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

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

18F-FDG:

18F-fluorodeoxyglucose

CRT:

Chemoradiotherapy

CTVs:

Clinical target volumes

GLNUz:

Gray-level nonuniformity for zone

GTV:

Gross tumor volume

HNSCC:

Head and neck squamous cell carcinoma

HPV:

Human papilloma virus

MTV:

Metastatic tumor volume

PET/CT:

Positron emission tomography-computed tomography

RLNU:

Run-length nonuniformity

RT:

Radiotherapy

SUV:

Standardized uptake value

TLG:

Total lesion glycolysis

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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

Author notes

    Authors and Affiliations

    1. Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan

      Shang-Wen Chen & Ying-Chun Lin

    2. School of Medicine, China Medical University, Taichung, Taiwan

      Shang-Wen Chen

    3. School of Medicine, Taipei Medical University, Taipei, Taiwan

      Shang-Wen Chen

    4. Graduate Institute of Clinical Medical Science, School of Medicine, College of Medicine, China Medical University, No. 2, Yuh-Der Road, Taichung, 40447, Taiwan

      Shang-Wen Chen & Chia-Hung Kao

    5. Cancer Center and Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

      Wei-Chih Shen

    6. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

      Wei-Chih Shen

    7. The Ph.D. Program for Cancer Biology and Drug Discovery, China Medical University and Academia Sinica, Taichung, Taiwan

      Ying-Chun Lin

    8. Department of Pathology, China Medical University Hospital, Taichung, Taiwan

      Rui-Yun Chen

    9. Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan

      Te-Chun Hsieh, Kuo-Yang Yen & Chia-Hung Kao

    10. Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan

      Te-Chun Hsieh & Kuo-Yang Yen

    11. Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan

      Chia-Hung Kao

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    Corresponding author

    Correspondence to Chia-Hung Kao.

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    Funding

    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.

    Appendices

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

    figure 4
    Fig. 5
    figure 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

    Full size image
    Fig. 6
    figure 6

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

    Full size image

    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
    Full size table

    Appendix 3

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

    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)

      Table

      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)

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    Chen, SW., Shen, WC., Lin, YC. 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). https://doi.org/10.1007/s00259-016-3580-5

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