The value of spectral CT quantitative parameters in predicting Ki-67 level in ovarian cancer - Pang, Siwen; Wu, Meng; Yu, Haijia; Ma, Tiantian; Liu, Jianhua; Liu, Siwen (Frontiers in Oncology, 2025)
ObjectiveThe objective of this study is to evaluate the predictive value of quantitative parameters from spectral CT for Ki-67 expression in ovarian cancer (OC).MethodsSpectral CT imaging data from 39 patients with ovarian cancer by pathology, encompassing 52 lesions overall, were collected retrospectively and split into two groups based on immunohistochemical results. Tumor solid components in arterial, venous, and delayed phases can be measured using post-processing software to obtain the quantitative parameters of spectral CT. An independent sample t-test was implemented for evaluating spectral CT parameters between two groups, and a Spearman correlation coefficient was applied among all participants to estimate the relationship between spectral parameters and Ki-67 levels. Moreover, an examination of the receiver operating characteristic (ROC) curve was conducted to assess the diagnostic efficacy of the significantly different parameters between the two groups.ResultsThe Ki-67 high-level group includes 22 patients and 29 lesions, while the Ki-67 low-level group contains 17 patients and 23 lesions. The A-sIC, A-sZeff, V-IC, D-Zeff, and D-sZeff values in the Ki-67 high-level group were greater than those in the Ki-67 low-level group (P =0.028, AUC = 0.705; P < 0.001, AUC = 0.742; P = 0.047, AUC = 0.657; P = 0.014, AUC = 0.665; and P = 0.006, AUC = 0.675, respectively). For correlation analysis, A-IC, A-sIC, A-Zeff, A-sZeff, A-λHU, D-IC, D-Zeff, and D-sZeff were positively correlated with Ki-67 levels, with correlation coefficients ranging from 0.277 to 0.417, P<0.05. Through multiple logistic regression, the combined model that included 5 quantitative parameters showed the highest diagnostic performance, with a sensitivity of 93.10%, a specificity of 60.90%, and an AUC value of 0.808.ConclusionSpectral CT provides multi-parametric imaging data and is useful in predicting Ki-67 expression in ovarian cancer, delivering comprehensive and reliable imaging evidence for the formulation of therapeutic treatment options.