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

  • Head and Neck
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Abstract

Purpose

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.

Results

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.

Conclusions

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.

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Abbreviations

CECT:

Contrast-enhanced computed tomography

DCE-MRI:

Dynamic contrast-enhanced magnetic resonance imaging

HNSCC:

Head and neck squamous cell carcinoma

ICC:

Intra-/inter-class correlation coefficient

NCCT:

Non-contrast computed tomography

PET-CT:

Positron emission tomography–computed tomography

PFS:

Progression-free survival

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Acknowledgments

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).

Funding

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).

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Author notes

  1. Xiaokai Mo and Xiangjun Wu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Guangdong Provincial People’s Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People’s Republic of China

    Xiaokai Mo, Baoliang Guo, Changhong Liang, Xiaoning Luo, Lu Zhang, Yuhao Dong, Zhouyang Lian, Jing Liu, Shufang Pei, Wenhui Huang & Fusheng Ouyang

  2. Shantou University Medical College, Shantou, Guangdong, People’s Republic of China

    Xiaokai Mo & Yuhao Dong

  3. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Hai Dian District, Beijing, 100190, People’s Republic of China

    Xiangjun Wu, Di Dong & Jie Tian

  4. University of Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Xiangjun Wu, Di Dong & Jie Tian

  5. Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People’s Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, People’s Republic of China

    Xiaoning Luo

  6. Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, Guangdong, 510627, People’s Republic of China

    Bin Zhang & Shuixing Zhang

  7. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, People’s Republic of China

    Jie Tian

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Corresponding authors

Correspondence to Jie Tian or Shuixing Zhang.

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The scientific guarantor of this publication is Shuixing Zhang.

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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.

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Mo, X., Wu, X., Dong, D. et al. Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation. Eur Radiol 30, 833–843 (2020). https://doi.org/10.1007/s00330-019-06452-w

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