Automated Pretreatment Thoracic CT-Based Body Composition Analysis Predicts Progression-Free Survival in Head and...

Area of Science:

• Oncology
• Radiology
• Medical Imaging

Background:

• Head and neck cancer (HNC) prognosis can be influenced by patient-specific factors.
• Body composition analysis (BCA) using computed tomography (CT) scans offers a quantitative method to assess tissue volumes.
• Automated BCA from pretreatment CT scans may provide valuable prognostic information.

Purpose of the Study:

• To evaluate the prognostic significance of automated, volumetric body composition analysis (BCA) in patients with head and neck cancer (HNC).
• To determine if skeletal muscle (SM) to bone (B) and subcutaneous adipose tissue (SAT) to bone (B) ratios predict progression-free survival (PFS).

Main Methods:

• Retrospective analysis of 160 HNC patients undergoing primary treatment.
• Automated BCA quantified bone (B), skeletal muscle (SM), and subcutaneous adipose tissue (SAT) volumes from pretreatment thoracic CT scans.
• Kaplan-Meier survival analysis and Cox proportional hazards modeling were used to assess PFS.

Main Results:

• The skeletal muscle to bone (SM/B) ratio was a significant predictor of PFS across the entire cohort and in both males and females.
• Multivariable analysis identified high SM/B and high subcutaneous adipose tissue to bone (SAT/B) ratios as independent prognostic factors for improved PFS.
• Patients with high SM/B and SAT/B ratios demonstrated significantly longer median PFS compared to those with low ratios.

Conclusions:

• Automated body composition analysis (BCA) metrics, particularly SM/B and SAT/B ratios, show potential as prognostic biomarkers in head and neck cancer (HNC).
• Further external validation and subgroup analyses are necessary to establish the generalizability of these findings in clinical practice.