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

PANOPTIC Trial Team

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17 Citations (Scopus)

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).

Original languageEnglish (US)
JournalChest
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Proteomics
Biomarkers
Lung
Neoplasms
Multicenter Studies
Registries
Blood Proteins
Lung Neoplasms
Mass Spectrometry
Physicians
Sensitivity and Specificity
Incidence

Keywords

  • biomarker
  • diagnosis
  • lung cancer
  • proteomics
  • pulmonary nodules
  • risk models

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine
  • Cardiology and Cardiovascular Medicine

Cite this

@article{b4ab73ed593a4c109bda2c9f3174c089,
title = "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",
author = "{PANOPTIC Trial Team} and Silvestri, {Gerard A.} and Tanner, {Nichole T.} and Paul Kearney and Anil Vachani and Massion, {Pierre P.} and Alexander Porter and Springmeyer, {Steven C.} and Fang, {Kenneth C.} and Midthun, {David Eric} and Mazzone, {Peter J.}",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.chest.2018.02.012",
language = "English (US)",
journal = "Chest",
issn = "0012-3692",
publisher = "American College of Chest Physicians",

}

TY - JOUR

T1 - Assessment of Plasma Proteomics Biomarker's Ability to Distinguish Benign From Malignant Lung Nodules

T2 - Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial

AU - PANOPTIC Trial Team

AU - Silvestri, Gerard A.

AU - Tanner, Nichole T.

AU - Kearney, Paul

AU - Vachani, Anil

AU - Massion, Pierre P.

AU - Porter, Alexander

AU - Springmeyer, Steven C.

AU - Fang, Kenneth C.

AU - Midthun, David Eric

AU - Mazzone, Peter J.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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).

AB - 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).

KW - biomarker

KW - diagnosis

KW - lung cancer

KW - proteomics

KW - pulmonary nodules

KW - risk models

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U2 - 10.1016/j.chest.2018.02.012

DO - 10.1016/j.chest.2018.02.012

M3 - Article

C2 - 29496499

AN - SCOPUS:85048343187

JO - Chest

JF - Chest

SN - 0012-3692

ER -