Virtual Monoenergetic Imaging Predicting Ki-67 Expression in Lung Cancer: Assessment of Optimum Slope and Energy Levels using Energy Spectrum Curve

Abstract Objective: This study aimed to optimize slope and energy levels for evaluating Ki-67 expression in lung cancer using virtual monoenergetic imaging and compare the predictive efficiency of different energy spectrum slopes for Ki-67. Material and Methods: Forty-three patients with primary lung cancer confirmed via pathological examination were enrolled in this study. They underwent baseline arterial-phase (AP) and venous-phase (VP) energy spectrum computed tomography (CT) scanning before surgery. The CT values were 40–190 keV, with 40–140 keV indicating pulmonary lesions at AP and VP, and P ＜ 0.05 indicating a statistically significant difference. An immunohistochemical examination was conducted and receiver operating characteristic curves were used to analyze the prediction performance of λHU for Ki-67 expression. SPSS Statistics 22.0 (IBM Corp., NY, USA) was used for statistical analysis, and x2, t and signed-rank test were used for quantitative and qualitative analyses of data. Results:Significant differences were reported in the corresponding CT values at 40 (40 keV is considered the best single-energy image for evaluating Ki-67 expression) and 50 keV in AP and at 40, 60, and 70 keV in VP between high- and low-Ki-67 expression groups (P < 0.05), and in λHU of the three-segment energy spectrum curve, in both AP and VP (P < 0.05). However, the VP data had greater predictive values for Ki-67; The areas under the curve were 0.859, 0.856, and 0.859, respectively. Conclusions: The 40-keV single-energy sequence was the best single-energy sequence to evaluate the expression of Ki-67 in lung cancer and to measure λHU of the energy spectrum curve in the venous phase. The CT values had better diagnostic efficiency.