| Objective To investigate the clinical value of quantitative artificial intelligence(AI) parameters to assess the degree of pulmonary nodal infiltration. Methods The clinical data of 114 patients with lung adenocarcinoma confirmed by surgical pathology who were hospitalised in Bozhou People's Hospital from November 2021 to September 2023 were retrospectively analysed and divided into the noninfiltrating group(n=72)and infiltrating group(n=42)according to the pathology results. Differences in general information and quantitative parameters of AI were compared between the two groups, and logistic regression was used to analyse the factors affecting the degree of lung adenocarcinoma infiltration, and the predictive value of quantitative parameters on the degree of lung adenocarcinoma infiltration was assessed by the receiver operator characteristic (ROC) curve. Results The differences in general information (age, gender) and 12 quantitative parameters (entropy, mean CT value, minimum CT value, 3D longitudinal diameter, volume, mass, mean long and short diameters, maximum surface area, skewness, maximum CT value, variance of CT value and surface area)between the two groups were statistically significant (P<0.05).The logistic regression results showed that the mean CT value and entropy were independent risk factorsfor the development of infiltrative lung adenocarcinoma，and the minimum CT value was an independent protective factor for the development of infiltrative lung adenocarcinoma (P<0.05).The ROC results showed that the area under the curve (AUC) of the three predicted the occurrence of infiltrative lung adenocarcinoma was 0.630, 0.888, and 0.890, respectively, and the AUC predicted by the combination of the three was 0.955, which was superior to that of their individual detection (P<0.05). Conclusions The quantitative CT parameters meanCT value, entropy and minimum CT levels can provide clinical diagnosis of lung adenocarcinoma infiltration, and the combined detection of the three indexes can further improve the diagnostic value. |
