Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

Abstract

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

Keywords: CT; acute pancreatitis; artificial intelligence; classification; radiomics; segmentation.

Figure 1

Example of automatic segmentation of the pancreas.

Figure 2

Heatmap for unsupervised hierarchical clustering of standardized radiomics features in 30 randomly selected patients with acute pancreatitis and controls.

Figure 3

Feature selection using the random forest-based Boruta package. Confirmed features are shown in green, tentative features in yellow, rejected features in red and “shadow” features in blue. Circles show outliers and the whiskers indicate minimum and maximum.

Figure 4

Boxplots for the three most important features for comparison between patients with and without acute pancreatitis (AP, blue without AP, orange with AP; (A): shape_SurfaceVolumeRatio, (B): gldm_DependenceNonUniformity, (C): shape_MeshVolume). Data shown after feature normalization (z-score) for shape_surfaceVolumeRatio, gldm_DependenceNonUniformity and shape_MeshVolume.

Figure 5

Receiver operating characteristic (ROC) curves of the logistic regression models. Areas under the ROC curve (AUC) shown for performance of the models on a test set (n = 55) for radiomics only (A), for lipase only (B) and for a combined approach using radiomics and lipase (C).