Radiomics based on diffusion-weighted imaging for differentiation between focal-type autoimmune pancreatitis and pancreatic carcinoma

Objective: To evaluate the parameters of support vector machine (SVM) using imaging data generated from the apparent diffusion coefficient (ADC) to differentiate between focal-type autoimmune pancreatitis (f-AIP) and pancreatic ductal adenocarcinoma (PDAC) when using SVM based on diffusion-weighted imaging. Methods: The 2D-ADC mean and texture parameters (16 texture features × [non-filter+17 filters]) were retrospectively segmented by 2 readers in 28 patients with f-AIP and 77 patients with pathologically proven PDAC. The diagnostic accuracy of the SVM model was evaluated by receiver operating characteristic curve analysis and calculation of the area under the curve (AUC). Interreader reliability was assessed by intraclass correlation coefficient (ICC). Results: The 2D-ADC mean and 3D-ADC mean were significantly lower in cases of f-AIP (1.10–1.15 × 10 –3 mm 2 /s and 1.21–1.23× 10 –3 mm 2 /s, respectively) vs PDAC (1.29–1.33 × 10 –3 mm 2 /s and 1.41–1.43 × 10 –3 mm 2 /s, respectively), with excellent and good interreader reliability, respectively (ICC = 0.909 and 0.891, respectively). Among the texture parameters, energy with exponential filtering yielded the highest AUC (Reader 1: 74.7%, Reader 2: 81.5%), with fair interreader reliability (ICC = 0.707). The non-linear SVM, a combination of 2D-ADC mean , object volume and exponential-energy showed an AUC value of 96.2% in the testing cohorts. Conclusion: Our results suggest that non-linear SVM using a combination of 2D-ADC mean , object volume, and exponential-energy may assist in differentiating f-AIP from PDAC. Advances in knowledge: The radiomics based on an apparent diffusion coefficient value may assist in differentiating f-AIP from PDAC.