Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis

Rouzbeh Mashayekhi, Vishwa S Parekh, Mahya Faghih, Vikesh K Singh, Michael A Jacobs, Atif Zaheer, Rouzbeh Mashayekhi, Vishwa S Parekh, Mahya Faghih, Vikesh K Singh, Michael A Jacobs, Atif Zaheer

Abstract

Purpose: Patients with recurrent abdominal pain and pancreatic enzyme elevations may be diagnosed clinically with recurrent acute pancreatitis (RAP) even with normal imaging or no imaging at all. Since neither abdominal pain nor enzyme elevations are specific for acute pancreatitis (AP), and patients with RAP often have a normal appearing pancreas on CT after resolution of an AP episode, RAP diagnosis can be challenging. This study aims to determine if quantitative radiomic features of the pancreas on CT can differentiate patients with functional abdominal pain, RAP, and chronic pancreatitis (CP).

Method: Contrast enhanced CT abdominal images of adult patients evaluated in a pancreatitis clinic from 2010 to 2018 with the diagnosis of RAP, functional abdominal pain, or CP were retrospectively reviewed. The pancreas was outlined by drawing region of interest (ROI) on images. 54 radiomic features were extracted from each ROI and were compared between the patient groups. A one-vs-one Isomap and Support Vector Machine (IsoSVM) classifier was also trained and tested to classify patients into one of the three diagnostic groups based on their radiomic features.

Results: Among the study's 56 patients, 20 (35.7 %) had RAP, 19 (33.9 %) had functional abdominal pain, and 17 (30.4 %) had CP. On univariate analysis, 11 radiomic features (10 GLCM features and one NGTDM feature) were significantly different between the patient groups. The IsoSVM classifier for prediction of patient diagnosis had an overall accuracy of 82.1 %.

Conclusions: Certain radiomic features on CT imaging can differentiate patients with functional abdominal pain, RAP, and CP.

Keywords: Abdomen; CT; Chronic pancreatitis; Machine learning; Pancreas; Pancreatitis; Radiomics; Recurrent acute pancreatitis.

Conflict of interest statement

Declaration of Competing Interest

Vikesh Singh is a consultant to Abbvie, Ariel Precision Medicine, Orgenesis, and Akcea Therapeutics. Other authors have no disclosures. Other authors have no relevant conflict of interest.

Copyright © 2019 Elsevier B.V. All rights reserved.

Figures

Fig. 1.. Sample selection.
Fig. 1.. Sample selection.
Flow diagram demonstrating selection of the study samples.
Fig. 2.. Image Analysis.
Fig. 2.. Image Analysis.
To generate radiomic features, general steps include 1) acquisition of the radiologic images, 2) identification of the regions of interest on the acquired images, 3) extraction of radiomic features from the regions of interests.
Fig. 3.. CT and radiomic sample images…
Fig. 3.. CT and radiomic sample images based on diagnosis.
For each diagnostic group, each CT slice containing the pancreas (left image) was used to generate the radiomic based image on the right after drawing the region of interest delineating the pancreas (shown with red outline). The radiomic feature in this figure is the GLCM entropy feature, which was found to be significant among all three patient group comparisons.
Fig. 4.. The Receiver Operating Characteristics Curve…
Fig. 4.. The Receiver Operating Characteristics Curve for ISO-SVM Model.
Each colored line represents the ROC curves created using the 11 significant radiomic features according to the clinical diagnosis.

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