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. 2017 May 17;12(5):e0177635.
doi: 10.1371/journal.pone.0177635. eCollection 2017.

An integrated risk predictor for pulmonary nodules

Paul Kearney  1 , Stephen W Hunsucker  1 , Xiao-Jun Li  1 , Alex Porter  1 , Steven Springmeyer  1 , Peter Mazzone  2
Affiliations

An integrated risk predictor for pulmonary nodules

Paul Kearney et al. PLoS One. .

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).

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Conflict of interest statement

Competing Interests: The authors have read the journal's policy and the authors of this manuscript and have the following competing interests: PK, SH, XL, AP and SS were employees of Integrated Diagnostics at the time this work was performed. PM's institution received funding support from Integrated Diagnostics. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Comparison of five clinical risk factors and the proteomic ratio LG3BP/C163A.
Fig 2
Fig 2. Performance of the integrated model.
Performance of the Integrated Model (IntMod) for different values of parameter t and T = 0.5. Optimal sustained performance occurs for values of t between .14 and .39 where AUC values are all at least 62% and with p-values all below 0.008 (Mann-Whitney).
Fig 3
Fig 3. Performance comparisons.
Comparison of proteomic ratio, the simplified Mayo algorithm and the Integrated Model (Ratio + Mayo. At sensitivity 90% and specificity 33% the integrated model has statistically significant better performance than both the simplified Mayo model and the proteomic ratio (see text for details).
See this image and copyright information in PMC

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