Identification of invasive lung adenocarcinoma and non-calcified lung tuberculoma on plain CT images based on texture analysis
10.13929/j.issn.1003-3289.2020.04.015
- VernacularTitle: 基于CT平扫图像纹理分析鉴别浸润性肺腺癌与非钙化结核球
- Author:
Liyou HUANG
1
Author Information
1. Department of Oncology, Suqian Hospital Affiliated to Xuzhou Medical University, Suqian People's Hospital of Nanjing Drum-Tower Hospital Group
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Diagnosis;
Lung neoplasms;
Radiomics;
Texture analysis;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2020;36(4):545-549
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the feasibility of differential diagnosis of invasive lung adenocarcinoma and non-calcified lung tuberculoma on CT plain images based on texture analysis. Methods: Data of plain CT images of 52 patients with single pulmonary nodules confirmed pathologically were retrospectively analyzed, including 31 cases of invasive lung adenocarcinoma and 21 cases of non-calcified lung tuberculosis. Totally 300 texture features of each kind of lesions were extracted with MaZda software, then 10 optimized texture parameters were selected for texture analysis with fisher coefficient (Fisher), minimization of both probability of classification error and average correction coefficient (POE+ACC), mutual information coefficients (MI) methods, respectively, and the optimal texture features combination combined with three methods (MPF) was obtained. The four groups of optimal texture characteristics were classified using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA), while classification of LDA and NDA were performed using K-nearest neighbor classifier (K-NN) and artificial neural network (ANN), respectively. The minimum error probability of 4 groups of texture features in differential diagnosing of 2 kinds of lesions was analyzed, the differences of 30 optimal texture features were compared between 2 kinds of lesions, their ROC curves for identifying 2 kinds of lesions were drawn, and then AUC of the curves were calculated to evaluate their diagnostic performance. Results: For single group of optimal texture features, NDA/ANN-Fisher method had the lowest error rate (7.69% [4/52]), while for MPF, the error rate of NDA/ANN-MPF was the lowest (5.77% [3/52]). There was no statistical difference of error rate between NDA/ ANN-Fisher and NDA/ ANN-MPF method (χ2=0.15, P>0.05). Statistical differences of 10 optimal texture features were noticed between 2 kinds of lesions, among which difference entropy S(1,1), difference variance S(1,1) and gradient variance had good diagnostic efficacy (AUC=0.71, 0.71, 0.70), and their AUC were not statistically different (all P>0.05). Conclusion: Based on texture analysis of plain CT images, invasive lung adenocarcinoma and non-calcified lung tuberculosis can be well distinguished, providing objective and reliable basis for differential diagnosis of these two lesions.
