Differential diagnosis of lung cancer and tuberculosis based on 18 F-fluorodeoxyglucose PET/CT multi-time points imaging

Objective To investigate the value of 18 F-fluorodeoxyglucose(FDG) PET/CT multi-time points imaging (MTPI) on the differential diagnosis between lung cancer (LC) and tuberculosis (TB). Methods Sixty-four patients underwent 18 F-FDG PET/CT MTPI. The stdSUVmax, stdSUVavg, retention index, metabolic tumor volume, total lesion glycolysis at four-time points and slope of metabolic curve were measured and calculated, and the sex, age, and uniformity of FDG uptake were recorded. The difference in each index between LC and TB was analyzed, and dynamic metabolic curves (DMCs) of LC and TB were fitted by significance indexes. Artificial neural network (ANN) prediction models were established between squamous cell carcinoma (SCC) and TB, as well as between adenocarcinomas and TB. Results Differences between SCC and TB, stdSUVmax/avg at four-time points, total lesion glycolysis, stdSUVmax/avg slope (1-2 h,1-3 h and 1-4 h), uniformity of FDG uptake and age were significant. stdSUVavg has the largest area under the 4 h curve; age was only significant between adenocarcinomas and TB. DMCs at 1-4 h fitted by stdSUVavg were more helpful in differentiating LC and TB than stdSUVmax. stdSUVavg(1 h and 4 h), stdSUVavg slope 1-4 h, age, and uniformity of FDG uptake were selected to establish an ANN prediction model between SCC and TB; the area under the curve (AUC) was 100.0%. The same indices were used to establish the prediction model between adenocarcinomas and TB; the AUC was up to 83.5, and after adding stdSUVavg (2 and 4 h) to adenocarcinomas and TB models, the AUC was 87.7%. Conclusion 18 F-FDG PET/CT MTPI fitting DMCs and establishing an ANN prediction model would distinguish SCC from TB relatively accurately and provide certain help in the differentiation between adenocarcinomas and TB.