Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging

被引:31
作者
Feng, Qi [1 ]
Liang, Jiangtao [2 ]
Wang, Luoyu [3 ]
Niu, Jialing [4 ]
Ge, Xiuhong [1 ]
Pang, Peipei [5 ]
Ding, Zhongxiang [1 ,6 ]
机构
[1] Zhejiang Univ, Sch Med, Dept Radiol, Affiliated Hangzhou Peoples Hosp 1, Hangzhou, Peoples R China
[2] Hangzhou Universal Med Imaging Diagnost Ctr, Hangzhou, Peoples R China
[3] Hangzhou Normal Univ, Inst Psychol Sci, Hangzhou, Peoples R China
[4] Zhejiang Chinese Med Univ, Hangzhou, Peoples R China
[5] GE Healthcare Life Sci, Hangzhou, Peoples R China
[6] Zhejiang Univ, Translat Med Res Ctr, Key Lab Clin Canc Pharmacol & Toxicol Res Zhejian, Affiliated Hangzhou Peoples Hosp 1,Sch Med, Hangzhou, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2020年 / 10卷
基金
中国国家自然科学基金;
关键词
nasopharyngeal carcinoma; positron emission tomography; magnetic resonance imaging; radiomics; staging; CANCER; TOMOGRAPHY; FEATURES; CLASSIFICATION;
D O I
10.3389/fonc.2020.01619
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Objective:Accurate staging is of great importance in treatment selection for patients with nasopharyngeal carcinoma (NPC). The aims of this study were to construct radiomic models of NPC staging based on positron emission tomography (PET) and magnetic resonance (MR) images and to investigate the correlation between metabolic parameters and radiomic features. Methods:A total of 100 consecutive cases of NPC (70 in training and 30 in the testing cohort) with undifferentiated carcinoma confirmed pathologically were recruited. Metabolic parameters of the local lesions of NPC were measured. A total of 396 radiomic features based on PET and MRI images were calculated [including histogram, Haralick, shape factor, gray level co-occurrence matrix (GLCM), and run length matrix (RLM)] and selected [using maximum relevance and minimum redundancy (mRMR) and least shrinkage and selection operator (LASSO)], respectively. The logistic regression models were established according to these features. Finally, the relationship between the metabolic parameters and radiomic features was analyzed. Results:We selected the nine most relevant radiomic features (six from MR images and three from PET images) from local NPC lesions. In the PET model, the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and the specificity of the training group were 0.84, 0.75, 0.90, and 0.69, respectively. In the MR model, those metrics were 0.85, 0.83, 0.75, and 0.86, respectively. Pearson's correlation analysis showed that the metabolic parameters had different degrees of correlation with the selected radiomic features. Conclusion:The PET and MR radiomic models were helpful in the diagnosis of NPC staging. There were correlations between the metabolic parameters and radiomic features of primary NPC based on PET/MR. In the future, PET/MR-based radiomic models, with further improvement and validation, can be a more useful and economical tool for predicting local invasion and distant metastasis of NPC.
引用
收藏
页数:10
相关论文
共 29 条
[1]   The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging [J].
Amin, Mahul B. ;
Greene, Frederick L. ;
Edge, Stephen B. ;
Compton, Carolyn C. ;
Gershenwald, Jeffrey E. ;
Brookland, Robert K. ;
Meyer, Laura ;
Gress, Donna M. ;
Byrd, David R. ;
Winchester, David P. .
CA-A CANCER JOURNAL FOR CLINICIANS, 2017, 67 (02) :93-99
[2]  
[Anonymous], 2019, FRONT ONCOL
[3]   Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types [J].
Bianconi, Francesco ;
Palumbo, Isabella ;
Fravolini, Mario Luca ;
Chiari, Rita ;
Minestrini, Matteo ;
Brunese, Luca ;
Palumbo, Barbara .
MOLECULAR IMAGING AND BIOLOGY, 2019, 21 (06) :1200-1209
[4]   Correlation of positron emission tomography standard uptake value and pathologic specimen size in cancer of the head and neck [J].
Burri, Ryan J. ;
Rangaswamy, Balasubramanya ;
Kostakoglu, Lale ;
Hoch, Benjamin ;
Genden, Eric M. ;
Som, Peter M. ;
Kao, Johnny .
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2008, 71 (03) :682-688
[5]  
Chaddad A, 2018, FRONT ONCOL, P8
[6]   Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images [J].
Chaddad, Ahmad ;
Daniel, Paul ;
Niazi, Tamim .
FRONTIERS IN ONCOLOGY, 2018, 8
[7]   Clinical utility of simultaneous whole-body 18F-FDG PET/MRI as a single-step imaging modality in the staging of primary nasopharyngeal carcinoma [J].
Chan, Sheng-Chieh ;
Yeh, Chih-Hua ;
Yen, Tzu-Chen ;
Ng, Shu-Hang ;
Chang, Joseph Tung-Chieh ;
Lin, Chien-Yu ;
Yen-Ming, Tsang ;
Fan, Kang-Hsing ;
Huang, Bing-Shen ;
Hsu, Cheng-Lung ;
Chang, Kai-Ping ;
Wang, Hung-Ming ;
Liao, Chun-Ta .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2018, 45 (08) :1297-1308
[8]   Preliminary clinical results for PET/MR compared with PET/CT in patients with nasopharyngeal carcinoma [J].
Cheng, Yong ;
Bai, Le ;
Shang, Jingjie ;
Tang, Yongjin ;
Ling, Xueying ;
Guo, Bin ;
Gong, Jian ;
Wang, Lu ;
Xu, Hao .
ONCOLOGY REPORTS, 2020, 43 (01) :177-187
[9]   Radiomic phenotype features predict pathological response in non-small cell lung cancer [J].
Coroller, Thibaud P. ;
Agrawal, Vishesh ;
Narayan, Vivek ;
Hou, Ying ;
Grossmann, Patrick ;
Lee, Stephanie W. ;
Mak, Raymond H. ;
Aerts, Hugo J. W. L. .
RADIOTHERAPY AND ONCOLOGY, 2016, 119 (03) :480-486
[10]  
Dhingra Vandana K, 2015, Indian J Radiol Imaging, V25, P332, DOI 10.4103/0971-3026.169467