Abstract： Objective:To distinguish lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) using 18F-fluorodeoxyglucose (FDG) PET-based radiomic features. Methods:A retrospective analysis was performed in 182 patients (109 males, 73 females, age (59.0±8.3) years) with non-small cell lung cancer (NSCLC) who underwent 18F-FDG PET/CT scan between January 2018 and December 2019 in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. All patients had been diagnosed pathologically with lung ADC or SCC. The patients were divided into a training set ( n=91) and a validation set ( n=91) using simple random sampling method. Radiomic features were extracted from the PET images of segmented tumors using the Python package. The minimum redundancy maximum relevance feature selection algorithm and least absolute shrinkage and selection operator were employed to select informative and non-redundant features, and a radiomics signature score (rad-score) was developed. Differences of rad-score between groups were compared by Mann-Whitney U test. Multivariate logistic regression was applied to select the important factors. A combined model was constructed based on the clinical variable and radiomics signature. The predictive performance of models was analyzed and compared using receiver operating characteristic (ROC) curves and Delong test. Results:Four radiomic features, namely HHL_first order_maximum, LHL_first order_entropy, HHH_ gray level dependence matrix_large dependence high gray level emphasis (GLDM_LDHGLE), HHL_GLDM_LDHGLE (H/L represent the high/low pass filter) were selected to build the rad-score. The rad-score showed a significant ability to discriminate between different histological subtypes in the two sets(training set: -1.30(-1.70, -1.04) vs -0.60 (-1.11, 0.20), z=-4.61, P<0.001); validation set: -1.31(-1.66, -0.96) vs -0.73(-1.02, -0.24), z=-4.76, P<0.001). The area under the curve (AUC) of the rad-score were equal to 0.815 (95% CI: 0.723-0.906) in the training set, and 0.813 (95% CI: 0.726-0.901) in the validation set, respectively, which were larger than those of the clinical variables (smoking had the best prediction performance, training set: 0.721 (95% CI: 0.617-0.810), validation set: 0.726 (95% CI: 0.623-0.814)), however, the difference was not significant ( z values: 1.319, 1.324, both P>0.05). When the clinical variable (smoking) and radiomics signature were combined, the complex model showed a better performance in the classification of histological subtypes, with the AUC increased to 0.862 (95% CI: 0.785-0.940; sensitivity: 88.00%(22/25), specificity: 72.73%(48/66)) in the training set and 0.854 (95% CI: 0.776-0.933; sensitivity: 75.00%(21/28), specificity: 84.13%(53/63)) in the validation set. The AUC values were significantly higher than those of the clinical variable (smoking; training set: z=3.257, P<0.001; validation set: z=3.872, P<0.001). Conclusion:Individualized diagnosis model incorporating with smoking and radiomics signature can help differentiate lung cancer subtypes in a non-invasive, repeatable modality.