Probability of cancer in pulmonary nodules detected on first screening CT

Annette McWilliams, Martin C Tammemagi, John R Mayo, Heidi Roberts, Geoffrey Liu, Kam Soghrati, Kazuhiro Yasufuku, Simon Martel, Francis Laberge, Michel Gingras, Sukhinder Atkar-Khattra, Christine D Berg, Ken Evans, Richard Finley, John Yee, John English, Paola Nasute, John Goffin, Serge Puksa, Lori Stewart, Scott Tsai, Michael R Johnston, Daria Manos, Garth Nicholas, Glenwood D Goss, Jean M Seely, Kayvan Amjadi, Alain Tremblay, Paul Burrowes, Paul MacEachern, Rick Bhatia, Ming-Sound Tsao, Stephen Lam, Annette McWilliams, Martin C Tammemagi, John R Mayo, Heidi Roberts, Geoffrey Liu, Kam Soghrati, Kazuhiro Yasufuku, Simon Martel, Francis Laberge, Michel Gingras, Sukhinder Atkar-Khattra, Christine D Berg, Ken Evans, Richard Finley, John Yee, John English, Paola Nasute, John Goffin, Serge Puksa, Lori Stewart, Scott Tsai, Michael R Johnston, Daria Manos, Garth Nicholas, Glenwood D Goss, Jean M Seely, Kayvan Amjadi, Alain Tremblay, Paul Burrowes, Paul MacEachern, Rick Bhatia, Ming-Sound Tsao, Stephen Lam

Abstract

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.).

Figures

Figure 1. Relationship between Nodule Size and…
Figure 1. Relationship between Nodule Size and Probability That a Nodule Is Lung Cancer
The reference variables for this model were male sex, lower or middle lobe location, and no spiculation. Estimates of variables are taken from the parsimonious model with spiculation.
Figure 2. Distributions of Lung-Cancer Probabilities
Figure 2. Distributions of Lung-Cancer Probabilities
The distribution of lung-cancer probabilities, based on the parsimonious model without spiculation, is shown among persons with lung cancer and those without lung cancer in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan) and British Columbia Cancer Agency (BCCA) data sets. Perifissural nodules and nodules with any missing value in any predictor were not included in the calculation.

Source: PubMed

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