Objective: Four commonly used clinical models for predicting the probability of malignancy in pulmonary nodules were compared. While three of the models (Mayo Clinic, Veterans Association [VA], and Brock University) are based on clinical and computed tomography (CT) characteristics, one model (Herder) additionally includes the 18F-fluorodeoxyglucose (FDG) uptake value among the positron emission tomography (PET) characteristics. This study aimed to compare the predictive power of these four models in the context of a population drawn from a single center in an endemic area for tuberculosis in Korea.
Methods: A retrospective analysis of 242 pathologically confirmed nodules (4-30 mm in diameter) in 242 patients from January 2015 to December 2015 was performed. The area under the receiver operating characteristic curve (AUC) was used to assess the predictive performance with respect to malignancy.
Results: Of 242 nodules, 187 (77.2%) were malignant and 55 (22.8%) were benign, with tuberculosis granuloma being the most common type of benign nodule (23/55). PET was performed for 227 nodules (93.8%). The Mayo, VA, and Brock models showed similar predictive performance for malignant nodules (AUC: 0.6145, 0.6042 and 0.6820, respectively). The performance of the Herder model (AUC: 0.5567) was not significantly different from that of the Mayo (vs. Herder, p = 0.576) or VA models (vs. Herder, p = 0.999), and there were no differences among the three models in determining the probability of malignancy of pulmonary nodules. However, compared with the Brock model, the Herder model showed a significantly lower ability to predict malignancy (adjusted p = 0.0132).
Conclusions: In our study, the Herder model including the 18FDG uptake value did not perform better than the other models in predicting malignant nodules, suggesting the limited utility of adding PET/CT data to models predicting malignancy in populations within endemic areas for benign inflammatory nodules, such as tuberculosis.