Diagnostic value of AI quantitative parameters combined with CT imaging features in pulmonary space-occupying lesions
Objective To explore the diagnostic value of AI quantitative parameters and CT im-aging features in differentiating benign and malignant pulmonary space-occupying lesions and assess-ing the invasiveness of lung adenocarcinoma,providing data support for clinical diagnosis and treat-ment.Methods A total of 175 patients with pulmonary space-occupying lesions who underwent tho-racoscopic surgery at the Affiliated Hospital of North China University of Science and Technology be-tween January 2023 and April 2024 were enrolled.Based on postoperative pathological results,the patients were divided into benign lesion group and malignant lesion group.AI quantitative parameters and CT imaging features were analyzed in both groups.Further,the malignant lesion group was sub-divided into invasive lesion group(＞0.5 cm)and non-invasive lesion group(＜0.5 cm)based on in-vasion status.Differences in parameters between the groups were compared,and ROC curves were used to analyze the diagnostic value of each parameter in differentiating benign and malignant pulmo-nary space-occupying lesions and assessing the invasiveness of lung adenocarcinoma.Results 3D max-imum diameter,lobulated margin,spiculated margin,solid component ratio,and mean CT value were independent predictors of benign and malignant pulmonary space-occupying lesions(P＜0.05).The AUC for combined parameters in differentiating benign and malignant lesions was 0.884.3D maximum diameter,spiculated margin,solid component ratio,and maximum CT value were inde-pendent predictors of the invasiveness of lung adenocarcinoma(P＜0.05).The AUC for combined parameters in assessing invasiveness was 0.899.Conclusion AI quantitative parameters combined with CT imaging features can effectively evaluate the benignity and malignancy of pulmonary space-occupying lesions and the invasiveness of lung adenocarcinoma.Moreover,AI quantitative parameters provide multiple data support for patients.
Pulmonary space-occupying lesionsArtificial intelligenceCT imaging featuresLung adenocarcinomaDegree of invasionDifferential diagnostic value
万方数据
维普
