[¹⁸F]FDG PET/CT Imaging and Hematological Parameters Can Help Predict HPV Status in Head and Neck Cancer

Abstract

Objective

To determine whether [¹⁸F]FDG PET/CT and hematological parameters provide supportive data to determine HPV status in HNSCC patients.

Methods

Retrospective analysis of clinical and diagnostic data from 106 patients with HNSCC: 26.4% HPV-positive and 73.6% HPV-negative was performed. The following semiquantitative PET/CT parameters for the primary tumor and hottest lymph node and liver were evaluated: SUV_max, SUV_mean, TotalSUV, MTV, TLG, maximum, mean and TLG tumor-to-liver ratio (TLR_max, TLR_mean,TLR_TLG) and heterogeneity index (HI). Following hematological variables were assessed: white blood cell (WBC); lymphocyte (LYMPH); neutrophil (NEU),monocyte (MON); platelet (PLT); neutrophil-to-lymphocyte ratio (NRL); lymphocyte-to-monocyte ratio (LMR); platelet-to lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Conventional statistical analyses were performed in parallel with an artificial neural network analysis (Neural Analyzer, v. 2.9.5).

Results

Significant between-group differences were observed for two of the semiquantitative PET/CT parameters, with higher values in the HPV-negative group: primary tumor MTV (22.2 vs 9.65; p=0.023), and TLR_max (3.50 vs 2.46; p=0.05). The HPV-negative group also had a significantly higher NEU count (4.84 vs. 6.04; p=0.04), NEU% (58.2 vs. 66.2; p=0.007), and NRL% (2.69 vs. 3.94; p=0.038). Based on ROC analysis (sensitivity 50%, specificity 80%, AUC 0.5), the following variables were independent predictors of HPV-negativity: primary tumor with SUV_max >10; TotalSUV >2800; MTV >23.5; TLG >180; TLR_max >3.7; TLR_TLG >5.7; and oropharyngeal localization.

Conclusion

Several semiquantitative parameters derived from [¹⁸F]FDG PET/CT imaging of the primary tumor (SUV_max, TotalSUV, MTV, TLG, TLR_max and TLR_TLG) were independent predictors of HPV-negativity.

Keywords

positron emission tomography/computed tomography - head and neck cancer - HPV - artificial intelligence - machine learning

Publication History

Received: 01 November 2023

Accepted after revision: 12 July 2024

Article published online:
04 December 2024

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