Advertisement

European Radiology

, Volume 30, Issue 1, pp 537–546 | Cite as

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

  • Lina Zhao
  • Jie Gong
  • Yibin Xi
  • Man Xu
  • Chen Li
  • Xiaowei Kang
  • Yutian Yin
  • Wei QinEmail author
  • Hong Yin
  • Mei ShiEmail author
Imaging Informatics and Artificial Intelligence

Abstract

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.

Key Points

MRI Radiomics can predict IC response and survival in non-endemic NPC.

Radiomics signature in combination with clinical data showed excellent predictive performance.

Radiomics signature could separate patients into two groups with different prognosis.

Keywords

Nasopharyngeal carcinoma Magnetic resonance imaging Radiomics Machine learning Induction chemotherapy 

Abbreviations

CCRT

Concurrent chemoradiation

IC

Induction chemotherapy

IMRT

Intensity-modulated radiotherapy

LASSO

Least absolute shrinkage and selection operator

NPC

Nasopharyngeal carcinoma

PFS

Progression-free survival

RF

Random forest

SVM

Support vector machine

T1-C

T1 contrast

Notes

Funding

This study has received funding by the National Natural Science Foundation of China Grants 81872699 and Key project of Shanxi Province 2017ZDXM-SF-043.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Lina Zhao.

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

Yutian Yin, one of the authors, has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because the retrospective nature of the study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6211_MOESM1_ESM.docx (1.2 mb)
ESM 1 (DOCX 1217 kb)

References

  1. 1.
    Zang J, Li C, Zhao LN et al (2016) Prognostic model of death and distant metastasis for nasopharyngeal carcinoma patients receiving 3DCRT/IMRT in nonendemic area of China. Medicine (Baltimore) 95:e3794CrossRefGoogle Scholar
  2. 2.
    Zhao LN, Zhou B, Shi M et al (2012) Clinical outcome for nasopharyngeal carcinoma with predominantly WHO II histology treated with intensity-modulated radiation therapy in non-endemic region of China. Oral Oncol 48:864–869CrossRefGoogle Scholar
  3. 3.
    Al-Sarraf M, LeBlanc M, Giri PG et al (1998) Chemoradiotherapy versus radiotherapy in patients with advanced nasopharyngeal cancer: phase III randomized intergroup study 0099. J Clin Oncol 16:1310–1317CrossRefGoogle Scholar
  4. 4.
    Wang J, Shi M, Hsia Y et al (2012) Failure patterns and survival in patients with nasopharyngeal carcinoma treated with intensity modulated radiation in Northwest China: a pilot study. Radiat Oncol 7:2CrossRefGoogle Scholar
  5. 5.
    Wee CW, Keam B, Heo DS, Sung MW, Won TB, Wu HG (2015) Locoregionally advanced nasopharyngeal carcinoma treated with intensity-modulated radiotherapy plus concurrent weekly cisplatin with or without neoadjuvant chemotherapy. Radiat Oncol J 33:98–108CrossRefGoogle Scholar
  6. 6.
    Blanchard P, Lee A, Marguet S et al (2015) Chemotherapy and radiotherapy in nasopharyngeal carcinoma: an update of the MAC-NPC meta-analysis. Lancet Oncol 16:645–655CrossRefGoogle Scholar
  7. 7.
    Cao SM, Yang Q, Guo L et al (2017) Neoadjuvant chemotherapy followed by concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locoregionally advanced nasopharyngeal carcinoma: a phase III multicentre randomised controlled trial. Eur J Cancer 75:14–23CrossRefGoogle Scholar
  8. 8.
    Zhao L, Xu M, Jiang W et al (2017) Induction chemotherapy for the treatment of non-endemic locally advanced nasopharyngeal carcinoma. Oncotarget 8:6763–6774PubMedGoogle Scholar
  9. 9.
    Peng H, Chen L, Zhang Y et al (2016) The tumour response to induction chemotherapy has prognostic value for long-term survival outcomes after intensity-modulated radiation therapy in nasopharyngeal carcinoma. Sci Rep 6:24835CrossRefGoogle Scholar
  10. 10.
    Zhang GY, Wang YJ, Liu JP et al (2015) Pretreatment diffusion-weighted MRI can predict the response to neoadjuvant chemotherapy in patients with nasopharyngeal carcinoma. Biomed Res Int 2015:307943PubMedPubMedCentralGoogle Scholar
  11. 11.
    Yen RF, Chen TH, Ting LL, Tzen KY, Pan MH, Hong RL (2005) Early restaging whole-body (18)F-FDG PET during induction chemotherapy predicts clinical outcome in patients with locoregionally advanced nasopharyngeal carcinoma. Eur J Nucl Med Mol Imaging 32:1152–1159CrossRefGoogle Scholar
  12. 12.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefGoogle Scholar
  13. 13.
    Verma V, Simone CB 2nd, Krishnan S, Lin SH, Yang J, Hahn SM (2017) The rise of radiomics and implications for oncologic management. J Natl Cancer Inst 109Google Scholar
  14. 14.
    Mao J, Fang J, Duan X et al (2019) Predictive value of pretreatment MRI texture analysis in patients with primary nasopharyngeal carcinoma. Eur Radiol.  https://doi.org/10.1007/s00330-018-5961-6 CrossRefGoogle Scholar
  15. 15.
    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.  https://doi.org/10.1158/1078-0432.CCR-16-2910 CrossRefGoogle Scholar
  16. 16.
    Wang G, He L, Yuan C, Huang Y, Liu Z, Liang C (2018) Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma. Eur J Radiol 98:100–106CrossRefGoogle Scholar
  17. 17.
    Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247CrossRefGoogle Scholar
  18. 18.
    Sauerbrei W, Royston P, Binder H (2007) Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 26:5512–5528CrossRefGoogle Scholar
  19. 19.
    Smyser CD, Dosenbach NU, Smyser TA et al (2016) Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 136:1–9CrossRefGoogle Scholar
  20. 20.
    Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res 23:7253–7262CrossRefGoogle Scholar
  21. 21.
    Contal COQJ (1999) An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 30:253–270CrossRefGoogle Scholar
  22. 22.
    Cui Z, Xia Z, Su M, Shu H, Gong G (2016) Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach. Hum Brain Mapp 37:1443–1458CrossRefGoogle Scholar
  23. 23.
    Liao XB, Mao YP, Liu LZ et al (2008) How does magnetic resonance imaging influence staging according to AJCC staging system for nasopharyngeal carcinoma compared with computed tomography? Int J Radiat Oncol Biol Phys 72:1368–1377CrossRefGoogle Scholar
  24. 24.
    Liu J, Mao Y, Li Z et al (2016) Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging 44:445–455CrossRefGoogle Scholar
  25. 25.
    Zhang B, Ouyang F, Gu D et al (2017) Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget 8:72457–72465PubMedPubMedCentralGoogle Scholar
  26. 26.
    Panth KM, Leijenaar RT, Carvalho S et al (2015) Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells. Radiother Oncol 116:462–466CrossRefGoogle Scholar
  27. 27.
    Diehn M, Nardini C, Wang DS et al (2008) Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 105:5213–5218CrossRefGoogle Scholar
  28. 28.
    Lee J, Narang S, Martinez J, Rao G, Rao A (2015) Spatial habitat features derived from multiparametric magnetic resonance imaging data are associated with molecular subtype and 12-month survival status in glioblastoma multiforme. PLoS One 10:e0136557CrossRefGoogle Scholar
  29. 29.
    Zhou M, Leung A, Echegaray S et al (2017) Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology.  https://doi.org/10.1148/radiol.2017161845:161845
  30. 30.
    Mani S, Chen Y, Li X et al (2013) Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. J Am Med Inform Assoc 20:688–695CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Lina Zhao
    • 1
  • Jie Gong
    • 2
  • Yibin Xi
    • 3
  • Man Xu
    • 1
  • Chen Li
    • 3
  • Xiaowei Kang
    • 3
  • Yutian Yin
    • 1
  • Wei Qin
    • 2
    Email author
  • Hong Yin
    • 3
  • Mei Shi
    • 1
    Email author
  1. 1.Department of Radiation Oncology, Xijing HospitalAir Force Medical UniversityXi’anChina
  2. 2.Life Sciences Research Center, School of Life Sciences and TechnologyXidian UniversityXi’anChina
  3. 3.Department of Radiology, Xijing HospitalAir Force Medical UniversityXi’anChina

Personalised recommendations