Recent data from the National Lung Screening Trial (NLST) suggest that annual low-dose chest CT scans in patients who smoke, leads to early detection of lung cancer and improves survival. CMS/Medicare has consequently approved CT scans for lung cancer screening, and the VA National Center for Health Promotion and Disease Prevention has adopted a similar approach. The Veteran (VA) population is at increased risk of developing lung cancer as compared to the general population because of higher smoking rates and increased likelihood of exposure to other carcinogens during their military service. The VA system cares for some 6.7 million mostly older male veterans each year, many of whom have long smoking histories. In a recent study, investigators from eight VA centers across the U.S. screened more than 2,000 Veterans over two years using criteria from the NLST. Among the 2,106 Veterans screened, a total of 1,257 (59.7%) had nodules, of which 1,184 (56.2%) required tracking. Nearly all of the positive results were negative for cancer, producing a false- positive rate of 97.5% for human-based interpretation. In the general population, many of the lung nodules identified by human readers as ?indeterminate? or ?suspicious? on chest CT trigger additional surgical interventions (~$5K-$25K/patient) and CT exams, but >30% of these nodules on subsequent biopsies or resection are identified as being benign. The current low false positive rate in diagnosis of nodules on screening CT exams results in patient anxiety, and one of the reasons for poor compliance in lung cancer screening. As a result, there is an urgent need for better image based decision support tools for improving lung cancer screening. PI Anant Madabhushi and his team have developed novel computerized image analysis and pattern recognition tools for improved discrimination of cancerous from non-cancerous nodules on routine screening chest CT scans. A significant breakthrough has been in developing a novel imaging marker called ?vessel tortuosity? for quantitatively characterizing the architectural complexity of the vasculature of a lung nodule on chest CT scans; measurements of vessel tortuosity being significantly different between benign and malignant lung nodules. Additionally our group has also identified other highly predictive image features that aim to capture (1) subtle textural patterns of the microarchitecture within and immediately outside the nodule, and (2) subtle 3D shape patterns of the nodule. Each of these imaging markers has been independently shown to have an area under the receiver operating characteristic curve (AUC) ranging from 77-87% in distinguishing malignant from benign nodules in a validation set of N=145 patients. By contrast, on this cohort an expert chest radiologist and pulmonologist had a maximum AUCs of 69-72%. More interestingly, on this cohort combining machine based interpretations with human readers resulted in an improvement of 30% in the AUC value for the human readers. Building on our current impressive results, in this study we propose to continue to optimize our computerized decision support technology (Lung Imaging based Risk Score (LunIRiS)) to assign a risk score of malignancy to a nodule on a chest CT scan.
In Aim 1 we will identify the best combination of intra- and peri- nodule texture, 3D shape, margin sharpness and vessel tortuosity measurements for constructing the LunIRiS software program by employing a cohort of over N=300 patients.
In Aim 2, LunIRiS will be independently validated on N=300 retrospective cases from the Cleveland VA. We will then deploy the LunIRiS program at the Cleveland VA in Aim 3 to quantitatively evaluate its role as a decision support tool. On an independent cohort of N=250 CT screening exams from Veteran patients, radiologists and pulmonologists at the Cleveland VA will first independently read the scans; following a wash out period they will perform a second interpretation with LunIRiS. Interpretation results with and without LunIRiS will then be compared to evaluate additional benefit of LunIRiS.
The Veteran (VA) population is at increased risk of developing lung cancer as compared to the general population because of higher smoking rates and increased likelihood of exposure to other carcinogens during their military service. The VA system cares for some 6.7 million mostly older male veterans each year, many of whom have long smoking histories. In a recent study, investigators from eight VA centers across the U.S. screened more than 2,000 Veterans over two years and found that only 2.5% of all suspicious nodules identified on chest CT scans were actually malignant. This high false positive rate results in a number of unnecessary repeat CT scans and surgical interventions (biopsy, bronchoscopy, wedge resections) for patients with benign nodules. In this project we seek to develop, optimize, validate and deploy an advanced Lung Image based Risk Score (LunIRiS) decision support tool for significantly improving the diagnostic evaluation of suspicious nodules on screening chest CT scans in the VA population. 1
