Literature DB >> 27435352

Tumor heterogeneity measured on F-18 fluorodeoxyglucose positron emission tomography/computed tomography combined with plasma Epstein-Barr Virus load predicts prognosis in patients with primary nasopharyngeal carcinoma.

Sheng-Chieh Chan1,2, Kai-Ping Chang3, Yu-Hua Dean Fang4, Ngan-Ming Tsang5, Shu-Hang Ng6, Cheng-Lung Hsu7, Chun-Ta Liao3, Tzu-Chen Yen2,8.   

Abstract

OBJECTIVES/HYPOTHESIS: Plasma Epstein-Barr virus (EBV) DNA concentrations predict prognosis in patients with nasopharyngeal carcinoma (NPC). Recent evidence also indicates that intratumor heterogeneity on F-18 fluorodeoxyglucose positron emission tomography (18 F-FDG PET) scans is predictive of treatment outcomes in different solid malignancies. Here, we sought to investigate the prognostic value of heterogeneity parameters in patients with primary NPC. STUDY
DESIGN: Retrospective cohort study.
METHODS: We examined 101 patients with primary NPC who underwent pretreatment 18 F-FDG PET/computed tomography. Circulating levels of EBV DNA were measured in all participants. The following PET heterogeneity parameters were collected: histogram-based heterogeneity parameters, second-order texture features (uniformity, contrast, entropy, homogeneity, dissimilarity, inverse difference moment), and higher-order (coarseness, contrast, busyness, complexity, strength) texture features.
RESULTS: The median follow-up time was 5.14 years. Total lesion glycolysis (TLG), tumor heterogeneity measured by histogram-based parameter skewness, and the majority of second-order or higher-order texture features were significantly associated with overall survival (OS) and/or recurrence-free survival (RFS). In multivariate analysis, age (P =.005), EBV DNA load (P = .0002), and uniformity (P = .001) independently predicted OS. Only skewness retained the independent prognostic significance for RFS. Tumor stage, standardized uptake value, or TLG did not show an independent association with survival endpoints. The combination of uniformity, EBV DNA load, and age resulted in a more reliable prognostic stratification (P < .001).
CONCLUSIONS: Tumor heterogeneity is superior to traditional PET parameters for predicting outcomes in primary NPC. The combination of uniformity with EBV DNA load can improve prognostic stratification in this clinical entity. LEVEL OF EVIDENCE: 4 Laryngoscope, 127:E22-E28, 2017.
© 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Entities:  

Keywords:  Epstein-Barr virus; F-18 fluorodeoxyglucose; Nasopharyngeal carcinoma; heterogeneity; positron emission tomography; prognosis; risk stratification; texture features

Mesh:

Substances:

Year:  2016        PMID: 27435352     DOI: 10.1002/lary.26172

Source DB:  PubMed          Journal:  Laryngoscope        ISSN: 0023-852X            Impact factor:   3.325


  15 in total

1.  Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma.

Authors:  Wenbing Lv; Qingyu Yuan; Quanshi Wang; Jianhua Ma; Qianjin Feng; Wufan Chen; Arman Rahmim; Lijun Lu
Journal:  Mol Imaging Biol       Date:  2019-10       Impact factor: 3.488

2.  Value of early evaluation of treatment response using 18F-FDG PET/CT parameters and the Epstein-Barr virus DNA load for prediction of outcome in patients with primary nasopharyngeal carcinoma.

Authors:  Yu-Hung Chen; Kai-Ping Chang; Sung-Chao Chu; Tzu-Chen Yen; Ling-Yi Wang; Joseph Tung-Chieh Chang; Cheng-Lung Hsu; Shu-Hang Ng; Shu-Hsin Liu; Sheng-Chieh Chan
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-09-27       Impact factor: 9.236

Review 3.  Radiomics in Oncological PET/CT: Clinical Applications.

Authors:  Jeong Won Lee; Sang Mi Lee
Journal:  Nucl Med Mol Imaging       Date:  2017-10-20

4.  Longitudinal evaluation of five nasopharyngeal carcinoma animal models on the microPET/MR platform.

Authors:  Jingjing Shi; Zhichao Xue; Kel Vin Tan; Hui Yuan; Anna Chi Man Tsang; Sai Wah Tsao; Pek-Lan Khong
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-12-04       Impact factor: 9.236

5.  Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up.

Authors:  Adam A Dmytriw; Claudia Ortega; Reut Anconina; Ur Metser; Zhihui A Liu; Zijin Liu; Xuan Li; Thiparom Sananmuang; Eugene Yu; Sayali Joshi; John Waldron; Shao Hui Huang; Scott Bratman; Andrew Hope; Patrick Veit-Haibach
Journal:  Cancers (Basel)       Date:  2022-06-24       Impact factor: 6.575

Review 6.  Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.

Authors:  David Morland; Elizabeth Katherine Anna Triumbari; Luca Boldrini; Roberto Gatta; Daniele Pizzuto; Salvatore Annunziata
Journal:  Diagnostics (Basel)       Date:  2022-05-27

7.  Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma.

Authors:  Hui Xu; Wenbing Lv; Hui Feng; Dongyang Du; Qingyu Yuan; Quanshi Wang; Zhenhui Dai; Wei Yang; Qianjin Feng; Jianhua Ma; Lijun Lu
Journal:  Mol Imaging Biol       Date:  2020-10       Impact factor: 3.488

8.  The Effect of Adding Neoadjuvant Chemotherapy to Concurrent Chemoradiotherapy in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma and Undetectable Pretreatment Epstein-Barr Virus DNA.

Authors:  Ya-Nan Jin; Ji-Jin Yao; Si-Yang Wang; Wang-Jian Zhang; Fan Zhang; Guan-Qun Zhou; Zhi-Bin Cheng; Hao-Yuan Mo; Ying Sun
Journal:  Transl Oncol       Date:  2017-05-29       Impact factor: 4.243

Review 9.  Cyclooxygenase-2 expression is positively associated with lymph node metastasis in nasopharyngeal carcinoma.

Authors:  Gui Yang; Qiaoling Deng; Wei Fan; Zheng Zhang; Peipei Xu; Shihui Tang; Ping Wang; Jun'e Wang; Mingxia Yu
Journal:  PLoS One       Date:  2017-03-16       Impact factor: 3.240

10.  Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging.

Authors:  Yuhui Qin; Xiaoping Yu; Jing Hou; Ying Hu; Feiping Li; Lu Wen; Qiang Lu; Yi Fu; Siye Liu
Journal:  Medicine (Baltimore)       Date:  2018-07       Impact factor: 1.889

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