R01CA253923

Novel Integrative Approach for the Early Detection of Lung Cancer Using Repeated Measures - Project Summary

Early detection of lung cancer among asymptomatic individuals is a priority for reducing mortality of the number one cancer killer worldwide. Most lung cancers are first detected as indeterminate pulmonary nodules (IPNs). While the vast majority of IPNs are benign, those malignant ones present with specific features that should allow for the early discrimination and intervention.

We have recently completed a study demonstrating the value of structural imaging features analysis in providing improved accuracy in detection of cancers among IPNs with accuracy of over 90% trained in the NLST and validated in two independent cohorts. The AUC increased from baseline risk estimate of disease using clinical parameters (Mayo model) 0.78 to 0.84 and from 0.82 to 0.92 in two independent validation cohorts.

Similarly, we tested the added value of our high sensitivity HSCYFRA 21-1 assay in three populations of lung nodules and obtained similar added value to the Mayo model. Finally, we identified signatures predictive of lung cancer using large scale data mining in the electronic health record (EHR). The performance of the established imaging predictor, HSCYFRA concentrations, and EHR trajectories will be validated in a prospective cohort.

In an innovative partnership between pulmonary oncology, radiology, machine learning, and data science experts at Vanderbilt, we propose to integrate the layer of clinical information accessible in the EHR to improve the noninvasive diagnosis accuracy. In addition, we propose to take advantage of repeated measures to improve the accuracy of the prediction of cancer and to reduce the time to diagnosis. We therefore propose the following aims.

Aim 1: We will validate advanced quantitative imaging analyses to distinguish early benign from malignant IPNs based on repeated measures of 1000 individuals.

Aim 2: We will test in 150 individuals with lung nodules the added value of repeated measures of HSCYFRA 21-1 protein blood biomarker in diagnostic accuracy over the baseline concentrations of the biomarker.

Aim 3: We will test a deep learning strategy from the EHR of 20,000 patients from VUMC to identify patterns likely to improve the early detection of lung cancer.

Aim 4: We will test the added value of monitoring changes in levels of the markers for early detection using repeated pre-diagnosis chest CT studies, serum analysis of HSCYFRA 21-1, and EHR patterns from our lung cancer screening program.

Built upon strong preliminary data and unique resources from VUMC that include access to large imaging and EHR data sources, this novel integrative study has the potential to generate highly impactful and translational results to reduce false positive rates among IPNs, and morbidity and mortality from lung cancer.

This application responds to PAR 19-264 using low-dose lung screening computed tomography longitudinal analysis integrated with a lead serum biomarker and the power of artificial intelligence to mine the EHR for the discovery of a novel integrative strategy for the early detection of premetastatic lung cancer.

Vanderbilt University Medical Center

TO IMPROVE SCREENING AND EARLY DETECTION STRATEGIES AND TO DEVELOP ACCURATE DIAGNOSTIC TECHNIQUES AND METHODS FOR PREDICTING THE COURSE OF DISEASE IN CANCER PATIENTS. SCREENING AND EARLY DETECTION RESEARCH INCLUDES DEVELOPMENT OF STRATEGIES TO DECREASE CANCER MORTALITY BY FINDING TUMORS EARLY WHEN THEY ARE MORE AMENABLE TO TREATMENT. DIAGNOSIS RESEARCH FOCUSES ON METHODS TO DETERMINE THE PRESENCE OF A SPECIFIC TYPE OF CANCER, TO PREDICT ITS COURSE AND RESPONSE TO THERAPY, BOTH A PARTICULAR THERAPY OR A CLASS OF AGENTS, AND TO MONITOR THE EFFECT OF THE THERAPY AND THE APPEARANCE OF DISEASE RECURRENCE. THESE METHODS INCLUDE DIAGNOSTIC IMAGING AND DIRECT ANALYSES OF SPECIMENS FROM TUMOR OR OTHER TISSUES. SUPPORT IS ALSO PROVIDED FOR ESTABLISHING AND MAINTAINING RESOURCES OF HUMAN TISSUE TO FACILITATE RESEARCH. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.

93.394 - Cancer Detection and Diagnosis Research

National Cancer Institute (NCI) [HHS - NIH]

Nashville, Tennessee 37203 United States

Single Zip Code

Imaging, Biomarkers and Digital Pathomics for the Early Detection of Premetastatic Aggressive Cancer (R01 Clinical Trial Optional) (PAR-19-264)

Amendment Since initial award the total obligations have increased 405% from $682,056 to $3,442,093.