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

doi:10.3727/096504020X16022401878096

HFCAS OpenIR

	The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma
	Piao Yongfeng 1,2,3,4; Jiang Chuner 1,5,6; Wang Lei 1,2,3,4; Yan Fengqin 1,2,3,4; Ye Zhimin 1,2,3,4; Fu Zhenfu 1,2,3,4; Jiang Haitao 1,7,8; Jiang Yangming 9; Wang Fangzheng 1,2,3,4
	2021
发表期刊	ONCOLOGY RESEARCH
ISSN	0965-0407
通讯作者	Jiang Haitao(jianght@zjcc.org.cn) ; Wang Fangzheng(wangfz76@126.com)
摘要	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. 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.
关键词	Nasopharyngeal carcinoma (NPC) Neoadjuvant chemotherapy (NAC) Response prediction Magnetic resonance imaging (MRI) Radiomics Texture analysis
DOI	10.3727/096504020X16022401878096
收录类别	SCI
语种	英语
资助项目	Medical and Health Science and Technology Program of Zhejiang Province[020KY084] ; Medical and Health Science and Technology Program of Zhejiang Province[2019KY041] ; Medical and Health Science and Technology Program of Zhejiang Province[2013KYB033] ; Medical and Health Science and Technology Program of Zhejiang Province[2009B026] ; Medical and Health Science and Technology Program of Zhejiang Province[2006A016] ; Medical and Health Science and Technology Program of Zhejiang Province[2005B012] ; Medical and Health Science and Technology Program of Zhejiang Province[2004B014] ; National Natural Science Foundation of China[81502647]
项目资助者	Medical and Health Science and Technology Program of Zhejiang Province ; National Natural Science Foundation of China
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000632699900002
出版者	COGNIZANT COMMUNICATION CORP
引用统计	被引频次：24[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/121362
专题	中国科学院合肥物质科学研究院
通讯作者	Jiang Haitao; Wang Fangzheng
作者单位	1.Univ Chinese Acad Sci, Canc Hosp, Dept Radiat Oncol, Zhejiang Canc Hosp, Hangzhou, Zhejiang, Peoples R China 2.Chinese Acad Sci, Inst Canc & Basic Med ICBM, Hangzhou, Zhejiang, Peoples R China 3.Zhejiang Canc Hosp, Key Lab Head Neck Canc Zhejiang Prov, Hangzhou, Zhejiang, Peoples R China 4.Key Lab Radiat Oncol Zhejiang Prov, Hangzhou, Zhejiang, Peoples R China 5.Univ Chinese Acad Sci, Dept Breast Tumor Surg, Canc Hosp, Hangzhou, Zhejiang, Peoples R China 6.Zhejiang Canc Hosp, Dept Breast Tumor Surg, Hangzhou, Zhejiang, Peoples R China 7.Univ Chinese Acad Sci, Dept Radiol, Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China 8.Zhejiang Canc Hosp, Dept Radiol, Hangzhou, Zhejiang, Peoples R China 9.Chinese Acad Sci, Dept Digital Earth, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Piao Yongfeng,Jiang Chuner,Wang Lei,et al. The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma[J]. ONCOLOGY RESEARCH,2021,28.
APA	Piao Yongfeng.,Jiang Chuner.,Wang Lei.,Yan Fengqin.,Ye Zhimin.,...&Wang Fangzheng.(2021).The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma.ONCOLOGY RESEARCH,28.
MLA	Piao Yongfeng,et al."The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma".ONCOLOGY RESEARCH 28(2021).