Magnetic resonance imaging-based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma

被引:17
作者
Gao, Yan [1 ,2 ,3 ]
Mao, Yitao [4 ]
Lu, Shanhong [1 ,2 ,3 ]
Tan, Lei [5 ]
Li, Guo [1 ,2 ,3 ]
Chen, Juan [1 ,2 ,3 ]
Huang, Donghai [1 ,2 ,3 ]
Zhang, Xin [1 ,2 ,3 ]
Qiu, Yuanzheng [1 ,2 ,3 ,6 ]
Liu, Yong [1 ,2 ,3 ,6 ]
机构
[1] Cent South Univ, Xiangya Hosp, Dept Otolaryngol Head & Neck Surg, 87 Xiangya Rd, Changsha 410008, Hunan, Peoples R China
[2] Otolaryngol Major Dis Res Key Lab Hunan Prov, Changsha, Peoples R China
[3] Clin Res Ctr Pharyngolaryngeal Dis & Voice Disord, Changsha, Peoples R China
[4] Cent South Univ, Xiangya Hosp, Dept Radiol, Changsha, Peoples R China
[5] Hunan Univ Technol & Business, Coll Comp & Informat Engn, Changsha, Peoples R China
[6] Xiangya Hosp, Natl Clin Res Ctr Geriatr Disorders, Changsha, Peoples R China
来源
HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK | 2021年 / 43卷 / 12期
基金
中国国家自然科学基金;
关键词
genomics; nasopharyngeal carcinoma; prediction model; prognosis; radiomics; INTENSITY-MODULATED RADIOTHERAPY; RADIOMICS; SURVIVAL; MUTATIONS; FEATURES; STAGE;
D O I
10.1002/hed.26867
中图分类号
R76 [耳鼻咽喉科学];
学科分类号
100213 ;
摘要
Background To establish a radiomics nomogram for survival prediction and determine if genomic data were related to radiomics signature in advanced nasopharyngeal carcinoma (NPC). Methods Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1WI) in 316 patients. A progression-free survival (PFS) nomogram was developed and validated by the combination of the radiomics signature and clinicopathologic factors. Whole transcriptomics sequencing was performed in pretreatment tumor samples; correlation of gene expression and radiomics signature was further investigated. Results A 24-feature-combined radiomics signature was highly correlated with PFS; its integration with clinical predictors showed good prediction performance in the training and the validation cohort (C-index: 0.80 and 0.73). A significant correlation was observed between certain gene expression and Rad-score, especially the mRNA expression of CDKL2, PLIN5, and SPAG1. Conclusion As a noninvasive method, the MRI-based radiomics signature might enable the pretreatment prediction of prognosis and gene expressions profile in advanced NPC.
引用
收藏
页码:3730 / 3742
页数:13
相关论文
共 50 条
  • [21] Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer
    Ye, Yong-Xia
    Yang, Liu
    Kang, Zheng
    Wang, Mei-Qin
    Xie, Xiao-Dong
    Lou, Ke-Xin
    Bao, Jun
    Du, Mei
    Li, Zhe-Xuan
    [J]. WORLD JOURNAL OF GASTROINTESTINAL ONCOLOGY, 2024, 16 (05) : 1849 - 1860
  • [22] Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging
    Hua, Hong-Li
    Deng, Yu-Qin
    Li, Song
    Li, Si-Te
    Li, Fen
    Xiao, Bai-Kui
    Huang, Jin
    Tao, Ze-Zhang
    [J]. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2023, 26 (07) : 1351 - 1363
  • [23] Prognosis Prediction of Hepatocellular Carcinoma Based on Magnetic Resonance Imaging Features
    Low, Hsien Min
    Lee, Jeong Min
    Tan, Cher Heng
    [J]. KOREAN JOURNAL OF RADIOLOGY, 2023, 24 (07) : 660 - 667
  • [24] A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma
    Liu, Lu
    Pei, Wei
    Liao, Hai
    Wang, Qiang
    Gu, Donglian
    Liu, Lijuan
    Su, Danke
    Jin, Guanqiao
    [J]. FRONTIERS IN ONCOLOGY, 2022, 12
  • [25] Prediction of glypican-3 expression in hepatocellular carcinoma using multisequence magnetic resonance imaging-based histology nomograms
    Li, Si-Qi
    Yang, Cun-Xia
    Wu, Chun-Mei
    Cui, Jin-Jing
    Wang, Jia-Ning
    Yin, Xiao-Ping
    [J]. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2024, 14 (07) : 4436 - 4449
  • [26] Multiparametric magnetic resonance imaging-based radiomics nomogram for predicting tumor grade in endometrial cancer
    Yue, Xiaoning
    He, Xiaoyu
    He, Shuaijie
    Wu, Jingjing
    Fan, Wei
    Zhang, Haijun
    Wang, Chengwei
    [J]. FRONTIERS IN ONCOLOGY, 2023, 13
  • [27] Imaging Biomarkers and Gene Expression Data Correlation Framework for Lung Cancer Radiogenomics Analysis Based on Deep Learning
    Sui, Dong
    Guo, Maozu
    Ma, Xiaoxuan
    Baptiste, Julian
    Zhang, Lei
    [J]. IEEE ACCESS, 2021, 9 (09): : 125247 - 125257
  • [28] Magnetic resonance imaging-based radiomics for predicting infiltration levels of CD68+tumor-associated macrophages in glioblastomas
    Zhou, Qing
    Zhang, Bin
    Xue, Caiqiang
    Ren, Jialiang
    Zhang, Peng
    Ke, Xiaoai
    Man, Jiangwei
    Zhou, Junlin
    [J]. STRAHLENTHERAPIE UND ONKOLOGIE, 2024,
  • [29] Impact of magnetic resonance imaging-derived skeletal muscle index in locoregionally advanced nasopharyngeal carcinoma
    Jiang, Jiali
    Cai, Zhuochen
    Zheng, Ronghui
    Yuan, Yawei
    Lv, Xing
    Qiu, Wenze
    [J]. EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY, 2024, 281 (07) : 3707 - 3715
  • [30] Feasibility analysis of magnetic resonance imaging-based radiomics features for preoperative prediction of nuclear grading of ductal carcinoma in situ
    Zhao, Meng-Ran
    Ma, Wen-Juan
    Song, Xiang-Chao
    Li, Zhi-Jun
    Shao, Zhen-Zhen
    Lu, Hong
    Zhao, Rui
    Guo, Yi-Jun
    Ye, Zhao-Xiang
    Liu, Pei-Fang
    [J]. GLAND SURGERY, 2023, 12 (09) : 1209 - 1223