Biomarkers in Pulmonary Nodule Diagnosis: Is It Time to Put Away the Biopsy Needle?

Factors associated with overdiagnosis of benign pulmonary nodules as malignancy: a retrospective cohort study

OBJECTIVE

To establish a preoperative model for the differential diagnosis of benign and malignant pulmonary nodules (PNs), and to evaluate the related factors of overdiagnosis of benign PNs at the time of imaging assessments.

MATERIALS AND METHODS

In this retrospective study, 357 patients (median age, 52 years; interquartile range, 46-59 years) with 407 PNs were included, who underwent surgical histopathologic evaluation between January 2020 and December 2020. Patients were divided into a training set (n = 285) and a validation set (n = 122) to develop a preoperative model to identify benign PNs. CT scan features were reviewed by two chest radiologists, and imaging findings were categorized. The overdiagnosis rate of benign PNs was calculated, and bivariate and multivariable logistic regression analyses were used to evaluate factors associated with benign PNs that were over-diagnosed as malignant PNs.

RESULTS

The preoperative model identified features such as the absence of part-solid and non-solid nodules, absence of spiculation, absence of vascular convergence, larger lesion size, and CYFRA21-1 positivity as features for identifying benign PNs on imaging, with a high area under the receiver operating characteristic curve of 0.88 in the validation set. The overdiagnosis rate of benign PNs was found to be 50%. Independent risk factors for overdiagnosis included diagnosis as non-solid nodules, pleural retraction, vascular convergence, and larger lesion size at imaging.

CONCLUSION

We developed a preoperative model for identifying benign and malignant PNs and evaluating factors that led to the overdiagnosis of benign PNs. This preoperative model and result may help clinicians and imaging physicians reduce unnecessary surgery.

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

Noninvasive biomarkers for lung cancer diagnosis, where do we stand?

The 2010's saw demonstration of the power of lung cancer screening to reduce mortality. However, with implementation of lung cancer screening comes the challenge of diagnosing millions of lung nodules every year. When compared to other cancers with widespread screening strategies (breast, colorectal, cervical, prostate, and skin), obtaining a lung nodule tissue biopsy to confirm a positive screening test remains associated with higher morbidity and cost. Therefore, non-invasive diagnostic biomarkers may have a unique opportunity in lung cancer to greatly improve the management of patients at risk. This review covers recent advances in the field of liquid biomarkers and computed tomographic imaging features, with special attention to new methods for combination of biomarkers as well as the use of artificial intelligence for the discrimination of benign from malignant nodules.