Imaging and Pathology Concordance in Head and Neck Cancer: Retrospective Analysis.

Abstract

Background: Lymph node metastasis critically impacts prognosis in head and neck malignancies. Preoperative imaging (CT/MRI) is vital for assessment but often yields false results. This study examines the concordance between preoperative imaging and postoperative pathology and identifies factors influencing imaging accuracy.

Methods: A retrospective cohort study from 2014 to 2023 included patients with head and neck malignancies. Clinical and radiological data were analyzed, and the random forest algorithm was utilized for indeterminate cases.

Results: Analyzing 1129 records, 26.1% had indeterminate imaging. Imaging accuracy for definitive findings was 72.8%, sensitivity 57.2%, and specificity 86.0%. Logistic regression highlighted alcohol, T, and clinical stage as accuracy influencers. The indeterminate group showed a link between multiple enlarged lymph nodes and positivity. A nomogram achieved 67.5% accuracy. The random forest model, focusing on lymph node diameter, stage, and T classification, improved accuracy to 75.5% over logistic regression.

Conclusion: Our study shows moderate imaging-pathology concordance. Key predictors like lymph node size suggest refining criteria with machine learning could enhance head and neck cancer diagnosis. These results could guide more accurate preoperative imaging assessments, leading to better surgical planning and patient outcomes. Subsequent exploration of adjusting lymph node size thresholds or integrating novel imaging technologies would be useful.