Open Access

ARTICLE

The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma

by

* Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Zhejiang, P.R. China
† Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Zhejiang, P.R. China
‡ Key Laboratory of Head Neck Cancer of Zhejiang Province, Zhejiang Cancer Hospital, Zhejiang, P.R. China
§ Key Laboratory of Radiation Oncology of Zhejiang Province, Zhejiang, P.R. China
¶ Department of Breast Tumor Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang, P.R. China
# Department of Breast Tumor Surgery, Zhejiang Cancer Hospital, Zhejiang, P.R. China
** Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang, P.R. China
†† Department of Radiology, Zhejiang Cancer Hospital, Zhejiang, P.R. China
‡‡ Department of Digital Earth, Institute of Remote Sensing and Digital Earth, CAS, Beijing, P.R. China

Oncology Research 2020, 28(6), 605-613. https://doi.org/10.3727/096504020X16022401878096

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.

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