations remain unresolved.…”

“…Currently, the feasibility of machine learning radiomics in diagnosing PDAC has been demonstrated using CT, MRI, PET‐CT, and US images 29–35 . Liu et al.…”

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

“…By leveraging high-dimensional feature spaces, radiomics enables the discovery of complex patterns and correlations that may not be readily apparent through traditional visual inspection by physicians (or radiologists). Consequently, radiomics has shown promise [6–8,44] in improving diagnostic accuracy with an AUC of 0.7–0.8, depending on the specific dataset and implementation [9 ▪ ,10 ▪ –12 ▪ ,13 ▪▪ ,14 ▪ ].…”

“…A common practice in these studies is to use feature selection methods to identify the most relevant radiomics features for tumor classification. Ma et al [9 ▪ ] and Flammia et al [13 ▪▪ ] both utilized the least absolute shrinkage and selection operator (LASSO) method to select features that were then integrated into their respective models. LASSO is a linear regression analysis method often used in statistics and machine learning as a regularization strategy to prevent overfitting.…”

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

“…With the rapid development of medical imaging technologies, radiomics has begun to be used in the differential diagnosis of MFCP and PDAC (21)(22)(23)(24)(25)(26)(27). For example, Deng et al (24) studied 96 patients with PDAC and 23 patients with MFCP.…”