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. 2022 Sep 1:534:106-114.
doi: 10.1016/j.cca.2022.07.010. Epub 2022 Jul 20.

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

Hannah N Marmor  1 , Laurel Jackson  2 , Susan Gawel  3 , Michael Kammer  4 , Pierre P Massion  5 , Eric L Grogan  6 , Gerard J Davis  7 , Stephen A Deppen  8
Affiliations

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

Hannah N Marmor et al. Clin Chim Acta. .

Abstract

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.

Keywords: Biomarker; Diagnosis; Lung cancer; Prediction modeling; Pulmonary nodule.

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Conflict of interest statement

Declarations of interest: LJ, SG, and GD are fulltime employees and shareholders of Abbott Laboratories. The remaining authors have no declarations of interest.

Figures

Figure 1.
Figure 1.
Receiver Operating Characteristics Curve Comparing Mayo Model, Random Forest Combined Biomarker Model, and Logistic Regression Combined Biomarker Model
Figure 2.
Figure 2.
Risk Score Distribution for Mayo, Random Forest Combined Biomarker Model Prediction (RF), and Logistic Regression Combined Biomarker Model Prediction (LR)
Figure 3.
Figure 3.
Risk Reclassification Results for Benign and Malignant Nodules Comparing Mayo with Logistic Regression Combined Biomarker Model Prediction (LR) and Random Forest Combined Biomarker Model Prediction (RF)
See this image and copyright information in PMC

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