Improved Visualization of Gastric 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.