Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers

被引：14

作者：

Marmor, Hannah N. ^{[1
]}

Jackson, Laurel ^{[2
]}

Gawel, Susan ^{[2
]}

Kammer, Michael ^{[3
]}

Massion, Pierre P. ^{[3
]}

Grogan, Eric L. ^{[1
,4
]}

Davis, Gerard J. ^{[2
,6
]}

Deppen, Stephen A. ^{[1
,4
,5
]}

机构：

[1] Vanderbilt Univ, Med Ctr, Dept Thorac Surg, 1211 Med Ctr Dr, Nashville, TN 37232 USA

[2] Abbott Diagnost Div, 100 Abbott Pk Rd, Abbott Pk, IL 60064 USA

[3] Vanderbilt Univ, Med Ctr, Dept Pulm & Crit Care Med, 1211 Med Ctr Dr, Nashville, TN 37232 USA

[4] Tennessee Valley Healthcare Syst, 1310 24th Ave South, Nashville, TN 37212 USA

[5] Vanderbilt Univ, Med Ctr, Dept Thorac Surg, 609 Oxford House, 1313 21st Ave South, Nashville, TN 37232 USA

[6] Abbott Labs, AP20-1, D09GP, 100 Abbott Pk Rd, Abbott Pk, IL 37232 USA

来源：

CLINICA CHIMICA ACTA | 2022年 / 534卷

基金：

美国国家卫生研究院;

关键词：

Pulmonary nodule; Lung cancer; Biomarker; Diagnosis; Prediction modeling; THORACIC SOCIETY GUIDELINES; LUNG-CANCER; TUMOR-MARKER; PROBABILITY; MANAGEMENT; VALIDATION; CYFRA-21-1; PROGNOSIS; DIAGNOSIS; MODELS;

D O I：

10.1016/j.cca.2022.07.010

中图分类号：

R446 [实验室诊断]; R-33 [实验医学、医学实验];

学科分类号：

1001 ;

摘要：

Background: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. Methods: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index. Results: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy. Conclusions: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

引用

页码：106 / 114

页数：9

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