Differentiation of cancer of unknown primary and lymphoma in head and neck metastatic poorly differentiated cancer using 18F-FDG PET/CT tumor metabolic heterogeneity index

Abstract

Objective To explore the value of 18F-FDG PET/CT tumor metabolic heterogeneity index (HI) and establish and validate a nomogram model for distinguishing head and neck cancer of unknown primary (HNCUP) from lymphoma with head and neck metastatic poorly differentiated cancer. Methods This retrospective analysis was conducted on 1242 patients with cervical metastatic poorly differentiated cancer. 108 patients, who were clinically and pathologically confirmed as HNCUP or lymphoma, were finally enrolled. Two independent sample t-tests and χ2 test were used to compare the clinical and imaging features. Binary logistic regression was used to screen for independent predictive factors. Results Among the 108 patients), 65 patients were diagnosed with HNCUP and 43 were lymphoma. Gender (P = 0.001), SUVmax (P < 0.001), SUVmean (P < 0.001), TLG (P = 0.012), and HI (P < 0.001) had statistical significance in distinguishing HNCUP and lymphoma. Female (OR = 4.546, P = 0.003) and patients with HI ≥ 2.37 (OR = 3.461, P = 0.047) were more likely to be diagnosed as lymphoma. Conclusion For patients with cervical metastatic poorly differentiated cancer, gender and HI were independent predictors of pathological type. For such patients, clinical attention should be paid to avoid misdiagnosing lymphoma as HNCUP, which may delay treatment.