CT image feature-based analysis on the prognostic factors of lung adenocarcinoma(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2019年第3期
- Page:
- 291-295
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- CT image feature-based analysis on the prognostic factors of lung adenocarcinoma
- Author(s):
- LU Xiaoteng; GONG Jing; NIE Shengdong
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200082, China
- Keywords:
- ; Keywords: lung adenocarcinoma; prognosis; image feature; independent prognostic factor
- PACS:
- R318；R734.2
- DOI:
- DOI:10.3969/j.issn.1005-202X.2019.03.009
- Abstract:
- Abstract: Objective To propose a method for the analysis of the prognostic factors of lung adenocarcinoma based on CT image features, and explore the effects of different kinds of CT image features on the prognosis of lung adenocarcinoma. Methods Firstly, the lung tumors were segmented and their features were extracted. Secondly, Kaplan-Meier method was used to perform univariate survival analysis, and a multivariate survival analysis was carried out with COX regression model to obtain independent prognostic factors. Finally, a classifier based on support vector machine was established to test the prognostic ability of independent prognosis factors. Results The data of 61 patients with lung adenocarcinoma were selected form Lung CT-Diagnosis dataset. The univariate analysis showed that several image features, including radial variance, edge roughness, GLCM entropy and GLCM non-similarity, had significant effects on the overall survival of patients with lung adenocarcinoma (P<0.05). The multivariate analysis based on COX regression model revealed that only radial variance was significantly associated with the survival of patients with lung adenocarcinoma (P<0.05). The result of classifier based on support vector machine showed that to some extent, using radial variance could predict the survival time of patients. Conclusion Four features, namely radial variance, edge roughness, GLCM entropy and GLCM non-similarity, are proved to be associated with the prognosis of patients with lung adenocarcinoma. Moreover, radial variance is regarded as the independent prognostic factor of lung adenocarcinoma. The analysis of the above image features can provide doctors with more accurate prognosis which is helpful for prolonging the survival time of patients with lung adenocarcinoma.
Last Update: 2019-03-25