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

Chest: The Journal of Circulation, Respiration and Related Systems

Anil Vachani^4¹  Nichole T. Tanner^1,2²  Paul Kearney^3³  Alexander Porter^3⁴  Steven C. Springmeyer^3⁵  Gerard A. Silvestri^1⁶  Pierre P. Massion^5⁷ 

[1] Division of Pulmonary, Allergy, and Critical Care Medicine, Penn Lung Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA^4;Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Veterans Affairs Hospital, Charleston, SC^2;Integrated Diagnostics, Seattle, WA^3;Mayo Clinic, Rochester, MN^6;Respiratory Institute, Cleveland Clinic, Cleveland, OH^7;Thoracic Oncology Research Group Division of Pulmonary and Critical Care Medicine, Medical University of South Carolina, Charleston, SC^1;Thoracic Program, Vanderbilt-Ingram Comprehensive Cancer Center, Nashville, and Veterans Affairs, Tennessee Valley Healthcare System, Nashville Campus, Nashville, TN^5

关键词: biomarker;    diagnosis;    lung cancer;    proteomics;    pulmonary nodules;    risk models;    AUC;    area under the receiver-operating characteristic curve;    NPV;    negative predictive value;    pCA;    probability of cancer;    TTNA;    transthoracic needle biopsy;    VA;    Veterans Affairs;   

DOI  :  10.1016/j.chest.2018.02.012

学科分类：呼吸医学

来源: American College of Chest Physicians

【 摘 要 】

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 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).

【 授权许可】

CC BY