Differentiation of pulmonary mucosa-associated lymphoid tissue lymphoma and pulmonary adenocarcinoma by radiomics
10.3760/cma.j.issn.1005?1201.2018.10.009
- VernacularTitle:肺黏膜相关淋巴组织淋巴瘤与肺腺癌鉴别的影像组学研究
- Author:
Bin LIN
1
;
Tao WANG
;
Keren SHEN
;
Xiaojun XU
;
Quanquan GU
;
Xiaojun GUAN
;
Minming ZHANG
Author Information
1. 浙江大学医学院附属第二医院放射科
- Keywords:
Mucosa-associated lymphoid tissue;
Pulmonary adenocarcinoma;
Texture analysis;
Tomography;
X-ray computed
- From:
Chinese Journal of Radiology
2018;52(10):766-769
- CountryChina
- Language:Chinese
-
Abstract:
Objective To differentiate between pulmonary mucosa-associated lymphoid tissue lymphoma (MALT) and adenocarcinoma by radiomics, and then evaluate the diagnostic value of this novel approach. Methods We retrospectively analyzed CT images of pulmonary MALT lymphoma (n=16) and invasive pulmonary adenocarcinoma (n=41) and all these cases were confirmed by pathology in the Second Affiliated Hospital of Zhejiang University School of Medicine from June 2012 to June 2017. After we delineated the lesions as region of interest (ROI), sixty-one radiomics features were extracted from each individual's CT images by Radcloud 1.0. All cases in each group were randomly divided into training set (70％cases) and testing set(30％cases), with 7 features (Wilcoxon test) of which showed group differences and were used to train and validate a support vector machine (SVM) classifier. Results Seven of 61 radiomics features showed differences between the two groups, i.e. 10th percentile, mean, median, minimum, total energy, run length non uniformity, gray level non uniformity. Using these 7 features, the resulted SVM successfully differentiated two diseases. The SVM showed high performance with 90％precision, recall 0.89, F1-score 0.87, ROC 0.75. Conclusions Pulmonary MALT and adenocarcinoma differ in radiomics features and machine learning can utilize these features to differentiate between pulmonary MALT and adenocarcinoma. Combination of radiomics and machine learning is promising in the differential diagnosis of these two diseases.
