Correlation Between Radiomic Features Based on Contrast-enhanced Computed Tomography Images and Ki-67 Proliferation Index in Lung Cancer: A Preliminary Study

Background: The purpose of the study was to investigate the association between radiomic features based on contrast-enhanced multidetector computed tomography (CT) and the Ki-67 proliferation index (PI) in patients with lung cancer.

Methods: One hundred and ten patients with lung cancer confirmed by surgical histology were retrospectively included. Radiomic features were extracted from preoperative contrast-enhanced chest multidetector CT images for each tumor using open-source three-dimensional Slicer software. Statistical analysis was performed to determine significant radiomic features serving as image predictors of Ki-67 status in lung cancer and to investigate the relationship between these features and Ki-67 PI.

Results: Higher Ki-67 expression was more common in men (P = 0.02) and patients with a smoking history (P = 0.01). Twelve radiomic features were significantly associated with Ki-67 status. Multivariate logistic regression analysis identified inverse variance, minor axis, and elongation as independent predictors of Ki-67 PI. There was a positive correlation between inverse variance, minor axis, elongation (P = 0.00, P = 0.02, and P = 0.14, respectively) and Ki-67 PI. The area under the curve to identify high Ki-67 status for inverse variance was 0.77 with a cutoff value of 0.47, which was significantly higher than for minor axis and elongation (P = 0.02 and P = 0.03, respectively).

Conclusion: Radiomic features based on contrast CT images, including inverse variance, minor axis, and elongation, can serve as noninvasive predictors of Ki-67 status in patients with lung cancer. Inverse variance could be superior to the other radiomic features to identify high Ki-67 status.