Predicting a high-grade pattern in IASLC grading system of lung adenocarcinoma using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)...

The new International Association for the Study of Lung Cancer (IASLC) grading system for invasive lung adenocarcinoma (IAC) has demonstrated significant prognostic efficacy, benefiting many patients with adjuvant chemotherapy. We aimed to develop a nomogram integrating ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and clinical features for preoperative prediction of IASLC grading.

This study included 1327 IAC patients who underwent preoperative ¹⁸F-FDG PET/CT, who were randomly divided into a training cohort (n=928) and a test cohort (n=399). Univariate and multivariate analyses identified independent clinical and imaging variables. Logistic regression analysis was performed to calculate the independent risk factors and construct models for predicting the high-grade pattern of IAC. Model performance was evaluated using receiver operating characteristic curves and accuracy assessments. Subsequently, a nomogram based on PET/CT imaging-clinical model was established. Decision curve analysis and calibration curves assessed clinical utility.

In the training cohort, PET/CT imaging-clinical combined model effectively predicted patients with a high-grade pattern (area under the curve [AUC] = 0.837), outperforming PET (AUC = 0.789, P<.001), CT (AUC = 0.780, P<.001), PET/CT (AUC = 0.817, P=.002), and clinical (AUC = 0.744, P<.001) models. In the test cohort, similar superiority of PET/CT imaging-clinical combined model (AUC = 0.834) over other models was also observed (all P<.05). The calibration curve suggested good coherence between model predictions and actual observations in both training and test cohorts (P>.05). Good decision curve analysis demonstrated the clinical usefulness of the nomogram.

The ¹⁸F-FDG PET/CT imaging-clinical combined model is effective in predicting a high-grade pattern in IAC patients. The derived nomogram aids in identifying patients who would benefit from adjuvant therapy.