Top

Published in:

Open Access 01-11-2020 | Magnetic Resonance Imaging | Head and Neck

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures

Authors: Steven W. Mes, Floris H. P. van Velden, Boris Peltenburg, Carel F. W. Peeters, Dennis E. te Beest, Mark A. van de Wiel, Joost Mekke, Doriene C. Mulder, Roland M. Martens, Jonas A. Castelijns, Frank A. Pameijer, Remco de Bree, Ronald Boellaard, C. René Leemans, Ruud H. Brakenhoff, Pim de Graaf

Published in: European Radiology | Issue 11/2020

Abstract

Objectives

Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer.

Materials and Methods

Native T1-weighted images of four independent, retrospective (2005–2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC).

Results

In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001).

Conclusions

MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters.

Key Points

• MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer.

• MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models.

• Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

Available only for authorised users

Forastiere A, Koch W, Trotti A, Sidransky D (2001) Head and neck cancer. N Engl J Med 345:1890–1900PubMed

Surveillance E, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1975-2016), National Cancer Institute, DCCPS, Surveillance Research Program, released April 2019, based on the November 2018 submission

Murphy BA, Deng J (2015) Advances in supportive care for late effects of head and neck cancer. J Clin Oncol 33:3314–3321PubMed

Mes SW, Leemans CR, Brakenhoff RH (2016) Applications of molecular diagnostics for personalized treatment of head and neck cancer: state of the art. Expert Rev Mol Diagn 16:205–221PubMed

Leemans CR, Snijders PJF, Brakenhoff RH (2018) The molecular landscape of head and neck cancer. Nat Rev Cancer 18:269–282PubMed

Gevaert O, Xu J, Hoang CD et al (2012) Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology 264:387–396PubMedPubMedCentral

O’Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21:249–257PubMed

Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248PubMedPubMedCentral

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMed

10.

Parmar C, Leijenaar RT, Grossmann P et al (2015) Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep 5:11044PubMedPubMedCentral

11.

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 7:13206PubMedPubMedCentral

12.

Vallieres M, Kay-Rivest E, Perrin LJ et al (2017) Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 7:10117PubMedPubMedCentral

13.

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 56:1531–1536PubMed

14.

Forghani R, Chatterjee A, Reinhold C et al (2019) Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning. Eur Radiol 29:6172–6181PubMed

15.

Jethanandani A, Lin TA, Volpe S et al (2018) Exploring applications of radiomics in magnetic resonance imaging of head and neck cancer: a systematic review. Front Oncol 8:131PubMedPubMedCentral

16.

Nooij RP, Hof JJ, van Laar PJ, van der Hoorn A (2018) Functional MRI for treatment evaluation in patients with head and neck squamous cell carcinoma: a review of the literature from a radiologist perspective. Curr Radiol Rep 6:2PubMedPubMedCentral

17.

Li H, Zhu Y, Burnside ES et al (2016) Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. https://doi.org/10.1038/npjbcancer.2016.12

18.

Gevaert O, Mitchell LA, Achrol AS et al (2014) Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273:168–174PubMed

19.

Gnep K, Fargeas A, Gutierrez-Carvajal RE et al (2017) Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging 45:103–117PubMed

20.

Dang M, Lysack JT, Wu T et al (2015) MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma. AJNR Am J Neuroradiol 36:166–170PubMedPubMedCentral

21.

Jansen JF, Lu Y, Gupta G et al (2016) Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer. World J Radiol 8:90–97PubMedPubMedCentral

22.

Yuan Y, Ren J, Shi Y, Tao X (2019) MRI-based radiomic signature as predictive marker for patients with head and neck squamous cell carcinoma. Eur J Radiol 117:193–198PubMed

23.

Ren J, Tian J, Yuan Y et al (2018) Magnetic resonance imaging based radiomics signature for the preoperative discrimination of stage I-II and III-IV head and neck squamous cell carcinoma. Eur J Radiol 106:1–6PubMed

24.

Hayes DN, Van Waes C, Seiwert TY (2015) Genetic landscape of human papillomavirus-associated head and neck cancer and comparison to tobacco-related tumors. J Clin Oncol 33:3227–3234PubMedPubMedCentral

25.

Leijenaar RT, Bogowicz M, Jochems A et al (2018) Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. Br J Radiol 91:20170498PubMedPubMedCentral

26.

Nauta IH, Rietbergen MM, van Bokhoven A et al (2018) Evaluation of the eighth TNM classification on p16-positive oropharyngeal squamous cell carcinomas in the Netherlands and the importance of additional HPV DNA testing. Ann Oncol 29:1273–1279PubMed

27.

Brierley JD, Gospodarowicz MK, Wittekind C (eds) (2016) TNM Classification of malignant tumours, 8th edn. Wiley-Blackwell, Hoboken

28.

Piccirillo JF, Tierney RM, Costas I, Grove L, Spitznagel EL Jr (2004) Prognostic importance of comorbidity in a hospital-based cancer registry. JAMA 291:2441–2447PubMed

29.

Bol GH, Kotte AN, van der Heide UA, Lagendijk JJ (2009) Simultaneous multi-modality ROI delineation in clinical practice. Comput Methods Programs Biomed 96:133–140PubMed

30.

Peeters CFW, Übelhör C, Mes SW et al (2019) Stable prediction with radiomics data. arXiv:1903.11696 [stat.ML]

31.

Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91PubMed

32.

Zhao B, Tan Y, Tsai WY et al (2016) Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 6:23428PubMedPubMedCentral

33.

Balagurunathan Y, Gu Y, Wang H et al (2014) Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol 7:72–87PubMedPubMedCentral

34.

Mao J, Fang J, Duan X et al (2019) Predictive value of pretreatment MRI texture analysis in patients with primary nasopharyngeal carcinoma. Eur Radiol 29:4105–4113PubMedPubMedCentral

35.

Zhang B, He X, Ouyang F et al (2017) Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 403:21–27PubMed

36.

Zhang B, Ouyang F, Gu D et al (2017) Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget 8:72457–72465PubMedPubMedCentral

37.

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 23:4259–4269PubMed

38.

Zhuo EH, Zhang WJ, Li HJ et al (2019) Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups. Eur Radiol 29:5590–5599PubMed

39.

Zhao L, Gong J, Xi Y et al (2020) MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma. Eur Radiol 30:537–546PubMed

40.

Bruce JP, Yip K, Bratman SV, Ito E, Liu FF (2015) Nasopharyngeal cancer: molecular landscape. J Clin Oncol 33:3346–3355PubMed

41.

Marcu LG, Reid P, Bezak E (2018) The promise of novel biomarkers for head and neck cancer from an imaging perspective. Int J Mol Sci. https://doi.org/10.3390/ijms190925

42.

Rivera C, Oliveira AK, Costa RAP, De Rossi T, Paes Leme AF (2017) Prognostic biomarkers in oral squamous cell carcinoma: a systematic review. Oral Oncol 72:38–47PubMed

43.

Sala E, Mema E, Himoto Y et al (2017) Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol 72:3–10PubMed

44.

Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ (2015) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol 5:272PubMedPubMedCentral

45.

Mes SW, Te Beest D, Poli T et al (2017) Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables. Oncotarget 8:59312–59323PubMedPubMedCentral

46.

Moeckelmann N, Ebrahimi A, Tou YK et al (2018) Prognostic implications of the 8th edition American Joint Committee on Cancer (AJCC) staging system in oral cavity squamous cell carcinoma. Oral Oncol 85:82–86PubMed

Title: Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures
Authors: Steven W. Mes
Floris H. P. van Velden
Boris Peltenburg
Carel F. W. Peeters
Dennis E. te Beest
Mark A. van de Wiel
Joost Mekke
Doriene C. Mulder
Roland M. Martens
Jonas A. Castelijns
Frank A. Pameijer
Remco de Bree
Ronald Boellaard
C. René Leemans
Ruud H. Brakenhoff
Pim de Graaf
Publication date: 01-11-2020
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Oropharyngeal Cancer
Published in: European Radiology / Issue 11/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-06962-y

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures

Abstract

Objectives

Materials and Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Materials and Methods

Results

Conclusions

Key Points

Please log in to get access to this content

Other articles of this Issue 11/2020

Early chest CT features of patients with 2019 novel coronavirus (COVID-19) pneumonia: relationship to diagnosis and prognosis

Feasibility of unconstrained three-material decomposition: imaging an excised human heart using a prototype silicon photon-counting CT detector

Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions

Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms

Advanced myocardial characterization in hypertrophic cardiomyopathy: feasibility of CMR-based feature tracking strain analysis in a case-control study