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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

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.

Keywords

Head and neck cancer Hypopharynx Chemoradiotherapy Recurrence Prognosis 

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

Notes

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).

Compliance with ethical standards

Guarantor

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.

Methodology

• 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)

References

  1. 1.
    American Cancer Society (2018) Cancer Facts and Figures 2018. https://www.cancer.org/cancer-facts-and-figures-2018.pdf. Accessed August 1, 2018.
  2. 2.
    Kuo P, Chen MM, Decker RH, Yarbrough WG, Judson BL (2014) Hypopharyngeal cancer incidence, treatment, and survival: Temporal trends in the United States. Laryngoscope 124:2064–2069CrossRefGoogle Scholar
  3. 3.
    American Cancer Society (2017) Cancer Facts and Figures 2017. https://www.cancer.org/cancer-facts-and-figures-2017.pdf. Accessed August 1, 2018.
  4. 4.
    Bar-Ad V, Palmer J, Yang H et al (2014) Current management of locally advanced head and neck cancer: the combination of chemotherapy with locoregional treatments. Semin Oncol 41:798–806CrossRefGoogle Scholar
  5. 5.
    Kuo P, Sosa JA, Burtness BA et al (2016) Treatment trends and survival effects of chemotherapy for hypopharyngeal cancer: Analysis of the National Cancer Data Base. Cancer 122:1853–1860CrossRefGoogle Scholar
  6. 6.
    Takes RP, Strojan P, Silver CE et al (2012) Current trends in initial management of hypopharyngeal cancer: the declining use of open surgery. Head Neck 34:270–281CrossRefGoogle Scholar
  7. 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
  8. 8.
    Pagh A, Grau C, Overgaard J (2016) Failure pattern and salvage treatment after radical treatment of head and neck cancer. Acta Oncol 55:625–632CrossRefGoogle Scholar
  9. 9.
    Matoscevic K, Graf N, Pezier TF, Huber GF (2014) Success of salvage treatment: a critical appraisal of salvage rates for different subsites of HNSCC. Otolaryngol Head Neck Surg 151:454–461CrossRefGoogle Scholar
  10. 10.
    Forastiere AA, Adelstein DJ, Manola J (2013) Induction chemotherapy meta-analysis in head and neck cancer: right answer, wrong question. J Clin Oncol 31:2844–2846CrossRefGoogle Scholar
  11. 11.
    Beitler JJ, Zhang Q, Fu KK et al (2014) Final results of local-regional control and late toxicity of RTOG 9003: a randomized trial of altered fractionation radiation for locally advanced head and neck cancer. Int J Radiat Oncol Biol Phys 89:13–20CrossRefGoogle Scholar
  12. 12.
    Ng SH, Liao CT, Lin CY et al (2016) Dynamic contrast-enhanced MRI, diffusion-weighted MRI and (18)F-FDG PET/CT for the prediction of survival in oropharyngeal or hypopharyngeal squamous cell carcinoma treated with chemoradiation. Eur Radiol 26:4162–4172CrossRefGoogle Scholar
  13. 13.
    Pak K, Cheon GJ, Kang KW, Chung JK, Kim EE, Lee DS (2015) Prognostic value of SUVmean in oropharyngeal and hypopharyngeal cancers: comparison with SUVmax and other volumetric parameters of 18F-FDG PET. Clin Nucl Med 40:9–13CrossRefGoogle Scholar
  14. 14.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefGoogle Scholar
  15. 15.
    Bartoschek M, Oskolkov N, Bocci M et al (2018) Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. Nat Commun 9:5150CrossRefGoogle Scholar
  16. 16.
    Parmar C, Leijenaar RT, Grossmann P et al (2015) Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Sci Rep 5:11044CrossRefGoogle Scholar
  17. 17.
    Zhang B, Tian J, Dong D et al (2017) Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma. Clin Cancer Res 23:4259–4269CrossRefGoogle Scholar
  18. 18.
    Rose BS, Jeong JH, Nath SK, Lu SM, Mell LK (2011) Population-based study of competing mortality in head and neck cancer. J Clin Oncol 29:3503–3509CrossRefGoogle Scholar
  19. 19.
    Leger S, Zwanenburg A, Pilz K et al (2017) A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling. Sci Rep 7:13206CrossRefGoogle Scholar
  20. 20.
    Bogowicz M, Riesterer O, Stark LS et al (2017) Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta Oncol 56:1531–1536CrossRefGoogle Scholar
  21. 21.
    Royston P, Altman DG (2013) External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol 13:33CrossRefGoogle Scholar
  22. 22.
    Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17:1471–1474CrossRefGoogle Scholar
  23. 23.
    Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefGoogle Scholar
  24. 24.
    Chen SW, Shen WC, Lin YC et al (2017) Correlation of pretreatment (18)F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes. Eur J Nucl Med Mol Imaging 44:567–580CrossRefGoogle Scholar
  25. 25.
    Leijenaar RT, Carvalho S, Hoebers FJ et al (2015) External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol 54:1423–1429CrossRefGoogle Scholar
  26. 26.
    Song J, Shi J, Dong D et al (2018) A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy. Clin Cancer Res 24:3583–3592CrossRefGoogle Scholar
  27. 27.
    Cheng NM, Fang YH, Lee LY et al (2015) Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging 42:419–428CrossRefGoogle Scholar
  28. 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
  29. 29.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefGoogle Scholar
  30. 30.
    Patel UA, Howell LK (2011) Local response to chemoradiation in T4 larynx cancer with cartilage invasion. Laryngoscope 121:106–110CrossRefGoogle Scholar
  31. 31.
    Gong EJ, Kim DH, Ahn JY et al (2016) Routine endoscopic screening for synchronous esophageal neoplasm in patients with head and neck squamous cell carcinoma: a prospective study. Dis Esophagus 29:752–759CrossRefGoogle Scholar
  32. 32.
    Kim SY, Rho YS, Choi EC et al (2017) Clinicopathological factors influencing the outcomes of surgical treatment in patients with T4a hypopharyngeal cancer. BMC Cancer 17:904CrossRefGoogle Scholar
  33. 33.
    Scherl C, Mantsopoulos K, Semrau S et al (2017) Management of advanced hypopharyngeal and laryngeal cancer with and without cartilage invasion. Auris Nasus Larynx 44:333–339CrossRefGoogle Scholar
  34. 34.
    Zhou H, Dong D, Chen B et al (2018) Diagnosis of distant metastasis of lung cancer: based on clinical and radiomic features. Transl Oncol 11:31–36CrossRefGoogle Scholar
  35. 35.
    Ho AS, Kim S, Tighiouart M et al (2018) Association of Quantitative Metastatic Lymph Node Burden With Survival in Hypopharyngeal and Laryngeal Cancer. JAMA Oncol 4:985–989CrossRefGoogle Scholar
  36. 36.
    Speight PM, Abram TJ, Floriano PN et al (2015) Interobserver agreement in dysplasia grading: toward an enhanced gold standard for clinical pathology trials. Oral Surg Oral Med Oral Pathol Oral Radiol 120:474–482 e472CrossRefGoogle Scholar
  37. 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. 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. 39.
    Chaddad A, Kucharczyk MJ, Niazi T (2018) Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer. Cancers (Basel) 10Google Scholar

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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