Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules

Abstract

Background: Current management of lung nodules is complicated by nontherapeutic resections and missed chances for cure. We hypothesized that a serum proteomic signature may add diagnostic information beyond that provided by combined clinical and radiographic data.

Methods: Cohort A included 265 and cohort B 114 patients. Using multivariable logistic regression analysis we calculated the area under the receiver operating characteristic curve (AUC) and quantified the added value of a previously described serum proteomic signature beyond clinical and radiographic risk factors for predicting lung cancer using the integration discrimination improvement (IDI) index.

Results: The average computed tomography (CT) measured nodule size in cohorts A and B was 37.83 versus 23.15 mm among patients with lung cancer and 15.82 versus 17.18 mm among those without, respectively. In cohort A, the AUC increased from 0.68 to 0.86 after adding chest CT imaging variables to the clinical results, but the proteomic signature did not provide meaningful added value. In contrast, in cohort B, the AUC improved from 0.46 with clinical data alone to 0.61 when combined with chest CT imaging data and to 0.69 after adding the proteomic signature (IDI of 20% P = 0.0003). In addition, in a subgroup of 100 nodules between 5 and 20 mm in diameter, the proteomic signature added value with an IDI of 15% (P ≤ 0.0001).

Conclusions: The results show that this serum proteomic biomarker signature may add value to the clinical and chest CT evaluation of indeterminate lung nodules.

Impact: This study suggests a possible role of a blood biomarker in the evaluation of indeterminate lung nodules.

Figure 1

Prediction models performance in cohorts A and B. A, receiver operating characteristic (ROC) curves showing diagnostic accuracy of clinical data alone: age, pack-year smoking history (green), MALDI MS signature alone (dotted gold), clinical combined with chest CT data including size and shape (blue), clinical combined with MALDI data (dotted purple), CT data alone (dotted light blue), CT data and MALDI MS signature (dotted black), and finally the added value of the serum MALDI MS signature to clinical and chest CT (red) in cohort A (208 patients, 150 cases and 58 controls). B, ROC curves showing diagnostic accuracy of clinical data alone: age, pack-year smoking history (green), MALDI MS signature alone (dotted gold), clinical combined with chest CT data including size and shape (blue), clinical combined with MALDI data (dotted purple), CT data alone (dotted light blue), CT data and MALDI MS signature (dotted black), and finally the added value of the serum MALDI MS signature to clinical and chest CT (red) in cohort B (62 patients, 25 cases and 37 controls).

Figure 2

Prediction models performance in indeterminate pulmonary nodules. ROC curves showing diagnostic accuracy of clinical data alone: age, pack-year smoking history (green), MALDI MS signature alone (dotted gold), clinical combined with chest CT data including size and shape (blue), clinical combined with MALDI data (dotted purple), CT data alone (dotted light blue), CT data and MALDI MS signature (dotted black), and finally the added value of the serum MALDI MS signature to clinical and chest CT (red) in patients with lung nodules sized 5 to 20 mm (100 patients, 51 cases, 49 controls).