Enhancing pancreatic tumor delineation using dual-energy CT-derived extracellular volume fraction map.

Abstract

Precise identification of pancreatic tumors is challenging for radiotherapy planning due to the anatomical variability of the tumor and poor visualization of the tumor on 3D cross-sectional imaging. Low extracellular volume fraction (ECVf) correlates with poor vasculature uptake and possible necrosis or hypoxia in pancreatic tumors. This work investigates the feasibility of delineating pancreatic tumors using ECVf spatial distribution maps derived from contrast enhanced dual-energy CT (DECT). Data acquired from radiotherapy simulation of 12 pancreatic cancer patients, using a dual source DECT scanner, were analyzed. For each patient, an ECVf distribution of the pancreas was computed from the simultaneously acquired low and high energy DECT series during the late arterial contrast phase combined with the patient's hematocrit level. Volume of interest (VECVf) maps in ECVf distribution of pancreas were identified by applying an appropriate threshold condition and a connected components clustering algorithm. The obtained VECVf was compared with the clinical gross tumor volume (GTV) using the positive predictive value (PPV), Dice similarity coefficient (DSC), mean distance to agreement (MDA) and true positive rate (TPR). As a proof of concept, our hypothetical threshold condition based on the first quartile separation of the ECVf distribution to find VECVf of the pancreas elucidates the tumor volume within the pancreas. Notably, 7 out of 12 cases studied for VECVf matched well with the GTV and the mean PPV of 0.83±0.12. The mean MDA (2.83±1.0) of the cases confirms that VECVf lies within the tolerance for comparing to the pancreatic GTV. For the remaining 5 cases, the VECVf is substantially affected by other compounding factors, e.g., large cysts, dilate ducts, and thus did not align with the GTVs. This work demonstrated the promising application of the ECVf map, derived from contrast enhanced DECT, to help delineate tumor target for RT planning of pancreatic cancer.