Macro-vasculature: A Novel Image Biomarker of Lung Cancer

University Of Pittsburgh At Pittsburgh, Pittsburgh PA

Investigators

Linked publications, trials & patents

Abstract

ABSTRACT Lung cancer remains the leading cause of cancer related deaths in the United States and worldwide despite advances in early detection, treatment, and smoking cessation programs. It was reported by the National Lung Screening Trial (NLST) that screening with low dose computed tomography (LDCT) scans may reduce lung cancer mortality by 20% compared to chest x-ray. This conclusion ultimately led to the approval and reimbursement for lung cancer screening using LDCT among asymptomatic adults with a history of tobacco smoking. However, LDCT-based screening often results in a large number of indeterminate nodules that later turn out to be non-cancerous. With increasing implementation of LDCT-based lung cancer screening in the U.S., the detection of indeterminate lung nodules during lung cancer screening is likely to increase. To reduce unnecessary diagnostic procedures, such as follow-up CT scan, positron emission tomography (PET)/CT exam, and invasive biopsies, a tool that can easily and accurately assess the malignancy and invasiveness of the indeterminate findings will be a welcomed addition to clinical practice. Different levels of invasiveness typically indicate different treatment plans and can often predict the treatment outcome. Compared to the investigative efforts dedicated to discriminating benign from malignant nodules, very limited effort has been focused on assessing the invasiveness of the suspicious nodules and explore the underlying factors associated with invasiveness. We proposed to develop and validate a novel computer tool to non-invasively assess the invasiveness of adenocarcinomas using LDCT scans from a large and diverse lung cancer database with pathology outcome. We will investigate and identify how image-based features contribute to invasiveness. Our exciting preliminary results demonstrate the feasibility of developing and implementing such a tool and its highly translational potential. We believe that our computer tool will be a tremendously useful addition to the clinical practice of lung cancer diagnosis and treatment. Its availability will: (1) enable a timely and accurate diagnosis of lung cancer, (2) limit the need for further imaging, biopsies, and possible surgery, and (3) facilitate an optimal selection of the treatment approach (e.g., surgical resection or radiotherapy). Ultimately, we want to improve survival and the quality of life of lung cancer patients.