Cancer and Increased Diagnostic Performance in Lesion Depiction and Depth Identification Using Monoenergetic Reconstructions from a Novel Dual-Layer Spectral Detector CT

Rationale and Objectives: To determine the optimal keV for the visualization of gastric cancer and to investigate its value in depicting lesions and in identifying depth invasion using virtual monoenergetic images (VMIs) on a novel dual-layer spectral detector CT. Materials and Methods: Eighty-two gastric cancer patients were retrospectively enrolled, and 41 patients who did not undergo surgery were evaluated for image quality in VMIs at different keVs (40 keV-70 keV with 10 keV increments) and in conventional 120 kVp polyenergetic images (PEIs) reconstructed from the portal venous phase. Objective image quality was assessed by the contrast-to-noise ratio of the gastric cancer, while subjective performance was compared using a 5-point Likert scale. Another 41 patients who underwent surgery were examined to compare the diagnostic performance of the VMIs taken at the optimal keV and that of the 120 kVp-PEIs. Results: The contrast-to-noise ratio of gastric cancer at 40 keV (10.4 +/- 4.6) was the highest among all the VMIs and was significantly superior to that of the 120 kVp-PEIs (3.5 +/- 1.5, p < 0.001). Gastric-specific image quality was rated highest for the 40 keV-VMIs (4.92 +/- 0.26), which was significantly superior to that of the 120 kVp-PEIs (4.15 +/- 0.82, p < 0.001). In the diagnostic group, there were 13 pT1, 10 pT2, 9 pT3, and 9 pT4 gastric cancer patients. Compared with the 120 kVp-PEIs, the VMIs at 40 keV tended to have a higher detection rate of gastric cancer (82.9% vs. 92.7%, respectively, p = 0.125) and a significantly improved diagnostic accuracy in the T stage (from 41.5% to 78.11%, respectively) (p < 0.001), particularly in pT1 patients, whose diagnostic accuracy was improved by 53.8% (7.7% vs. 61.5%, respectively, p = 0.016). Conclusion: VMIs at 40 keV performed the best, both objectively and subjectively, for gastric cancer, leading to improved lesion depiction and higher T stage accuracy.