Abstract: Objective To investigate and analyze the clinical value of computed tomography texture analysis for the diagnosis of pulmonary nodules. Methods A total of 86 cases with pulmonary nodules who were underwent consultation at Jinhu County Hospital of Traditional Chinese Medicine from February 2019 to May 2022 were selected for the study, and 18 nodules were pathologically diagnosed as malignant nodules (malignant group), while 68 nodules as benign nodules (benign group). All patients were given conventional CT with computed tomography texture analysis to record imaging features and quantify texture parameters. Results All patients' images showed grade 4-6 branches, and there was no significant difference in the CT image quality score in the malignant group compared with the benign group (P>0.05). The percentages of lobar, vacuolar, pleural depression, and bronchial inflation signs in the malignant group were 88.89%, 77.78%, 72.22%, and 83.33%, which were significantly higher than those in the benign group, which were 32.35%, 33.82%, 27.94%, and 32.35% (P<0.05). The CT values of main pulmonary artery, lobar artery, segmental artery and subsegmental artery were significantly higher in the malignant group compared with the benign group (P<0.05). The parameters associated with computed tomographic texture analysis-entropy, skewness, and maximum value-were significantly higher in the malignant group compared with the benign group (P<0.05). Among 86 patients, computed tomography texture analysis diagnosed malignant nodules in 17 cases and benign nodules in 69 cases, for which the sensitivity and specificity of computed tomography texture analysis for the diagnosis of pulmonary nodules were 88.89% (16/18) and 98.52% (67/68). Conclusion The application of computed tomography texture in pulmonary nodules can provide texture features that cannot be observed by the naked eye, allowing quantitative assessment of pulmonary nodules, with good sensitivity and specificity in the diagnosis of benign and malignant pulmonary nodules.

Key words: computed tomography texture analysis, pulmonary nodules, bronchial inflation sign, CT values, entropy values, skewness