MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma

被引:127
|
作者
Zhao, Lina [1 ]
Gong, Jie [2 ]
Xi, Yibin [3 ]
Xu, Man [1 ]
Li, Chen [3 ]
Kang, Xiaowei [3 ]
Yin, Yutian [1 ]
Qin, Wei [2 ]
Yin, Hong [3 ]
Shi, Mei [1 ]
机构
[1] Air Force Med Univ, Xijing Hosp, Dept Radiat Oncol, Xian, Peoples R China
[2] Xidian Univ, Sch Life Sci & Technol, Life Sci Res Ctr, Xian 710126, Peoples R China
[3] Air Force Med Univ, Xijing Hosp, Dept Radiol, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
Nasopharyngeal carcinoma; Magnetic resonance imaging; Radiomics; Machine learning; Induction chemotherapy; CHEMORADIOTHERAPY; RADIOTHERAPY; CANCER;
D O I
10.1007/s00330-019-06211-x
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objectives To establish and validate a radiomics nomogram for prediction of induction chemotherapy (IC) response and survival in nasopharyngeal carcinoma (NPC) patients. Methods One hundred twenty-three NPC patients (100 in training and 23 in validation cohort) with multi-MR images were enrolled. A radiomics nomogram was established by integrating the clinical data and radiomics signature generated by support vector machine. Results The radiomics signature consisting of 19 selected features from the joint T1-weighted (T1-WI), T2-weighted (T2-WI), and contrast-enhanced T1-weighted MRI images (T1-C) showed good prognostic performance in terms of evaluating IC response in two cohorts. The radiomics nomogram established by integrating the radiomics signature with clinical data outperformed clinical nomogram alone (C-index in validation cohort, 0.863 vs 0.549; p < 0.01). Decision curve analysis demonstrated the clinical utility of the radiomics nomogram. Survival analysis showed that IC responders had significant better PFS (progression-free survival) than non-responders (3-year PFS 84.81% vs 39.75%, p < 0.001). Low-risk groups defined by radiomics signature had significant better PFS than high-risk groups (3-year PFS 76.24% vs 48.04%, p < 0.05). Conclusions Multiparametric MRI-based radiomics could be helpful for personalized risk stratification and treatment in NPC patients receiving IC.
引用
收藏
页码:537 / 546
页数:10
相关论文
共 50 条
  • [21] Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram
    Jingyu Zhong
    Chengxiu Zhang
    Yangfan Hu
    Jing Zhang
    Yun Liu
    Liping Si
    Yue Xing
    Defang Ding
    Jia Geng
    Qiong Jiao
    Huizhen Zhang
    Guang Yang
    Weiwu Yao
    [J]. European Radiology, 2022, 32 : 6196 - 6206
  • [22] MRI-based radiomics models can improve prognosis prediction for nasopharyngeal carcinoma with neoadjuvant chemotherapy
    Zeng, Fan
    Lin, Kai-Rong
    Jin, Ya-Bin
    Li, Hao-Jiang
    Quan, Qiang
    Su, Jian-Chun
    Chen, Kai
    Zhang, Jing
    Han, Chen
    Zhang, Guo-Yi
    [J]. MAGNETIC RESONANCE IMAGING, 2022, 88 : 108 - 115
  • [23] Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram
    Zhong, Jingyu
    Zhang, Chengxiu
    Hu, Yangfan
    Zhang, Jing
    Liu, Yun
    Si, Liping
    Xing, Yue
    Ding, Defang
    Geng, Jia
    Jiao, Qiong
    Zhang, Huizhen
    Yang, Guang
    Yao, Weiwu
    [J]. EUROPEAN RADIOLOGY, 2022, 32 (09) : 6196 - 6206
  • [24] Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy
    Wang, Shun-Xin
    Yang, Yi
    Xie, Hui
    Yang, Xin
    Liu, Zhi-Qiao
    Li, Hao-Jiang
    Huang, Wen-Jie
    Luo, Wei-Jie
    Lei, Yi-Ming
    Sun, Ying
    Ma, Jun
    Chen, Yan-Feng
    Liu, Li-Zhi
    Mao, Yan-Ping
    [J]. EUROPEAN RADIOLOGY, 2024,
  • [25] The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma
    Lu, Shuang
    Wang, Chenglong
    Liu, Yun
    Chu, Funing
    Jia, Zhengyan
    Zhang, Hongkai
    Wang, Zhaoqi
    Lu, Yanan
    Wang, Shuting
    Yang, Guang
    Qu, Jinrong
    [J]. EUROPEAN RADIOLOGY, 2024, 34 (01) : 485 - 494
  • [26] The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma
    Shuang Lu
    Chenglong Wang
    Yun Liu
    Funing Chu
    Zhengyan Jia
    Hongkai Zhang
    Zhaoqi Wang
    Yanan Lu
    Shuting Wang
    Guang Yang
    Jinrong Qu
    [J]. European Radiology, 2024, 34 : 485 - 494
  • [27] MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma
    Liao, Hai
    Chen, Xiaobo
    Lu, Shaolu
    Jin, Guanqiao
    Pei, Wei
    Li, Ye
    Wei, Yunyun
    Huang, Xia
    Wang, Chenghuan
    Liang, Xueli
    Bao, Huayan
    Liu, Lidong
    Su, Danke
    [J]. JOURNAL OF MAGNETIC RESONANCE IMAGING, 2022, 56 (02) : 547 - 559
  • [28] Editorial for "An MRI-Based Radiomics Nomogram to Predict Recurrence in Sinonasal Malignant Tumors"
    Hu, Houchun Harry
    [J]. JOURNAL OF MAGNETIC RESONANCE IMAGING, 2023, 58 (02) : 532 - 533
  • [29] MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma
    Ming, Xue
    Oei, Ronald Wihal
    Zhai, Ruiping
    Kong, Fangfang
    Du, Chengrun
    Hu, Chaosu
    Hu, Weigang
    Zhang, Zhen
    Ying, Hongmei
    Wang, Jiazhou
    [J]. SCIENTIFIC REPORTS, 2019, 9 (1)
  • [30] MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma
    Xue Ming
    Ronald Wihal Oei
    Ruiping Zhai
    Fangfang Kong
    Chengrun Du
    Chaosu Hu
    Weigang Hu
    Zhen Zhang
    Hongmei Ying
    Jiazhou Wang
    [J]. Scientific Reports, 9