Abstract
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 subset of 222 lung nodules, between 8mm and 20mm in diameter, collected in a previously reported prospective study. 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).
Highlights
Over 1.5 million lung nodules are identified annually in the U.S presenting a difficult clinical challenge as the majority prove to be of benign origin [1, 2]
For the purposes of this analysis we focus on the performance of the clinical risk factors and molecular markers for identification of NSCLC as the number of other lung cancer subtypes were too few to reasonably estimate performance on
These areas under the curve (AUC) values demonstrate an improved performance for the integrated model over the proteomic ratio and Mayo alone, we focus on the clinically relevant point on the IntMod ROC curve to statistically compare performance at the same sensitivity or the same specificity
Summary
Over 1.5 million lung nodules are identified annually in the U.S presenting a difficult clinical challenge as the majority prove to be of benign origin [1, 2]. The diagnostic dilemma faced by physicians is to identify nodules that are malignant and yet minimize the risks of invasive procedures on benign nodules. Evidence suggests that the currently available tools such as clinical risk predictors [3,4,5] and imaging have limitations in clinical practice [6, 7]. This has resulted in a growing interest in utilizing molecular tests as diagnostic adjuncts [8, 9]. We have previously validated a blood-based risk predictor that used 11 molecular factors [8, 10] and established its potential clinical utility on a prospectively collected biobank [11]
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