Radiomics features of ascending and descending nasopharyngeal carcinoma.
10.11817/j.issn.1672-7347.2020.190114
- Author:
Jiajia YAO
1
;
Pei YANG
2
;
Lina ZHAO
3
;
Hekun JIN
4
;
Xiaoxue XIE
2
;
Jingru YANG
2
;
Fan LOU
2
;
Rong ZHANG
2
;
Zi XU
2
;
Chaowei CHEN
2
Author Information
1. Department of Radiotherapy, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013. 459015204@qq.com.
2. Department of Radiotherapy, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013.
3. Department of Radiotherapy, Xijing Hospital, Xi'an 710032, China.
4. Department of Radiotherapy, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013. hkjin2008@163.com.
- Publication Type:Journal Article
- Keywords:
clinical type;
nasopharyngeal carcinoma;
radiomics
- MeSH:
Humans;
Nasopharyngeal Carcinoma;
Nasopharyngeal Neoplasms;
ROC Curve;
Retrospective Studies;
Sensitivity and Specificity
- From:
Journal of Central South University(Medical Sciences)
2020;45(7):819-826
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:To evaluate the application value of CT-based radiomics features for the ascending and descending types of nasopharyngeal carcinoma (NPC).
METHODS:A total of 217 NPC patients (48 ascending type and 169 descending type), who obtained CT images before radiotherapy in Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University from February 2015 to October 2017, were analyzed retrospectively. All patients were randomly divided into a training set (=153) and a test set (=64). Gross tumor volume in the nasopharynx (GTVnx) was selected as regions of interest (ROI) and was analyzed by radiomics. A total of 1 300 radiomics features were extracted via IBEX. The least absolute shrinkage and selection operator (LASSO) logistic regression was performed to choose the significant features. Support vector machine (SVM) and random forest (RF) classifiers were built and verified.
RESULTS:Six features were selected by the LASSO from 1 300 radiomics features. Compared with SVM classifier, RF classifier showed better classification performance. The area under curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were 0.989, 0.941, 1.000, and 0.924, respectively for the training set; 0.994, 0.937, 1.000, and 0.924, respectively for the validation set.
CONCLUSIONS:CT-based radiomics features possess great potential in differentiating ascending and descending NPC. It provides a certain basis for accurate medical treatment of NPC, and may affect the treatment strategy of NPC in the future.
