Assessment of Plasma Proteomics Biomarker's Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial

Abstract

Background: Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%.

Methods: A prospective, multicenter observational trial of 685 patients with 8- to 30-mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made.

Results: A subgroup of 178 patients with a clinician-assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82-100), a specificity of 44% (CI, 36-52), and a negative predictive value of 98% (CI, 92-100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified.

Conclusions: When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance.

Trial registry: ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).

Keywords: biomarker; diagnosis; lung cancer; proteomics; pulmonary nodules; risk models.

Published by Elsevier Inc.

Figures

Figure 1

Eligibility of the Pulmonary Nodule Plasma Proteomic Classifier study patients for integrated classifier performance analysis in lung nodules according to the probability of malignancy. ∗Incomplete clinical data are broken down as follows: n = 9, no pretest probability provided; n = 48, no follow-up procedure documented; n = 88, no 1-year follow-up CT scan; n = 39, no follow-up after PET scan; n = 5, time between interval scans did not reach 1 year; and n = 3, biopsy performed without documentation of results.

Figure 2

Distribution of physician-assigned pretest pCA for eligible Pulmonary Nodule Plasma Proteomic Classifier study patients (n = 392) by deciles. Shown are the physician-assigned pCA percentages for nodules with either a malignant (n = 197) or benign (n = 195) diagnosis. Note: the first two columns represent 5% pCA increments. pCA = probability of cancer.

Figure 3

Comparison of the area under the receiver-operating characteristic curves of lung nodule malignancy risk assessment tools relative to the 95% NPV zone. Shown are the receiver-operating characteristic curves for subjects with lung nodules assigned a pCA ≤ 50% (n = 178) comparing the integrated classifier vs the physician-assigned pCA, PET, and the VA and Mayo cancer risk models. The shaded area indicates the ≥ 95% NPV diagnostic performance zone, which corresponds to the 5% cancer risk threshold specified in the CHEST guidelines for lung management. NPV = negative predictive value; VA = Veterans Affairs. See Figure 2 legend for expansion of other abbreviation.