Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules

Abstract

Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.

Keywords: biomarkers; early detection; indeterminate pulmonary nodules; lung cancer.

Figure 1

New stratification algorithm based on validated and clinically useful biomarkers. US = United States.

Figure 2

A, B, Schematic representation of feature extraction and selection with conventional radiomics and deep learning. A, With conventional radiomics, nodules are segmented, and expert-defined features are extracted and analyzed by using traditional statistical regression methods. B, With artificial neural networks, automated learning of relevant features through multilayered networks mitigates expert bias, but extraction and analysis are hidden, and only the output (benign or cancer) is known (black box).