PT  - JOURNAL ARTICLE
AU  - Paul Kearney
AU  - Xiao-Jun Li
AU  - Alex Porter
AU  - Steve Springmeyer
AU  - Peter Mazzone
TI  - An Integrated Risk Predictor for Pulmonary Nodules
AID  - 10.1101/094920
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 094920
4099  - http://biorxiv.org/content/early/2016/12/17/094920.short
4100  - http://biorxiv.org/content/early/2016/12/17/094920.full
AB  - It is estimated that over 1.5 million lung nodules are detected annually in the United States. Most of these are benign but frequently undergo invasive and costly procedures to rule out malignancy. A risk predictor that can accurately differentiate benign and malignant lung nodules could be used to more efficiently route benign lung nodules to non-invasive observation by CT surveillance and route malignant lung nodules to invasive procedures. The majority of risk predictors developed to date are based exclusively on clinical risk factors, imaging technology or molecular markers. Assessed here are the relative performances of previously reported clinical risk factors and proteomic molecular markers for assessing cancer risk in lung nodules. From this analysis an integrated model incorporating clinical risk factors and proteomic molecular markers is developed and its performance assessed on a previously reported prospective collection of lung nodules that enrolled 475 patients from 12 sites with lung nodules between 8 and 30mm in diameter. In this analysis it is found that the molecular marker is most predictive. However, the integration of clinical and molecular markers is superior to both clinical and molecular markers separately.Clinical Trial Registration: Registered at ClinicalTrials.gov (NCT01752101).