Correlation analysis between metabolic tumor burden measured by positron emission tomography/computed tomography and the 2015 World Health Organization classification of lung adenocarcinoma, with a risk prediction model of tumor spread through air spaces

Background: Tumor spread through air spaces (STAS) is an important pattern of invasion and impacts the frequency and location of recurrence. The objective was to assess the correlation between metabolic tumor burden of positron emission tomography/computed tomography (PET/CT) and 2015 World Health Organization (WHO) classification of lung adenocarcinoma, and to establish a risk prediction model of STAS. Methods: We reviewed 127 consecutive patients. The SUVmax, SUVmean, MTV, TLG, diameter, and CTV were measured. All risk factors were analyzed by multivariate logistic regression analysis; regression coefficients and odds ratios were calculated for independent risk factors. A STAS risk prediction model was created using the regression coefficients to determine the predictive probability (PP). Results: The nodule types and SUV were significantly correlated with 2015 WHO pathological categories (P<0.001). Most of (83.3%) the lepidic predominant adenocarcinoma (LPA) appeared as non-solid or part-solid nodules with the lowest SUV (P<0.05). There was a significant difference in STAS distribution among different nodule types (P=0.000). STAS was significantly correlated with SUVmax (P=0.000), SUVmean (P=0.000), SUVpeak (P=0.000), TLG (P=0.001), and diameter (P=0.044). The risk prediction model of STAS was established. The PP of STAS and the incidence of STAS were analyzed using the ROC curve (AUC =0.759, P=0.000). The sensitivity, specificity, and accuracy of the predictive model for STAS were 47.1%, 88.6%, and 71.1%, respectively. Conclusions: The LPA appeared as non-solid nodule with low SUV without STAS has a good prognosis. SUV and TLG are valuable predictive indices in the prediction of STAS. The predictive model developed in predicting the incidence of STAS has good specificity and accuracy.