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



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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3







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.

    CAS  Article  PubMed  Google 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.

    CAS  Article  PubMed  PubMed Central  Google 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.

    Article  PubMed  PubMed Central  Google 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.

    Article  PubMed  Google 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.

    Article  PubMed  Google 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.

    Article  PubMed  Google 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.

    CAS  Article  PubMed  Google 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.

    Article  PubMed  PubMed Central  Google 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.

    CAS  Article  PubMed  PubMed Central  Google 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.

    CAS  Article  PubMed  Google 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.

    CAS  Article  PubMed  Google 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.

    CAS  Article  PubMed  Google 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.

    Article  Google 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.

    CAS  Article  PubMed  PubMed Central  Google 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.

    CAS  Article  PubMed  Google 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.

    Article  PubMed  Google Scholar 

  17. 17.

    Vordermark D, Brown JM. Endogenous markers of tumor hypoxia predictors of clinical radiation resistance? Strahlenther Onkol. 2003;179:801–11.

    Article  PubMed  Google 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.

    Article  PubMed  PubMed Central  Google 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.

    CAS  PubMed  Google Scholar 

  20. 20.

    Brown JM, William WR. Exploiting tumour hypoxia in cancer treatment. Nat Rev Cancer. 2004;4:437–47.

    CAS  Article  PubMed  Google 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.

    CAS  Article  PubMed  Google 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.

    CAS  Article  PubMed  PubMed Central  Google 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.

    CAS  Article  PubMed  Google 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.

    CAS  Article  PubMed  Google 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.

    Article  PubMed  Google 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.

    PubMed  Google Scholar 

  27. 27.

    Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3:610–21.

    Article  Google Scholar 

  28. 28.

    Sun C, Wee WG. Neighboring gray level dependence matrix for texture classification. Comput Vis Graph Image Process. 1983;23:341–52.

    Article  Google 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.

    Article  Google 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.

  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.

    Article  PubMed  Google 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.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Brooks FJ. On some misconceptions about tumor heterogeneity quantification. Eur J Nucl Med Mol Imaging. 2013;40:1292–4.

    Article  PubMed  Google 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.

    CAS  Article  PubMed  PubMed Central  Google 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.

    CAS  Article  PubMed  Google 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.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author’s contributions

SW Chen, WC Shen, and CH Kao were responsible for design of the study. All authors collected the data. SW Chen, WC Shen, and CH Kao carried out statistical analysis, interpretation of data, and drafting the article. All authors provided some intellectual content. SW Chen, WC Shen, and CH Kao approved the version to be submitted. All authors read and approved the final manuscript.

Author information



Corresponding author

Correspondence to Chia-Hung Kao.

Ethics declarations


This study was supported in part by China Medical University Hospital (DMR-104-041), Ministry of Science and Technology (NSC 100-2221-E-468-003 and MOST 105-5518-E-009-034), Taiwan; the Taiwan Ministry of Health and Welfare Clinical Trial and Research Center of Excellence (MOHW105-TDU-B-212-133019); China Medical University Hospital, Academia Sinica Taiwan Biobank Stroke Biosignature Project (BM10501010037); NRPB Stroke Clinical Trial Consortium (MOST 105-2325-B-039-003); Tseng-Lien Lin Foundation (Taichung, Taiwan), Taiwan Brain Disease Foundation (Taipei, Taiwan), and Katsuzo and Kiyo Aoshima Memorial Funds, Japan; CMU under the Aim for Top University Plan of the Ministry of Education, Taiwan; and the Welfare Surcharge of Tobacco Products, China Medical University Hospital Cancer Research Center of Excellence (MOHW105-TDU-B-212-134-003, Taiwan). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.

Conflict of interest

Shang-Wen Chen declares that he/she has no conflict of interest. Wei-Chih Shen declares that he/she has no conflict of interest. Ying-Chun Lin declares that he/she has no conflict of interest. Rui-Yun Chen declares that he/she has no conflict of interest. Te-Chun Hsieh declares that he/she has no conflict of interest. Kuo-Yang Yen declares that he/she has no conflict of interest. Chia-Hung Kao declares that he/she has no conflict of interest.

Ethical approval

This study was approved by a local institutional review board (CMUH103-REC2-093FR).

Informed consent

The IRB also specifically waived the consent requirement.

Additional information

Shang-Wen Chen and Wei-Chih Shen contributed equally to this work.


Appendix 1. Representative images of immunohistochemistry for HIF-1α, VEGF, and Glut1

Fig. 5

Representative figures of membranous or cytoplasmic staining for vascular endothelial growth factor (VEGF). a Weak VEGF staining. b Moderate VEGF staining. c Strong VEGF staining. Original magnification ×100

Fig. 6

Representative figures of membranous staining for Glut-1. a Moderate Glut-1 staining. b Strong Glut-1 staining. Original magnification ×100

Appendix 2

Table 6 Immunohistochemistry results according to the optimal cutoffs for predicting local recurrence in the current series and a similar cohort comprising 57 patients with oropharyngeal or hypopharyngeal cancer

Appendix 3

Table 7 Correlation between the GTV and the calculated values of textural indices using Pearson correlation analysis

Appendix 4. Detailed data on the role of VEGF in pharyngeal cancer regarding to the textural features and treatment outcome

  1. 1)

    Primary relapse-free survival in patients who had tumors with a VEGF IRS of >2 and ≤2 (p = 0.002).

  2. 2)

    Cause-specific survival in oropharyngeal cancer patients who had tumors with a VEGF IRS of >2 and ≤2 (p = 0.005 and p = 0.006, respectively).

  3. 3)


    Indices calculated from the textural analysis and the area under the ROC curve for predicting the expression of VEGF using a cutoff of IRS >2 vs. ≤2.

    Classification of matrix Index Area under the curve
    CT-based volume GTV 0.75 ± 0.07
    Classical PET-related parameter SUVmax 0.57 ± 0.08
    Gray-level co-occurrence matrix (GLCM) MTV2.5 0.71 ± 0.09
    TLG40% 0.61 ± 0.10
    Homogeneity 0.44 ± 0.08
    Energy 0.31 ± 0.08
    Correlation 0.55 ± 0.10
    Contrast 0.61 ± 0.08
    Entropy 0.67 ± 0.08
    Dissimilarity 0.61 ± 0.08
    Gray-level run length matrix (GLRLM) SRE 0.53 ± 0.09
    LRE 0.49 ± 0.09
    LGRE 0.37 ± 0.08
    HGRE 0.61 ± 0.08
    SRLGE 0.37 ± 0.08
    SRHGE 0.60 ± 0.08
    LRLGE 0.39 ± 0.08
    LRHGE 0.64 ± 0.07
    GLNUr 0.66 ± 0.09
    RLNU 0.70 ± 0.09
    RP 0.65 ± 0.09
    Neighborhood gray-level different matrix (NGLDM) Coarseness 0.31 ± 0.09
    Contrast 0.38 ± 0.09
    Busyness 0.51 ± 0.09
    Gray-level zone length matrix (GLSZM) SZE 0.51 ± 0.09
    LZE 0.57 ± 0.09
    LGZE 0.37 ± 0.08
    HGZE 0.61 ± 0.08
    SZLGE 0.34 ± 0.08
    SZHGE 0.57 ± 0.08
    LZLGE 0.53 ± 0.08
    LZHGE 0.65 ± 0.10
    GLNUz 0.72 ± 0.08
    ZLNU 0.67 ± 0.08
    ZP 0.48 ± 0.09
    1. GTV CT-based gross tumor volume, SRE 5 short-run emphasis, LRE 5 long-run emphasis, LGRE low gray-level run emphasis, HGRE high gray-level run emphasis, SRLGE 5 short-run low gray-level emphasis, SRHGE 5 short-run high gray-level emphasis, LRLGE 5 long-run low gray-level emphasis, LRHGE long-run high gray-level emphasis, GLNUr gray-level nonuniformity for run, RLNU run-length nonuniformity, RP run percentage, SZE short-zone emphasis, LZE long-zone emphasis, LGZE low gray-level zone emphasis, HGZE high gray-level zone emphasis, SZLGE short-zone low gray-level emphasis, SZHGE 5 short-zone high gray-level emphasis, LZLGE long-zone low gray-level emphasis, LZHGE long-zone high gray-level emphasis, GLNUz gray-level nonuniformity for zone, ZLNU zone length nonuniformity, ZP zone percentage

Appendix 5 The GLNUz value exhibited superior discrimination of local recurrence in patients with T3–T4 disease (n = 32). The 2-year PRFS for those with a GLNUz value in the >50th and ≤50th percentile was 10% and 84%, respectively (p < 0.001)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Shen, W., Lin, Y. et al. Correlation of pretreatment 18F-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, 567–580 (2017).

Download citation


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