Risk stratification of indeterminate pulmonary nodules

Abstract

Purpose of review Lung cancer remains the leading cause of cancer-related death in the United States, with poor overall 5-year survival. Early detection and diagnosis are key to survival as demonstrated in lung cancer screening trials. However, with increasing implementation of screening guidelines and use of computed tomography, there has been a sharp rise in the incidence of indeterminate pulmonary nodules (IPNs). Risk stratification of IPNs, particularly those in the intermediate-risk category, remains challenging in clinical practice. Individual risk factors, imaging characteristics, biomarkers, and prediction models are currently used to assist in risk stratifying patients, but such strategies remain suboptimal. This review focuses on established risk stratification methods, current areas of research, and future directions. Recent findings The multitude of yearly incidental and screening-detected IPNs, its management-related healthcare costs, and risk of invasive procedures provides a strong rationale for risk stratification efforts. The development of new molecular and imaging biomarkers to discriminate benign from malignant lung nodules shows great promise. Yet, risk stratification methods need integration into the diagnostic workflow and await validation in prospective, biomarker-driven clinical trials. Summary Novel biomarkers and new imaging analysis, including radiomics and deep-learning methods, have been developed to optimize the risk stratification of IPNs. While promising, additional validation and clinical studies are needed before they can be part of routine clinical practice.

Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera

“…Accordingly, the minimum size threshold for the need of routine CT follow-up is 5 mm recommended by the British Thoracic Society (BTS) and 6 mm recommended by the Fleischner Society 29 . However, nodules greater than 6 mm without clear benign features, termed indeterminate pulmonary nodules (IPN), remain a major challenge for evaluation and management in clinical practice 30 , 31 . With the combined samples from both discovery and internal validation cohort, we further investigated whether there is impact of nodule size on the metabolomic profile.…”

“…It has been shown that a large number of indeterminate pulmonary nodules detected by LDCT may cause additional evaluation such as PET CT, lung biopsies and overtreatment due to false positive diagnosis of malignancy 31 . As illustrated in Fig.…”

Comparing Modalities for Risk Assessment in Patients with Pulmonary Lesions and Nondiagnostic Bronchoscopy for Suspected Lung Cancer

“…What is needed is an objective, less error-prone means for risk assessment that can be used across the heterogeneous spectrum of patients with indeterminant lung lesions. Optimally, this would be an accurate, noninvasive biomarker that could further enhance the utility of clinical and radiographic factors in differentiating early-stage lung cancers from benign disease (7,21). Various novel methods for risk assessment have been developed that utilize plasma biomarkers, autoantibodies to tumor-associated antigens, exhaled breath compounds, and bronchial and nasopharyngeal genomic classi ers, to augment the accuracy of existing risk prediction models in identifying malignant lesions from benign disease (22)(23)(24)(25)(26).…”

Comparing modalities for risk assessment in patients with pulmonary lesions and nondiagnostic bronchoscopy for suspected lung cancer

“…What is needed is an objective, less error-prone means for risk assessment that can be used across the heterogeneous spectrum of patients with indeterminant lung lesions. Optimally, this would be an accurate, noninvasive biomarker that could further enhance the utility of clinical and radiographic factors in differentiating early-stage lung cancers from benign disease[ 7 , 21 ]. There have been remarkable advances in lung cancer biomarkers that variably employ blood-based testing, genomic testing, or artificial intelligence to improve on the performance of the clinical-only risk models.…”