Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation

  • Xiaokai Mo
  • Xiangjun Wu
  • Di Dong
  • Baoliang Guo
  • Changhong Liang
  • Xiaoning Luo
  • Bin Zhang
  • Lu Zhang
  • Yuhao Dong
  • Zhouyang Lian
  • Jing Liu
  • Shufang Pei
  • Wenhui Huang
  • Fusheng Ouyang
  • Jie TianEmail author
  • Shuixing ZhangEmail author
Head and Neck



To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans.

Materials and methods

We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model’s prognostic performance. A Kaplan–Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression.


Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic–clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688–0.920) and 0.756 (95% CI, 0.605–0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts.


A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy.

Key Points

Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer.

Imaging features extracted from CECT and NCCT images were independent predictors of PFS.

We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.


Head and neck cancer Hypopharynx Chemoradiotherapy Recurrence Prognosis 



Contrast-enhanced computed tomography


Dynamic contrast-enhanced magnetic resonance imaging


Head and neck squamous cell carcinoma


Intra-/inter-class correlation coefficient


Non-contrast computed tomography


Positron emission tomography–computed tomography


Progression-free survival



This work is supported by the National Natural Science Foundation of China (81571664, 81871323, 81801665, 81227901, 81771924, 81501616, 81671854), the National Natural Science Foundation of Guangdong Province (2018B030311024), the Scientific Research General Project of Guangzhou Science Technology and Innovation Commission (201707010328), the China Postdoctoral Science Foundation (2016 M600145), and the Beijing Natural Science Foundation (L182061).


This study has received funding by the National Natural Science Foundation of China (81571664, 81871323, 81801665, 81227901, 81771924, 81501616, 81671854), the National Natural Science Foundation of Guangdong Province (2018B030311024), the Scientific Research General Project of Guangzhou Science Technology and Innovation Commission (201707010328), the China Postdoctoral Science Foundation (2016 M600145), and the Beijing Natural Science Foundation (L182061).

Compliance with ethical standards


The scientific guarantor of this publication is Shuixing Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study/observational

• performed at one institution

Supplementary material

330_2019_6452_MOESM1_ESM.docx (168 kb)
ESM 1 (DOCX 167 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Xiaokai Mo
    • 1
    • 2
  • Xiangjun Wu
    • 3
    • 4
  • Di Dong
    • 3
    • 4
  • Baoliang Guo
    • 1
  • Changhong Liang
    • 1
  • Xiaoning Luo
    • 1
    • 5
  • Bin Zhang
    • 6
  • Lu Zhang
    • 1
  • Yuhao Dong
    • 1
    • 2
  • Zhouyang Lian
    • 1
  • Jing Liu
    • 1
  • Shufang Pei
    • 1
  • Wenhui Huang
    • 1
  • Fusheng Ouyang
    • 1
  • Jie Tian
    • 3
    • 4
    • 7
    Email author
  • Shuixing Zhang
    • 6
    Email author
  1. 1.Department of RadiologyGuangdong Provincial People’s Hospital/Guangdong Academy of Medical SciencesGuangzhouPeople’s Republic of China
  2. 2.Shantou University Medical CollegeShantouPeople’s Republic of China
  3. 3.CAS Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingPeople’s Republic of China
  4. 4.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  5. 5.Department of Otolaryngology-Head and Neck SurgeryGuangdong Provincial People’s Hospital/Guangdong Academy of Medical SciencesGuangzhouPeople’s Republic of China
  6. 6.Department of Radiology, The First Affiliated HospitalJinan UniversityGuangzhouPeople’s Republic of China
  7. 7.Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Medicine, Beihang UniversityBeijingPeople’s Republic of China

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