Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation
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.
• 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.
KeywordsHead 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
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.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study/observational
• performed at one institution
- 1.American Cancer Society (2018) Cancer Facts and Figures 2018. https://www.cancer.org/cancer-facts-and-figures-2018.pdf. Accessed August 1, 2018.
- 3.American Cancer Society (2017) Cancer Facts and Figures 2017. https://www.cancer.org/cancer-facts-and-figures-2017.pdf. Accessed August 1, 2018.
- 7.Hamoir M, Schmitz S, Suarez C et al (2018) The Current Role of Salvage Surgery in Recurrent Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 10Google Scholar
- 28.Leger S, Zwanenburg A, Pilz K et al (2018) CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer. Radiother Oncol. https://doi.org/10.1016/j.radonc.2018.07.020
- 37.Park YW, Oh J, You SC et al (2018) Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol. https://doi.org/10.1007/s00330-018-5830-3
- 38.Su C, Jiang J, Zhang S et al (2018) Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour. Eur Radiol. https://doi.org/10.1007/s00330-018-5704-8
- 39.Chaddad A, Kucharczyk MJ, Niazi T (2018) Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer. Cancers (Basel) 10Google Scholar