Objective To explore the complementary value of ¹⁸F-fluorodeoxyglucose (FDG) PET/CT texture analysis in the differential diagnosis of solitary lung cancer and tuberculosis nodules with hypermetabolic solitary pulmonary nodules (SPN).

Methods A total of 108 patients with hypermetabolic SPN (maximum standard uptake value (SUV_max)≥2.5) who were examined by ¹⁸F-FDG PET/CT were recruited retrospectively in Chifeng Municipal Hospital of Inner Mongolia and Beijing Cancer Hospital, Beijing Institute for Cancer Research from January 2017 to June 2020. The patients consisted of 68 males and 40 females aged 35 to 72 years, with a median age of 50 years. Forty-five patients had tuberculosis (tuberculosis group), and 63 patients had lung cancer (lung cancer group). All the nodules were confirmed by pathological examination. The benign and malignant SPN of the ¹⁸F-FDG PET/CT images of all patients were analyzed (subjective qualitative diagnosis), and misjudgment rate, sensitivity, and specificity were calculated. MaZda texture analysis software was used to delineate the region of interest manually on the maximum cross section and the adjacent upper and lower layers of the lung nodule on the CT and PET images to extract texture feature parameters. Fisher coefficient, classification error probability + average correlation coefficients, mutual information, and their combination (FPM) were used to filter texture features with differential diagnostic significance. The software program was also used for raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis to discriminate benign or malignant nodules. The discriminating efficacy was evaluated by misclassified rate. Independent sample t test was used to compare the measurement data between groups. Chi-square test was used to compare the count data between groups. Receiver operating characteristic (ROC) curve analysis was carried out for each texture feature parameter of the lowest misclassified rate to screen the top three most discriminative texture features.

Results The differences in age (54(42–72) years old vs. 47(35–64) years old) and SUV_max ((9.51±4.65) vs. (5.35±2.89)) were statistically significant between the tuberculosis group and lung cancer group (t=2.180, 2.520; both P<0.05). No statistically significant differences in gender and long diameter (χ²=0.070, t=0.675; both P>0.05) were found. The misclassified rate of the subjective qualitative diagnosis of high metabolic SPN was 26.9% (29/108). The sensitivity was 93.7% (59/63), and the specificity was 35.9% (14/39). ROC curve analysis was conducted based on SUV_max. When SUV_max had the best cut-off value of 5.3, the misclassified rate was 25.0% (27/108). No significant difference was found when compared with subjective qualitative diagnosis (χ²=0.096, P>0.05). The CT and PET images based on the combination of FPM feature selection and nonlinear discriminant analysis had lower misclassification rates of 8.33% and 1.85%, respectively, and the difference between them was statistically significant (χ²=4.694, P<0.05). Compared with subjective qualitative diagnosis and ROC curve analysis based on SUV_max, the differences were statistically significant (χ²=10.800, 27.457; both P<0.05). The most discriminative texture features were vertical long-run emphasis and 135° long-run emphasis in the gray-scale run length matrix and inverse difference moment in the gray-level co-occurrence matrix.

Conclusion MaZda texture analysis has higher diagnostic efficiency than subjective judgment in identifying benign and malignant hypermetabolic SPN nodules and thus complementary value in the differential diagnosis of hypermetabolic lung cancer and tuberculosis nodules.