Feasibility of prognosis assessment for cancer of unknown primary origin using texture analysis of ¹⁸F-fluorodeoxyglucose PET/computed tomography images of largest metastatic lymph node

Objective Cancers of unknown primary origin cannot be staged using images, making the prognosis difficult. We attempted to predict prognosis of patients with unknown primary origin using tumour heterogeneity recently introduced in 18F-fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT). Methods Overall, 30 patients with unknown primary origin who underwent whole-body 18F-FDG PET/CT scans were retrospectively enrolled for texture analysis. The volume of interest was placed in the largest metastatic lymph nodes and conventional parameters and grey-level co-occurrence matrix (GLCM) were calculated. Statistical analysis of image-based variables was performed using Cox regression analyses. Patients were stratified into two groups based on cutoff values of GLCMentropy obtained using receiver operating characteristics (ROCs). Patients were analyzed, and overall survival (OS) was compared using Kaplan-Meier analysis. Results Univariate Cox regression analysis showed significant differences in prognosis for parenchymal organ metastasis (P < 0.01), GLCM homogeneity (P = 0.01), GLCMcontrast (P < 0.01), GLCMentropy (P < 0.01) and GLCMdissimilarity (P < 0.01). Multivariate Cox regression analysis showed a significant difference in reduced prognosis for GLCMentropy positive (P < 0.01). Stratification was performed based on the GLCMentropy cutoff value, determined using ROCs analysis, with smaller groups showing better OS. Conclusions Despite previous difficulties in predicting prognosis in patients with unknown primary origin, 18F-FDG PET/CT texture features may enable stratification of prognosis. This could be useful for appropriate patient selection and management and help identify a subset of patients with favourable outcomes. These novel findings may be helpful for prognostication and improving patient care.