Abstract

Objective

We aimed to evaluate whether the degree of F-18 fuorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).

Methods

We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health checkup. Individuals who developed lung cancer within 5 years of the PET/CT scan were classifed into the lung cancer group (n=136); those who did not were classifed into the control group (n=659). The healthy individuals were then randomly assigned to either the training (n=585) or validation sets (n=210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.

Results

The lung SUVR and lung MH index in the lung cancer group were signifcantly higher than in the control group (p<0.001 and p<0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were signifcantly associated with lung cancer development (age: OR 1.07, p<0.001; male: OR 2.08, p=0.015; BMI: OR 0.93, p=0.057; current or past smoker: OR 5.60, p<0.001; lung SUVR: OR 1.13, p<0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a signifcant association with lung cancer development (age: OR 1.06, p<0.001; male: OR 1.87, p=0.045; BMI: OR 0.93, p=0.010; current or past smoker: OR 4.78, p<0.001; lung MH index: OR 1.33, p<0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively].

Conclusions

The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk