Radiology · 2010
Transient part-solid nodules detected at screening thin-section CT for lung cancer: comparison with persistent part-solid nodules.
Сравнительное исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Lee Sang Min, Park Chang Min, Goo Jin Mo, Lee Chang Hyun, Lee Hyun Ju, Kim Kwang Gi, Kang Mi-Jin, Lee In Sun
Аннотация
PURPOSE: To retrospectively investigate clinical and computed tomographic (CT) features of transient part-solid nodules (PSNs) initially detected at screening thin-section CT for lung cancer and to determine predictive factors that may differentiate transient PSNs from persistent PSNs.
MATERIALS AND METHODS: This study was approved by the institutional review board. From January 2006 to August 2008, 93 individuals with 126 PSNs were identified from among 16777 individuals who underwent chest CT. Clinical features and CT characteristics of PSNs were reviewed, and clinical and thin-section CT features were compared between transient and persistent PSNs. To identify predictive factors of transient PSNs and evaluate predictive performance, logistic regression analysis and C statistic analysis were performed.
RESULTS: Eighty-eight (69.8%) of 126 PSNs were transient. Between transient and persistent PSNs, there were significant differences (P < .05) in patient age, patient sex, risk of lung cancer, presence of eosinophilia, mode of detection, lesion size, lesion multiplicity, size of solid portion, and lesion border. Multivariate analysis revealed that young patient age, detection of the lesion at follow-up, blood eosinophilia, lesion multiplicity, large solid portion, and ill-defined border were significant (P < .05) independent predictors of transient PSNs. The performance in the discrimination of transient PSNs from persistent PSNs of the logistic regression model that incorporated both clinical and thin-section CT features was significantly higher than the performance of the models that incorporated clinical features or thin-section CT features alone.
CONCLUSION: A substantial proportion of PSNs detected at screening CT were transient. Transient PSNs could be predicted with high accuracy by using the features of young patient age, detection of the PSN at follow-up, blood eosinophilia, lesion multiplicity, large solid portion, and ill-defined lesion border.
MATERIALS AND METHODS: This study was approved by the institutional review board. From January 2006 to August 2008, 93 individuals with 126 PSNs were identified from among 16777 individuals who underwent chest CT. Clinical features and CT characteristics of PSNs were reviewed, and clinical and thin-section CT features were compared between transient and persistent PSNs. To identify predictive factors of transient PSNs and evaluate predictive performance, logistic regression analysis and C statistic analysis were performed.
RESULTS: Eighty-eight (69.8%) of 126 PSNs were transient. Between transient and persistent PSNs, there were significant differences (P < .05) in patient age, patient sex, risk of lung cancer, presence of eosinophilia, mode of detection, lesion size, lesion multiplicity, size of solid portion, and lesion border. Multivariate analysis revealed that young patient age, detection of the lesion at follow-up, blood eosinophilia, lesion multiplicity, large solid portion, and ill-defined border were significant (P < .05) independent predictors of transient PSNs. The performance in the discrimination of transient PSNs from persistent PSNs of the logistic regression model that incorporated both clinical and thin-section CT features was significantly higher than the performance of the models that incorporated clinical features or thin-section CT features alone.
CONCLUSION: A substantial proportion of PSNs detected at screening CT were transient. Transient PSNs could be predicted with high accuracy by using the features of young patient age, detection of the PSN at follow-up, blood eosinophilia, lesion multiplicity, large solid portion, and ill-defined lesion border.