Correlation analysis and prediction model construction of CT signs and infiltration degree of pure ground-glass nodule lung adenocarcinoma

Abstract:

Objective To explore the correlation between computed tomography（CT） signs and infiltration degree of pure ground-glass nodule（pGGN） lung adenocarcinoma，and establish a prediction model of CT signs and infiltration degree.Methods The clinical data and CT signs of 424 patients with lung adenocarcinoma confirmed by surgical resection，pathological biopsy and chest CT findings of pGGN were analyzed retrospectively，and according to the results of pathological biopsy，they were divided into four groups： atypical adenomatous hyperplasia，adenocarcinoma in situ，minimally invasive adenocarcinoma and invasive adenocarcinoma. The Chi-square test or Fisher’s exact probability test was used for statistical analysis of differences between groups. For the results with statistical significance，six learning algorithms in Waikato environment for knowledge analysis（WeKa） were used to build the prediction model，verify the accuracy，and select the prediction model most suitable for this study.Results The differences in nodule diameter and nodule density between the four groups were statistically significant（P<0.001），with the corresponding mean diameters of 6.90，8.65，10.71 and 14.56 mm，and the corresponding mean densities of -633.16，-543.04，-401.03 and -322.94 HU，respectively. The diameter and density of the nodules showed an obvious upward trend with the increase of the degree of invasion of the lesions. There were statistically significant differences among the four groups in nodule boundary，lobulation，burr，vascular perforation，pleural indentation，air bronchogram sign，and vacuole sign（P< 0.05），but there were no statistically significant differences in nodule growth position，age，smoking history，family history of lung cancer in immediate family（P>0.05）. The prediction accuracy of the model constructed by the random forest algorithm fluctuated between 76.42% and 79.72%，the Kappa coefficient fluctuated between 0.597 and 0.670， and the area under the receiver operating characteristic（ROC） curve was greater than 0.9，which was the best among the error indicators. Thus，it is the most suitable prediction model for this study.Conclusion The different CT signs of pGGN are closely related to the degree of infiltration， and can be used to establish a prediction model. Based on the model built by random forest algorithm，the average accuracy of quickly identifying the pGGN infiltration degree in the early stage before invasive intervention is 78.07%，which is the highest accuracy，and can potentially be used in the prediction of lung cancer in the future.