Stephen Deppen, PhD, associate professor Thoracic Surgery, spoke about research on biomarkers for the early detection of lung cancer at the World Conference on Lung Cancer, which was held Sept. 7-10 in San Diego.
Deppen is co-director of the National Cancer Institute Early Detection Research Network Lung Cancer Clinical Validation Center. A clinical epidemiologist, he is an expert on understanding how environmental factors impact lung nodules and utilizing that data to distinguish between benign and malignant nodules on lung screens. The Thoracic Biorepository, which is headquartered within the MASLAB at Vanderbilt-Ingram Cancer Center, focuses on research to better assess indeterminate pulmonary nodules so physicians can distinguish whether they are cancerous or benign, reduce the time to diagnosis for cancers, and minimize invasive procedures in patients without cancer.
Deppen spoke about biological, population and technology issues related to the discovery and implementation of biomarkers for early detection. The process is complex because of the many types of lung cancer, the biological heterogeneity of the disease, and benign nodules that can mimic malignant lesions.
“Unlike in the posttreatment setting, in early detection biomarkers we are looking for a very small signal in a very noisy and dynamic environment, especially in the setting of noninvasive disease and early diagnosis,” Deppen said.
Yency Forero, MD, a postdoctoral fellow working at MASLAB, also presented at the workshop about her research validating radiomics models using chest CT scans of patients with pulmonary nodules.
“We explained the challenges and potential solutions for addressing variability in image acquisition, image de-identification, image reconstructions, and tumor changes over time, as well as how to assess the performance of radiomics models in clinical practice, considering the characteristics of the populations where these models are applied,” Forero said. “The opportunity for MASLAB to represent Vanderbilt at this conference allowed us to showcase and explain the complexity of this process and underscore the need for collaborative work to implement these radiomics models in diverse population settings.”
