A study of radiomics parameters from dual-energy computed tomography images for lymph node metastasis evaluation in colorectal mucinous adenocarcinoma. Issue 11 (March 2020)

• Record Type: Journal Article
• Authors: Liu, Yingying; Dou, Yafang; Lu, Fang; Liu, Lei
• Other Names: Lalwani. Neeraj section editor.
• Is Part Of: Medicine. Volume 99:Issue 11(2020)
• Publication Date: 2020-03
• Subjects: colorectal mucinous adenocarcinoma -- dual-energy computed tomography -- logistic regression -- lymph nodes metastasis -- radiomics image parameters -- 2 sample t test; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000019251
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: Lymph nodes (LN) metastasis differentiation from computed tomography (CT) images is a challenging problem. This study aims to investigate the association between radiomics image parameters and LN metastasis in colorectal mucinous adenocarcinoma (MAC). Clinical records and CT images of 15 patients were included in this study. Among them, 1 patient was confirmed with all metastatic LNs, the other 14 were confirmed with all non-metastatic LNs. The regions of the LNs were manually labeled on each slice by experienced radiologists. A total of 1054 LN regions were obtained. Among them, 164 were from metastatic LNs. One hundred nine image parameters were computed and analyzed using 2-sample t test method and logistic regression classifier. Based on 2 sample t test, image parameters between the metastatic group and the non-metastatic group were compared. A total of 73 parameters were found to be significant ( P < .01). The selected shape parameters demonstrate that non-metastatic LNs tend to have smaller sizes and more circle-like shapes than metastatic LNs, which validates the common agreement of LN diagnosis using computational method. Besides, several high order parameters were selected as well, which indicates that the textures vary between non-metastatic LNs and metastatic LNs. The selected parameters of significance were further used to train logistic regression classifier with L1 penalty. Based on receiver operating characteristic (ROC) analysis, large area under curve (AUC) values were achieved over 5-fold cross validation (0.88 ± 0.06). Moreover, high accuracy, specificity, and sensitivity values were observed as well. The results of the study demonstrate that some quantitative image parameters are of significance in differentiating LN metastasis. Logistic regression classifiers showed that the parameters are with predictive values in LN metastasis, which may be used to assist preoperative diagnosis.