Novel silicon-based material decomposition images in diagnosis of pancreatic ductal adenocarcinoma: comparison with iodine-based and 50-keV virtual monoenergetic images.

Objectives: To identify the optimal material decomposition (MD) images for diagnosis of pancreatic ductal adenocarcinoma (PDAC) and evaluate the added value of the MD image to 50-keV virtual monoenergetic images (VMIs) by comparing with iodine-based images and 50-keV VMIs.

Methods: This retrospective study included patients who underwent pancreatic protocol dual-energy CT (DECT) between February 2019 and May 2023. First, a radiologist evaluated 702 image datasets generated using 27 different materials to identify the top three MD images which provided maximum contrast difference between normal pancreas and PDAC, and subsequently, the best MD image was selected based on z value and image quality by four radiologists. Then, another four radiologists independently interpreted the conventional image dataset (iodine-based images and 50-keV VMIs) and new optimal image dataset (optimal MD images and 50-keV VMIs), and graded the presence or absence of PDAC. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were compared between the two image datasets using generalized estimating equations.

Results: Overall, 110 patients (median age, 73 years; 63 men) were included. Among them, 67 patients (61%) had pathologically proven PDAC, and the optimal MD image selected was Silicon/Struvite. The optimal image dataset had higher specificity (88% vs. 81%; P = 0.006), PPV (93% vs. 89%; P < 0.001), and accuracy (94% vs. 92%; P = 0.01) than the conventional image dataset. No difference was found in the sensitivity (P = 0.34) and NPV (P = 0.33) between the two image datasets.

Conclusion: Silicon/Struvite images provided high contrast difference between normal pancreas and PDAC and higher diagnostic performance for diagnosis of PDAC in combination of 50-keV VMIs compared to iodine-based images and 50-keV VMIs.