Development of a radiomics signature to predict Ki-67 expression level in non-small cell lung cancer.
10.11817/j.issn.1672-7347.2018.11.008
- Author:
Qianbiao GU
1
;
Zhichao FENG
1
;
Qi LIANG
1
;
Meijiao LI
1
;
Wei WANG
1
;
Pengfei RONG
1
Author Information
1. Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
- Publication Type:Journal Article
- MeSH:
Carcinoma, Non-Small-Cell Lung;
diagnostic imaging;
Gene Expression Regulation, Neoplastic;
Humans;
Ki-67 Antigen;
genetics;
Lung Neoplasms;
diagnostic imaging;
Prognosis;
Retrospective Studies;
Tomography, X-Ray Computed
- From:
Journal of Central South University(Medical Sciences)
2018;43(11):1216-1222
- CountryChina
- Language:Chinese
-
Abstract:
To develop a radiomics signature based on CT image features to estimate the expression level of Ki-67 in non-small cell lung cancer (NSCLC).
Methods: A total of 108 NSCLC patients, who underwent non-enhanced and contrast-enhanced CT scan in our hospital from January 2014 to November 2017, were retrospectively analyzed. They were confirmed by histopathological examination and undergone Ki-67 expression level test within 2 weeks after CT examination. The non-enhanced and contrast-enhanced CT three-dimensional structural images of the lesions were manually delineated by MaZda software, and the texture features of the region of interest were extracted. Combination of feature selection and classification methods were used to build radiomics signatures, and the classification were assessed using misclassification rates. The MaZda software provides texture feature selection methods including mutual information (MI), Fisher coefficients (Fisher), classification error probability combined with average correlation coefficients (POE+ACC), and Fisher+POE+ACC+MI (FPM), and texture feature analysis including raw data analysis (RDA), principal component analysis (PCA), linear classification analysis (LDA) and nonlinear classification analysis (NDA).
Results: Among the 108 patients, 50 cases were at high levels of Ki-67 expression and 58 cases were at low levels of Ki-67 expression, respectively. The differences of gender, age and pathological type between the two groups were statistically significant (P<0.05). The radiomics signature built by FPM feature selection combined with NDA feature analysis based on non-enhanced CT images achieved the best performance for predicting the level of Ki-67 with a misclassification rate of 14.81%. However, radiomics signature based on contrast-enhanced CT images did not reduce the misclassification rate.
Conclusion: The radiomics signature based on conventional CT image texture features is helpful to predict the expression of Ki-67 in NSCLC lesions, which can provide a non-invasive technique for assessing the invasiveness and prognosis for NSCLC.
