Probability of Cancer in Pulmonary Nodules Detected on First Screening CT
By A. Mcwilliams, M. Tammemagi, J. Mayo, H. Roberts, Geoffrey Liu, Kam Soghrati, K. Yasufuku, S. Martel, F. Laberge, Michel Gingras and 23 more. New England Journal of Medicine, 2013
1,241 citations recorded by the source corpus.
BACKGROUND Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up. METHODS We analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer. RESULTS In the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set. CONCLUSIONS Predictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.)
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/481b5fd153f8d3872afc540778051f1cf5c14fd8
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In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. (2017, methodology): “The recommendation for 2-year follow-up was based on earlier studies with thick CT sections or chest radiographs and was made before the important differences between solid and subsolid nodules were recognized (6,7).”
- Developing prediction models for clinical use using logistic regression: an overview. (2019, methodology): “Tammemagi model (9) Lung cancer screening-”
- Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning (2017, methodology): “The best known model is the PanCan model by McWilliams et al. [54], published in the New England Journal of Medicine, which was derived from a Canadian screening program for which 102 cancerous and 6906 benign nodules were available.”