Open AccessCorrelation between radiomic features based on contrast‐enhanced computed tomography images and Ki‐67 proliferation index in lung cancer: A preliminary study
Author(s) -
Zhou Bodong,
Xu Jie,
Tian Ye,
Yuan Shuai,
Li Xubin
Publication year - 2018
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.12821
Subject(s) - medicine , lung cancer , logistic regression , analysis of variance , correlation , contrast (vision) , nuclear medicine , multivariate analysis , radiology , mathematics , geometry , artificial intelligence , computer science
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.