Multimodal ultrasonographic algorithm in the differentiation of submandibular masses

Area of Science:

• Otolaryngology
• Head and Neck Surgery
• Diagnostic Imaging

Background:

• Pre-operative assessment of submandibular masses is crucial for clinical management.
• Accurate differentiation of these masses impacts treatment strategies.
• Existing diagnostic methods may have limitations in specificity.

Purpose of the Study:

• To evaluate the diagnostic utility of a novel multimodal algorithm.
• To differentiate submandibular masses using epidemiological and B-mode ultrasonographic data.
• To assess the algorithm's effectiveness in pre-operative evaluation.

Main Methods:

• A multimodal diagnostic algorithm was developed using epidemiological and B-mode ultrasonographic data.
• The algorithm was structured from data of 128 patients with submandibular triangle masses.
• Prospective validation was performed on an additional 60 patients.

Main Results:

• The multimodal algorithm demonstrated high diagnostic accuracy for sialolithiasis (100%/100% sensitivity/specificity).
• It achieved 94%/96% sensitivity/specificity for nodal lymphoma/unspecific lymphadenitis and 91%/84% for carcinomas.
• Sensitivity for benign submandibular gland diseases (BSD) was 44% and specificity for carcinomas was 84%, improved with intra-operative frozen sections.

Conclusions:

• The multimodal diagnostic algorithm is a simple and effective tool for pre-operative assessment of submandibular masses.
• It facilitates the differentiation of various submandibular lesions.
• The algorithm supports one-stage surgical concepts by improving pre-operative diagnostic certainty.