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.
KeywordsHead 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
Standardized uptake value
Total lesion glycolysis
- 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
- 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
- 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
- 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
- 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
- 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