Systemic Metabolic and Volumetric Assessment via Whole-Body [18F]FDG-PET/CT: Pancreas Size Predicts Cachexia in Head and Neck Squamous Cell Carcinoma

<b>Background/Objectives:</b> Cancer-associated cachexia in head and neck squamous cell carcinoma (HNSCC) is challenging to diagnose due to its complex pathophysiology. This study aimed to identify metabolic biomarkers linked to cachexia and survival in HNSCC patients using [<sup>18</sup>F]FDG-PET/CT imaging and machine learning (ML) techniques. <b>Methods:</b> We retrospectively analyzed 253 HNSCC patients from Vienna General Hospital and the MD Anderson Cancer Center. Automated organ segmentation was employed to quantify metabolic and volumetric data from [<sup>18</sup>F]FDG-PET/CT scans across 29 tissues and organs. Patients were categorized into low weight loss (LoWL; grades 0-2) and high weight loss (HiWL; grades 3-4) groups, according to the weight loss grading system (WLGS). Machine learning models, combined with Cox regression, were used to identify survival predictors. Shapley additive explanation (SHAP) analysis was conducted to determine the significance of individual features. <b>Results:</b> The HiWL group exhibited increased glucose metabolism in skeletal muscle and adipose tissue (<i>p</i> = 0.01), while the LoWL group showed higher lung metabolism. The one-year survival rate was 84.1% in the LoWL group compared to 69.2% in the HiWL group (<i>p</i> < 0.01). Pancreatic volume emerged as a key biomarker associated with cachexia, with the ML model achieving an AUC of 0.79 (95% CI: 0.77-0.80) and an accuracy of 0.82 (95% CI: 0.81-0.83). Multivariate Cox regression confirmed pancreatic volume as an independent prognostic factor (HR: 0.66, 95% CI: 0.46-0.95; <i>p</i> < 0.05). <b>Conclusions:</b> The integration of metabolic and volumetric data provided a strong predictive model, highlighting pancreatic volume as a key imaging biomarker in the metabolic assessment of cachexia in HNSCC. This finding enhances our understanding and may improve prognostic evaluations and therapeutic strategies.

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Publication Details

Journal
Cancers
Published
2024-09-30
DOI
https://doi.org/10.3390/cancers16193352
Citations
8
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
2.61

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article

Systemic Metabolic and Volumetric Assessment via Whole-Body [18F]FDG-PET/CT: Pancreas Size Predicts Cachexia in Head and Neck Squamous Cell Carcinoma

Michael Beyerlein, Clemens P. Spielvogel, Josef Yu, Barbara Katharina Geist et al.
8 citations
Cancers
Nutrition and Health in Aging
2.61
article

Systemic Metabolic and Volumetric Assessment via Whole-Body [18F]FDG-PET/CT: Pancreas Size Predicts Cachexia in Head and Neck Squamous Cell Carcinoma

Michael Beyerlein, Clemens P. Spielvogel, Josef Yu, Barbara Katharina Geist, Stefan Grünert, Ying‐Ting Chen, Sam Augustine Kandathil, Marcus Hacker, Erdem Yıldız, Chrysoula Vraka, Öykü Özer, Tingting Wu, Zhaoqi Zhang, Zewen Jiang, Katarina Kumpf, David Haberl, Smilla Pusitz, Till Buschhorn, Iustin Tibu, Julia Schnöll
article en
8 citations

Abstract

<b>Background/Objectives:</b> Cancer-associated cachexia in head and neck squamous cell carcinoma (HNSCC) is challenging to diagnose due to its complex pathophysiology. This study aimed to identify metabolic biomarkers linked to cachexia and survival in HNSCC patients using [<sup>18</sup>F]FDG-PET/CT imaging and machine learning (ML) techniques. <b>Methods:</b> We retrospectively analyzed 253 HNSCC patients from Vienna General Hospital and the MD Anderson Cancer Center. Automated organ segmentation was employed to quantify metabolic and volumetric data from [<sup>18</sup>F]FDG-PET/CT scans across 29 tissues and organs. Patients were categorized into low weight loss (LoWL; grades 0-2) and high weight loss (HiWL; grades 3-4) groups, according to the weight loss grading system (WLGS). Machine learning models, combined with Cox regression, were used to identify survival predictors. Shapley additive explanation (SHAP) analysis was conducted to determine the significance of individual features. <b>Results:</b> The HiWL group exhibited increased glucose metabolism in skeletal muscle and adipose tissue (<i>p</i> = 0.01), while the LoWL group showed higher lung metabolism. The one-year survival rate was 84.1% in the LoWL group compared to 69.2% in the HiWL group (<i>p</i> < 0.01). Pancreatic volume emerged as a key biomarker associated with cachexia, with the ML model achieving an AUC of 0.79 (95% CI: 0.77-0.80) and an accuracy of 0.82 (95% CI: 0.81-0.83). Multivariate Cox regression confirmed pancreatic volume as an independent prognostic factor (HR: 0.66, 95% CI: 0.46-0.95; <i>p</i> < 0.05). <b>Conclusions:</b> The integration of metabolic and volumetric data provided a strong predictive model, highlighting pancreatic volume as a key imaging biomarker in the metabolic assessment of cachexia in HNSCC. This finding enhances our understanding and may improve prognostic evaluations and therapeutic strategies.

CancersVol. 16(19)
Hebei Medical University (CN), Central South University (CN), Christian Doppler Laboratory for Thermoelectricity (AT), Fourth Hospital of Hebei Medical University (CN), Xiangya Hospital Central South University (CN), Medical University of Vienna (AT)
Austrian Science Fund
Good health and well-being
Openalex Percentile: Top 10%
Nutrition and Health in Aging
2.61
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