Dataset Information

Relationship between Circulating Inflammation Proteins and Lung Cancer Diagnosis in the National Lung Screening Trial.

ABSTRACT: BACKGROUND:Previously, we observed a strong relationship between circulating serum inflammation proteins in relation to lung cancer diagnosis and risk, both in case-control and prospective cohorts. Low-dose computed tomography (LDCT) screening has a high prevalence of false-positive nodules, thus companion noninvasive biomarkers that can distinguish between benign and malignant nodules could have clinical utility and positive impact on patient outcomes. METHODS:We conducted a nested case-control study within the National Lung Screening Trial. Concentrations of 30 inflammation proteins were measured on plasma samples of 262 cases and 528 controls using a highly sensitive and analytically validated electrochemiluminescence V-PLEX immunoassay. RESULTS:Comparing the fourth quartile with the first quartile, we found increased IFN? and IL12/IL23p40 associated with increased odds of a lung cancer diagnosis [OR 1.89, 95% confidence intervals (CI), 1.16-3.09; OR 2.49, 95% CI, 1.46-4.23, respectively]. Confirming our previous observations, we also detected a relationship between increased IL6, IL8, and C-reactive protein (CRP) with lung cancer diagnosis. These relationships were significant after adjustment for age, gender, race, smoking, body mass index (BMI), family history of lung cancer, and previous diagnoses of inflammatory conditions. However, none of these proteins could distinguish between a benign and malignant lung nodule (IL6: OR 1.25, 95% CI, 0.59-2.64; IL8: OR 1.40, 95% CI, 0.70-2.81; CRP: OR 0.98, 95% CI, 0.45-2.12). CONCLUSIONS:We have discovered new associations for IFN? and IL12/IL23p40 with lung cancer but have no evidence that these proteins can distinguish between benign and malignant lung nodules. IMPACT:Circulating inflammation proteins are unlikely to have utility as companion LDCT biomarkers.

SUBMITTER: Brown D

PROVIDER: S-EPMC6325020 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Relationship between Circulating Inflammation Proteins and Lung Cancer Diagnosis in the National Lung Screening Trial.

Brown Derek D Zingone Adriana A Yu Yunkai Y Zhu Bin B Candia Julián J Cao Liang L Ryan Bríd M BM

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20181008 1

<h4>Background</h4>Previously, we observed a strong relationship between circulating serum inflammation proteins in relation to lung cancer diagnosis and risk, both in case-control and prospective cohorts. Low-dose computed tomography (LDCT) screening has a high prevalence of false-positive nodules, thus companion noninvasive biomarkers that can distinguish between benign and malignant nodules could have clinical utility and positive impact on patient outcomes.<h4>Methods</h4>We conducted a nest ...[more]

PMID: 30297515

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Project description:RATIONALE:Lung computed tomography (CT) Screening Reporting and Data System (lung-RADS) has standardized follow-up and management decisions in lung cancer screening. To date, little is known how lung-RADS classification compares with radiological semantic features in risk prediction and diagnostic discrimination. OBJECTIVES:To compare the performance of radiological semantic features and lung-RADS in predicting nodule malignancy in lung cancer screening. METHODS:We used data and low-dose CT (LDCT) images from the National Lung Screening Trial (NLST). The training cohort contained 60 patients with screen-detected incident lung cancers who had a positive baseline screen (T0) that was not diagnosed and then was diagnosed at second follow-up (T2), and 139 nodule-positive controls who had 3 consecutive positive screens (T0 to T2) that were not diagnosed as lung cancer. The testing cohort included 40 patients with incident lung cancers that were diagnosed at first follow-up (T1) and 40 nodule-positive controls. Twenty-four semantic features were scored on a point scale from the LDCT images. Multivariable linear predictor model was built on the semantic features and the performances were compared with lung-RADS in 3 screening rounds. We also combined non-size-based semantic features with lung-RADS to improve malignancy detection. RESULTS:At T0, the average area under the receiver operating characteristic curve (AUROC) for border definition in risk prediction was 0.72. The average AUROC for contour at T1 in risk prediction and T2 in diagnostic discrimination was 0.82 and 0.88, respectively. By comparison, the average AUROC of lung-RADS at T0, T1 and T2 were 0.60, 0.76 and 0.87, respectively. The combined model of the semantic features and lung-RADS shows improvement with AUROCs of 0.74, 0.88 and 0.96 at T0, T1, and T2, respectively, achieved by adding border definition (at T0) or contour (at T1 and T2). CONCLUSION:We find semantic features defined by border definition and contour performed similar to lung-RADS at follow-up time point and outperformed lung-RADS at baseline. These semantics alongside of lung-RADS shows improved performance to detect malignancy.

Dataset Information

Relationship between Circulating Inflammation Proteins and Lung Cancer Diagnosis in the National Lung Screening Trial.

Publications

Relationship between Circulating Inflammation Proteins and Lung Cancer Diagnosis in the National Lung Screening Trial.

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