Clinicopathologic and metabolic variables from 18F-FDG PET/CT in the prediction of recurrence pattern in stage I-III non-small-cell lung cancer after curative surgery.

Aim. This study aimed to analyze the clinicopathologic and metabolic parameters derived from staging ¹⁸ F-FDG PET/CT that can predict recurrence patterns in non-small-cell lung cancer (NSCLC) after curative surgery. Material and methods. Retrospective study including stage I-III NSCLC patients with a baseline ¹⁸ F-FDG PET/CT scan. Relapse patterns were analyzed based on location, lesion and organ-specific recurrence. Clinicopathologic variables were recorded. Three distinct categories of variables were obtained: standardized uptake value (SUV)-based metrics, heterogeneity parameters, and morphological features. The relation of relapse patterns with clinicopathologic and metabolic parameters was analyzed using the uni-multivariate logistic regression. Results Out of 173 patients, 104 experienced recurrences, with 66% presenting distant involvement and 56.7% exhibiting polymetastatic disease at initial recurrence. Patient age, pathologic lymphovascular invasion and normalized SUVmax perimeter distance (nSPD) were considered as risk factors for early recurrence. Adenocarcinoma histology was identified as an independent variable for distant recurrence. Patient age, number of metastatic mediastinal lymph nodes at staging (nN), sphericity, normalized SUVpeak to centroid distance (nSCD), entropy, low gray-level run emphasis, and high gray-level run emphasis were independent variables for polymetastatic disease. Certain variables were correlated with organ-specific recurrence. Bone recurrence was related to nN and SUVmean. Brain recurrence was related to adenocarcinoma histology. Lung recurrence was associated with coefficient of variation and nSPD. Conclusion: The metabolic profile of lung primary tumors obtained from ¹⁸ F-FDG PET/CT seems to be predictive of recurrence patterns that are closely linked to the overall survival of NSCLC patients. These findings could help in the development of personalized follow-up strategies based on an individual's recurrence pattern.