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Predicting Progression-Free Survival Using MRI-Based Radiomics For Patients With Nonmetastatic Nasopharyngeal Carcinoma

Hesong Shen, Yu Wang, D. Liu, Rongfei Lv, Yuanying Huang, Chao Peng, Shi-Xi Jiang, Ying Wang, Yongpeng He, Xiaosong Lan, H. Huang, Jianqing Sun, J. Zhang
Published 2020 · Medicine

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Objectives: This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC). Methods: A total of 327 nonmetastatic NPC patients [training cohort (n = 230) and validation cohort (n = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic features. Five models [Model 1: clinical data, Model 2: overall stage, Model 3: radiomics, Model 4: radiomics + overall stage, Model 5: radiomics + overall stage + Epstein–Barr virus (EBV) DNA] were constructed. The prognostic performances of these models were evaluated by Harrell's concordance index (C-index). The Kaplan–Meier method was applied for the survival analysis. Results: Model 5 incorporating radiomics, overall stage, and EBV DNA yielded the highest C-indices for predicting PFS in comparison with Model 1, Model 2, Model 3, and Model 4 (training cohorts: 0.805 vs. 0.766 vs. 0.749 vs. 0.641 vs. 0.563, validation cohorts: 0.874 vs. 0.839 vs. 836 vs. 0.689 vs. 0.456). The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group. Conclusions: The model incorporating radiomics, overall stage, and EBV DNA showed better performance for predicting PFS in nonmetastatic NPC patients.
This paper references
Nasopharyngeal Carcinoma
P. Vilar (1966)
potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma
H Peng (2019)
Is pretreatment Epstein-Barr virus DNA still associated with 6-year survival outcomes in locoregionally advanced nasopharyngeal carcinoma?
Ya-Nan Jin (2017)
MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma
L. Zhao (2019)
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
Proposed modifications and incorporation of plasma Epstein‐Barr virus DNA improve the TNM staging system for Epstein‐Barr virus‐related nasopharyngeal carcinoma
R. Guo (2019)
Progression-free survival versus overall survival as the primary end point in anticancer drug trials: Increasingly relevant impact of therapy following progression
M. Markman (2009)
Impact of Plasma Epstein-Barr Virus-DNA and Tumor Volume on Prognosis of Locally Advanced Nasopharyngeal Carcinoma
Meng Chen (2015)
association with disease-free survival in patients with invasive breast cancer
H Park (2018)
A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma.
Kaixuan Yang (2019)
How Can Radiomics Be Consistently Applied across Imagers and Institutions?
Peter Steiger (2019)
Pretreatment MRI radiomics analysis allows for reliable prediction of local recurrence in non-metastatic T4 nasopharyngeal carcinoma
L. Zhang (2019)
Plasma Epstein-Barr viral DNA load at midpoint of radiotherapy course predicts outcome in advanced-stage nasopharyngeal carcinoma.
S. F. Leung (2014)
Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images
Dongyang Du (2019)
an updated interpretation
L Peng (2018)
Relationship between pretreatment concentration of plasma Epstein‐Barr virus DNA and tumor burden in nasopharyngeal carcinoma: An updated interpretation
L. Peng (2018)
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.
Yanqi Huang (2016)
Progression-free survival as primary end point in advanced non-small-cell lung cancer: does the size matter?
Massimo Di Maio (2008)
a potential biomarker for the prediction of disease-free survival in earlystage (I or II) non-small cell lung cancer
Y Huang (2016)
Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma
H. Peng (2019)
Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation
Xiaokai Mo (2019)
The addition of pretreatment plasma Epstein–Barr virus DNA into the eighth edition of nasopharyngeal cancer TNM stage classification
V. Lee (2019)
Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer
Hyunjin Park (2018)
Nasopharyngeal carcinoma. Lancet
YP Chen (2019)
Relationship of circulating tumor cells and Epstein–Barr virus DNA to progression‐free survival and overall survival in metastatic nasopharyngeal carcinoma patients
R. You (2019)
Validation of the 8th Edition of the UICC/AJCC Staging System for Nasopharyngeal Carcinoma From Endemic Areas in the Intensity-Modulated Radiotherapy Era.
Ling-Long Tang (2017)
Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer
R. Larue (2018)
Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma
Bin Zhang (2017)
application to nasopharyngeal carcinoma post-therapy PET/CT images
D Du (2019)
Depicting distant metastatic risk by refined subgroups derived from the 8th edition nasopharyngeal carcinoma TNM.
Q. Guo (2019)

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