Differentiation of benign and malignant neck pathologies: preliminary experience using spectral computed tomography

Area of Science:

• Radiology
• Medical Imaging
• Oncology

Background:

• Distinguishing benign from malignant neck masses is crucial for appropriate patient management.
• Dual-energy CT offers advanced material decomposition capabilities beyond conventional CT.
• Gemstone spectral imaging (GSI) provides material-specific data, including spectral Hounsfield unit (HU) curves and effective atomic number (Zeff).

Purpose of the Study:

• To evaluate the utility of spectral HU curves and effective Z derived from dual-energy GSI CT in differentiating benign and malignant neck lesions.
• To assess the diagnostic performance of these parameters in a cohort of patients with neck masses.

Main Methods:

• Retrospective review of 38 patients with neck masses who underwent dual-energy GSI CT.
• Analysis of spectral HU curves and effective Z values from lesions and paraspinal muscles (PSMs).
• Comparison of curve parameters (range, asymptote, decay) and effective Z between benign and malignant groups using logistic regression.

Main Results:

• The difference in spectral HU curve range between the lesion and PSM was the strongest predictor of malignancy.
• A threshold difference in range of 75 or greater achieved 95% sensitivity, 89% specificity, and 91.8% area under the curve (AUC).
• Effective Z alone had a lower AUC (79.9%), and adding other parameters did not significantly improve the model's performance.

Conclusions:

• Spectral HU curve analysis, particularly the difference in range between lesion and PSM, is a valuable tool for differentiating benign and malignant neck pathologies.
• Dual-energy GSI CT provides quantitative data that can aid in lesion characterization.
• Further prospective studies may validate these findings for clinical application.