@Article{096504020X16022401878096,
AUTHOR = {Piao Yongfeng, Jiang Chuner, Wang Lei, Yan Fengqin, Ye Zhimin, Fu Zhenfu, Jiang Haitao, Jiang Yangming, Wang Fangzheng},
TITLE = {The Usefulness of Pretreatment MR-Based Radiomics on Early Response  of Neoadjuvant Chemotherapy in Patients With Locally Advanced  Nasopharyngeal Carcinoma},
JOURNAL = {Oncology Research},
VOLUME = {28},
YEAR = {2020},
NUMBER = {6},
PAGES = {605--613},
URL = {http://www.techscience.com/or/v28n6/48498},
ISSN = {1555-3906},
ABSTRACT = {The aim of this study was to explore the predictive role of pretreatment MRI-based radiomics on early response 
of neoadjuvant chemotherapy (NAC) in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. 
Between January 2016 and December 2016, a total of 108 newly diagnosed NPC patients who were hospitalized in the Cancer Hospital of the University of Chinese Academy of Sciences were reviewed. All patients 
had complete data of enhanced MR of nasopharynx before treatment, and then received two to three cycles of 
TP-based NAC. After 2 cycles of NAC, enhanced MR of nasopharynx was conducted again. Compared with 
the enhanced MR images before treatment, the response after NAC was evaluated. According to the evaluation 
criteria of RECIST1.1, 108 cases were divided into two groups: 52 cases for the NAC-sensitive group and 56 
cases for the NAC-resistance group. ITK-SNAP software was used to manually sketch and segment the region 
of interest (ROI) of nasopharyngeal tumor on the MR enhanced T1WI sequence image. The parameters were 
analyzed and extracted by using AI Kit software. ANOVA/MW test, correlation analysis, and LASSO were 
used to select texture features. We used multivariate logistic regressions to select texture features and establish 
a predictive model. The ROC curve was used to evaluate the efficiency of the predictive model. A total of 396 
texture features were obtained by using feature calculation. After all features were screened, we selected two 
features including ClusterShade_angle135_offset4 and Correlation_AllDirection_offshe1_SD. Based on these 
two features, we established a predictive model by using multivariate logistic regression. The AUC of the two 
features used alone (0.804, 95% CI = 0.602–0.932; 0.762, 95% CI = 0.556–0.905) was smaller than the combination of these two features (0.905, 95% CI = 0.724–0.984, p = 0.0005). Moreover, the sensitivity values of the 
two features used alone and the combined use were 92.9%, 51.7%, and 85.7%, respectively, while the specificity values were 66.7%, 91.7%, and 83.3%, respectively, in the early response of NAC for NPC. The predictive 
model based on MRI-enhanced sequence imaging could distinguish the sensitivity and resistance to NAC and 
provide new biomarkers for the early prediction of the curative effect in NPC patients.},
DOI = {10.3727/096504020X16022401878096}
}