Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes

  • Shang-Wen Chen
  • Wei-Chih Shen
  • Ying-Chun Lin
  • Rui-Yun Chen
  • Te-Chun Hsieh
  • Kuo-Yang Yen
  • Chia-Hung Kao
Original Article



This study investigated the correlation of the matrix heterogeneity of tumors on 18F-fluorodeoxyglucose positron emission tomography–computed tomography (PET–CT) with gene-expression profiling in patients with pharyngeal cancer and determined the prognostic factors for radiotherapy-based treatment outcomes.


We retrospectively reviewed the records of 57 patients with stage III–IV oropharyngeal or hypopharyngeal cancer who had completed definitive therapy. Four groups of the textural features as well as 31 indices were studied in addition to maximum standard uptake value, metastatic tumor volume, and total lesion glycolysis. Immunohistochemical data from pretreatment biopsy specimens (Glut1, CAIX, VEGF, HIF-1α, EGFR, Ki-67, Bcl-2, CLAUDIN-4, YAP-1, c-Met, and p16) were analyzed. The relationships between the indices and genomic expression were studied, and the robustness of various textural features relative to cause-specific survival and primary relapse-free survival was analyzed.


The overexpression of VEGF was positively associated with the increased values of the matrix heterogeneity obtained using gray-level nonuniformity for zone (GLNUz) and run-length nonuniformity (RLNU). Advanced T stage (p = 0.01, hazard ratio [HR] = 3.38), a VEGF immunoreactive score of >2 (p = 0.03, HR = 2.79), and a higher GLNUz value (p = 0.04, HR = 2.51) were prognostic factors for low cause-specific survival, whereas advanced T stage, a HIF-1α staining percentage of ≥80%, and a higher GLNUz value were prognostic factors for low primary-relapse free survival.


The overexpression of VEGF was associated with the increased matrix index of GLNUz and RLNU. For patients with pharyngeal cancer requiring radiotherapy, the treatment outcome can be stratified according to the textural features, T stage, and biomarkers.


Head and neck cancer 18F-fluorodeoxyglucose positron emission Textural analysis Genomic expression 







Clinical target volumes


Gray-level nonuniformity for zone


Gross tumor volume


Head and neck squamous cell carcinoma


Human papilloma virus


Metastatic tumor volume


Positron emission tomography-computed tomography


Run-length nonuniformity




Standardized uptake value


Total lesion glycolysis


  1. 1.
    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.CrossRefPubMedGoogle Scholar
  2. 2.
    Gerlinger M, Swanton C. How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br J Cancer. 2010;103:1139–43.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52:369–78.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012;102:239–45.CrossRefPubMedGoogle Scholar
  5. 5.
    Tan S, Kligerman S, Chen W, Lu M, Kim G, Feigenberg S, et al. Spatial-temporal [18 F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys. 2013;85:1375–82.CrossRefPubMedGoogle Scholar
  6. 6.
    Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26.CrossRefPubMedGoogle Scholar
  7. 7.
    Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, Groheux D, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med. 2015;56:38–44.CrossRefPubMedGoogle Scholar
  8. 8.
    Ohri N, Duan F, Snyder BS, Wei B, Machtay M, Alavi A, et al. Pretreatment 18FDG-PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235. J Nucl Med. 2016;57:842–8.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Yang F, Thomas MA, Dehdashti F, Grigsby PW. Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging. 2013;40:716–27.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Cheng NM, Fang YH, Lee LY, Chang JT, Tsan DL, Ng SH, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015;42:419–28.CrossRefPubMedGoogle Scholar
  11. 11.
    Wang HM, Cheng NM, Lee LY, Fang YH, Chang JT, Tsan DL, et al. Heterogeneity of (18) F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma. Int J Cancer. 2016;138:731–8.CrossRefPubMedGoogle Scholar
  12. 12.
    Orlhac F, Soussan M, Maisonobe JA, Garcia CA, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med. 2014;55:414–22.CrossRefPubMedGoogle Scholar
  13. 13.
    Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol. 2014;87:1042.CrossRefGoogle Scholar
  14. 14.
    Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-Tân PF, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363:24–35.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Akervall J, Nandalur S, Zhang J, Qian CN, Goldstein N, Gyllerup P, et al. A novel panel of biomarkers predicts radioresistance in patients with squamous cell carcinoma of the head and neck. Eur J Cancer. 2014;50:570–81.CrossRefPubMedGoogle Scholar
  16. 16.
    Lallemant B, Evrard A, Chambon G, Sabra O, Kacha S, Lallemant JG, et al. Gene expression profiling in head and neck squamous cell carcinoma: clinical perspectives. Head Neck. 2010;32:1712–9.CrossRefPubMedGoogle Scholar
  17. 17.
    Vordermark D, Brown JM. Endogenous markers of tumor hypoxia predictors of clinical radiation resistance? Strahlenther Onkol. 2003;179:801–11.CrossRefPubMedGoogle Scholar
  18. 18.
    Kumar B, Cordell KG, Lee JS, Worden FP, Prince ME, Tran HH, et al. EGFR, p16, HPV titer, Bcl-xL and p53, sex, and smoking as indicators of response to therapy and survival in oropharyngeal cancer. J Clin Oncol. 2008;26:3128–37.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Smith BD, Smith GL, Carter D, Sasaki CT, Haffty BG. Prognostic significance of vascular endothelial growth factor protein levels in oral and oropharyngeal squamous cell carcinoma. J Clin Oncol. 2000;18:2046–52.PubMedGoogle Scholar
  20. 20.
    Brown JM, William WR. Exploiting tumour hypoxia in cancer treatment. Nat Rev Cancer. 2004;4:437–47.CrossRefPubMedGoogle Scholar
  21. 21.
    Kyzas PA, Stefanou D, Batistatou A, Agnantis NJ. Prognostic significance of VEGF immunohistochemical expression and tumor angiogenesis in head and neck squamous cell carcinoma. J Cancer Res Clin Oncol. 2005;131:624–30.CrossRefPubMedGoogle Scholar
  22. 22.
    Klimowicz AC, Bose P, Petrillo SK, Magliocco AM, Dort JC, Brockton NT. The prognostic impact of a combined carbonic anhydrase IX and Ki-67 signature in oral squamous cell carcinoma. Br J Cancer. 2013;109:1859–86.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hong A, Dobbins T, Lee CS, Jones D, Jackson E, Clark J, et al. Relationships between epidermal growth factor receptor expression and human papillomavirus status as markers of prognosis in oropharyngeal cancer. Eur J Cancer. 2010;46:2088–96.CrossRefPubMedGoogle Scholar
  24. 24.
    Couture C, Raybaud-Diogène H, Têtu B, Bairati I, Murry D, Allard J, et al. p53 and Ki-67 as markers of radioresistance in head and neck carcinoma. Cancer. 2002;94:713–22.CrossRefPubMedGoogle Scholar
  25. 25.
    Chen SW, Hsieh TC, Yen KY, Liang JA, Kao CH. Pretreatment 18F-FDG PET/CT in whole body total lesion glycolysis to predict survival in patients with pharyngeal cancer treated with definitive radiotherapy. Clin Nucl Med. 2014;39:e296–300.CrossRefPubMedGoogle Scholar
  26. 26.
    Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med. 2005;46:1342–8.PubMedGoogle Scholar
  27. 27.
    Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3:610–21.CrossRefGoogle Scholar
  28. 28.
    Sun C, Wee WG. Neighboring gray level dependence matrix for texture classification. Comput Vis Graph Image Process. 1983;23:341–52.CrossRefGoogle Scholar
  29. 29.
    Loh H, Leu J, Luo R. The analysis of natural textures using run length features. IEEE Trans Ind Electron. 1988;35:323–8.CrossRefGoogle Scholar
  30. 30.
    Thibault G, Fertil B, Navarro C, Pereira S, Cau P, Levy N, et al. Texture indexes and gray level size zone matrix: application to cell nuclei classification. Pattern Recogn Inf Process. 2009;140–145.Google Scholar
  31. 31.
    Lin SC, Liao CY, Kao CH, Yen KY, Yang SN, Wang YC, et al. Pretreatment maximal standardized uptake value of the primary tumor predicts outcome to radiotherapy in patients with pharyngeal cancer. J Radiat Res. 2012;53:462–8.CrossRefPubMedGoogle Scholar
  32. 32.
    Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Brooks FJ. On some misconceptions about tumor heterogeneity quantification. Eur J Nucl Med Mol Imaging. 2013;40:1292–4.CrossRefPubMedGoogle Scholar
  34. 34.
    Nyflot MJ, Kruser TJ, Traynor AM, Khuntia D, Yang DT, Hartig GK, et al. Phase 1 trial of bevacizumab with concurrent chemoradiation therapy for squamous cell carcinoma of the head and neck with exploratory functional imaging of tumor hypoxia, proliferation, and perfusion. Int J Radiat Oncol Biol Phys. 2015;91:942–51.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Fleming IN, Manavaki R, Blower PJ, West C, Williams KJ, Harris AL, et al. Imaging tumour hypoxia with positron emission tomography. Br J Cancer. 2015;112:238–50.CrossRefPubMedGoogle Scholar
  36. 36.
    Welsh L, Panek R, McQuaid D, Dunlop A, Schmidt M, Riddell A, et al. Prospective, longitudinal, multi-modal functional imaging for radical chemo-IMRT treatment of locally advanced head and neck cancer: the INSIGHT study. Radiat Oncol. 2015;10:112.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Shang-Wen Chen
    • 1
    • 2
    • 3
    • 4
  • Wei-Chih Shen
    • 5
    • 6
  • Ying-Chun Lin
    • 1
    • 7
  • Rui-Yun Chen
    • 8
  • Te-Chun Hsieh
    • 9
    • 10
  • Kuo-Yang Yen
    • 9
    • 10
  • Chia-Hung Kao
    • 4
    • 9
    • 11
  1. 1.Department of Radiation OncologyChina Medical University HospitalTaichungTaiwan
  2. 2.School of MedicineChina Medical UniversityTaichungTaiwan
  3. 3.School of MedicineTaipei Medical UniversityTaipeiTaiwan
  4. 4.Graduate Institute of Clinical Medical Science, School of Medicine, College of MedicineChina Medical UniversityTaichungTaiwan
  5. 5.Cancer Center and Department of Medical Research, China Medical University HospitalChina Medical UniversityTaichungTaiwan
  6. 6.Department of Computer Science and Information EngineeringAsia UniversityTaichungTaiwan
  7. 7.The Ph.D. Program for Cancer Biology and Drug DiscoveryChina Medical University and Academia SinicaTaichungTaiwan
  8. 8.Department of PathologyChina Medical University HospitalTaichungTaiwan
  9. 9.Department of Nuclear Medicine and PET CenterChina Medical University HospitalTaichungTaiwan
  10. 10.Department of Biomedical Imaging and Radiological ScienceChina Medical UniversityTaichungTaiwan
  11. 11.Department of Bioinformatics and Medical EngineeringAsia UniversityTaichungTaiwan

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