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Head and neck (HN) cancer is the sixth most common malignancy worldwide, with around 800,000 new cases and 320,000 deaths in 2015. These malignancies encompass cancers of the oral cavity, oropharynx, hypopharynx and larynx and concerned squamous cell carcinomas (SCC) 90% of the time.
Despite aggressive treatment strategies, the five-year survival rate has only marginally improved in the past decade especially because of a high rate (up to 40%) of loco-regional recurrence. Moreover, two-third of local relapse and lymph node metastases occur in the first 2 years after treatment. Hence, predicting tumor response to therapy remains a real challenge in head and neck cancers.
Usual prognostic factors include the tumor size, the lymph node involvement, the presence of distant metastasis, the anatomic subsite, and the human papilloma virus (HPV 16, 18) infection status. However, pre-treatment selection of patients with poor prognosis or who require intensified therapy remains difficult despite their careful evaluation.
HNSCC also present a high biologic heterogeneity with hypoxic area, necrotic regions, zones of high cellular proliferation and intra-tumoral angiogenic heterogeneity. A better characterization of tumor heterogeneity could help classify patients in different risk subgroups in order to improve their therapeutic management.
Pre-therapeutic 18F-fluorodeoxyglucose positron-emission tomography (FDG PET/CT) is recommended by guidelines to assess remote extension of locally advanced HNSCC and/or to look for synchronous cancer. Its prognostic significance has already been suggested and a selection of patients at risk of recurrence appears possible using different quantitative parameters. Indeed, static (SUV = Standardized Uptake Value), volumetric (MTV = Metabolic Tumor Volume, TLG = Total Lesion Glycolysis) and kinetic (RI = Retention Index) parameters were demonstrated to be independent prognostic factors in several studies.
Texture analysis is currently highly studied in order to characterize tumor heterogeneity, and consists in extracting texture indices from different imaging modalities such as FDG PET/CT. Calculating texture indices relies on choosing a contouring method to delineate a tumor volume of interest and selecting several parameters such as the resampling method, matrix definitions and indices formula. Several studies conducted on small series of patients with HNSCC have showed promising results to predict survival, analyzing various cancer anatomic subsites, tumor segmentation methods and texture indices.
The objective of this study is to assess the prognostic value of texture indices in a large cohort of patients with HSNCC, including any anatomic subtype and staging.
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