Clinical trial NCT03622528: Quantitative Lung Cancer Screening
Official title: Precision, Pulmonary Disease Evaluation and Lung Cancer Detection Using Quantitative Low-dose CT
Study type: OBSERVATIONAL
Overall status: WITHDRAWN
Conditions: Pulmonary Disease
Chronic Obstructive Pulmonary Disease
Cancer, Lung
Lead sponsor: University of Iowa
Sponsor class: OTHER
Collaborators: name: Vida Diagnostics, Inc
First posted: 2018-08-09
Enrollment: 0
Sex: ALL
Minimum age (years): 50
Maximum age (years): 74
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Subjects will be patients at the University of Iowa Hospitals and Clinics that are participating in the Lung Cancer Screening clinic and have met those guidelines for being included in that screening program.

Brief summary: The purpose of this project is to validate quantitative lung structure assessment using an automated analysis software (VIDA), for application to low dose computed tomography (LDCT) acquired for lung cancer screening. Currently the software runs on standard dose CT data. In addition, it is the plan to incorporate algorithms into the software to address assessment of any identified pulmonary lesions.

Detailed description: The purpose of this project is to validate quantitative lung structure assessment using an automated analysis software (VIDA), for application to low dose computed tomography (LDCT) acquired for lung cancer screening. Currently the software runs on standard dose CT data. In addition, it is the plan to incorporate algorithms into the software to address assessment of any identified pulmonary lesions.

The Lung Cancer Screening Clinic is a new program that screens a group of patients that meet a predetermined criteria, set by the program, for lung cancer. At this time primary care providers, within the UIowa network, have the ability to place an order for this screening process for their patients. The hope is to expand this program to any primary care provider in the future. Clinic staff look over the patients past medical history to determine if they fit the clinic criteria.

For this study, the study team will ask the patients seen in the lung cancer screening clinic at the University of Iowa to undergo an additional standard dose CT scan during that same visit(for comparison/validation of the software performance). For those subjects that agree to participate, the study team will also ask to collect the data and CT scans that were also collected for their clinical Lung Cancer Screening visit. For this group the study team will also ask to collect all medical records associated with nodules found at this Lung Cancer Screening visit.

The study team propose to test the performance of VIDA's existing image analysis tools for the assessment of chronic obstructive pulmonary disease (COPD), when applied to the LDCT data acquired for lung cancer screening. The study team will also develop new lung nodule evaluation functions to specifically address the clinical needs within the at-risk lung cancer patient population.

To carry out the work, the study team propose two specific aims:

1. Validate VIDA's quantitative computed tomography (QCT) metrics for COPD and emphysema utilizing the lung cancer LDCT screening cohort as compared to the standard quantitative high resolution computed tomography (HRCT) scanning protocol. With collaboration between the University of Iowa and VIDA, the study team will establish a tightly controlled, CT data acquisition protocol in our local lung cancer screening program. Data collected will incorporate thin-slice and iterative reconstructions, breath coaching to target lung inflation levels for patients and HRCT data acquisition in a sub-cohort. The VIDA analysis will be refined and the appropriate reconstruction setting identified such that the outputs from the LDCT data correlate to the HRCT (current standard) outputs.
2. Complete and refine development of VIDA's novel lung nodule segmentation algorithm for solid and non-solid nodules using the standardized lung cancer LDCT screening protocol. Automated nodule segmentation in CT scans is challenging due to border ambiguity with neighboring structures (such as vessels and the pleura), poor boundary contrast of more subtle nodules, and noise associated with LDCT. The study team will develop a novel method that robustly segments pulmonary lesions by allowing mutual interaction between the nodules and surrounding structures, leading to a highly accurate segmentation with less manual corrective actions for a high volume lung cancer screening workflow.

Eligibility criteria: Inclusion Criteria:

* Subjects that meet the clinical Lung Cancer Screening guidelines and participate in that clinical screening.

Exclusion Criteria:

* Subjects that do not meet the clinical Lung Cancer Screening guidelines.

Primary outcomes:
measure: Segmental Airway Branch Differences
timeFrame: 1 day of Study Visit
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: University of Iowa
name: Jessica Sieren, PhD
role: PRINCIPAL_INVESTIGATOR
References: