Prediction of true positive lung cancers in individuals with abnormal suspicious chest radiographs: a prostate, lung, colorectal, and ovarian cancer screening trial study.

Department of Community Health Sciences, Brock University, St Catharines, Ontario, Canada.
Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer (Impact Factor: 5.8). 04/2009; 4(6):710-21. DOI: 10.1097/JTO.0b013e31819e77ce
Source: PubMed

ABSTRACT Chest radiographs are routinely employed in clinical practice. Radiographic findings that are abnormal suspicious (AS) for lung cancer occur commonly. The majority of AS radiographic abnormalities are not cancer. This study identifies predictors of true positive (TP) AS and presents models for estimating the probability of lung cancer.
This is a prospective cohort study nested in the randomized National Cancer Institute's Prostate Lung Colorectal Ovarian Cancer Screening Trial (PLCO). First-time AS screens in the screening arm of the PLCO were studied. Associations between nonradiographic and radiographic factors, and TP AS were evaluated by multiple logistic regression.
The PLCO intervention arm had 77,465 individuals, of whom 12,314 were AS and of these 232 (1.9%) had lung cancer (were TP). Important independent predictors of TP were older age, lower education, greater pack years and duration smoking history, body mass index <30, family history of lung cancer, lung nodule, lung mass, unilateral mediastinal or hilar lymphadenopathy, lung infiltrate, and upper/middle chest AS location. The model including these variables had a receiver operator characteristic area under the curve (ROC AUC) of 86.4%. This model excluding the smoking variables had an ROC AUC of 77.1% and excluding all nonradiographic variables had an ROC AUC of 73.3% (p < 0.0001 for all these model differences). Smoking and nonsmoking nonradiographic variables significantly added to prediction.
This study identifies important nonradiographic and radiographic predictors of lung cancer, and presents an accurate model for estimating the probability of lung cancer in individuals with suspicious radiographs. These findings may be of value for screening, research, and patient and clinician decision-making.

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