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Principal Investigator: HAN WEN
Organization: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE
Fiscal Year: 2024
Award: $1,531,012
Funding agency: National Heart Lung and Blood Institute
This project is the main effort of my lab for the last year. On the clinical application front, we further developed computed-tomography image analysis tools for two clinical protocols:
96-H-0100, LAM and other rare cystic lung diseases, PI Joel Moss, NHLBI Pulmonary Branch
Lymphangioleiomyomatosis (LAM) is a multi-system disease that affects almost exclusively women. It causes progressive formation of air-filled cysts in the lungs and associated decline of lung function. With a median transplant-free survival of 29 years from the onset of symptoms, computed-tomographic tracking of cystic changes in the lungs is an integral part of the management of the disease, and provides valuable information in clinical studies on the treatment of the disease. This is an on-going collaboration with Dr. Joel Moss since 2018.
This year we focused our effort on solving a persistent issue in the routine clinical evaluation of these patients, namely the inconsistencies in the CT-derived measure of the extent of cystic changes in the lungs. The formation of air-filled cysts in the lungs is a hallmark of lung involvement of the disease. In regular follow ups over the course of the disease, CT scans are used to measure the extent and the rate of change of the cyst formation in the lungs. The measurement is the cyst score, the percentage of the total lung volume occupied by air-filled cysts. In the FDA-approved standard semi-automatic software, a trained operator manually adjusts a threshold of brightness based on visual inspection to identify the cysts in the images. However, the procedure is susceptible to variability in human visual judgement, either between operators or of the same operator at different times. The problem is compounded by variability of CT density values across different scanner hardware and/or software versions. The resulting inconsistency particularly affects the assessment of the rate of change of the cyst score between consecutive scans, which causes uncertainty for the clinical care team and unnecessary anxiety on the part of the patient.
We solved the problem by developing fully automated software to identify and measure the pulmonary cysts. The software incorporated self-adaptive features to replace human adjustment. The robustness of the software was demonstrated in a preliminary study involving 268 CT scans from 24 patients over a period of 23 years. This was followed by a validation study involving 208 CT scans from 152 LAM patients. The validation study demonstrated the agreement between the automatic cyst scores and independent measures of pulmonary function tests, and improved consistency of the cyst scores when compared to the standard semi-automatic scoring procedure. Following the validation study, the software has been used weekly for LAM patient CT scans since February of 2024. The resulting automated cyst scores are reported to the clinical care team along with the standard cyst scores, and help them assess changes particularly when the standard scores show unusually large changes.
Since our automated software has reached the stage of routine clinical utility for LAM patients, we began to work on FDA 510k applications to allow it to replace the standard software in the routine care of the patients. This year we made preliminary progress in this area, including obtaining a 510k filing plan, identification of the predicate, FDA product code and the safety procedures.
18-H-0108, Genetic disease ACDC, PI Manfred Boehm, NHLBI Translational Vascular Medicine
This protocol studies the rare genetic disease of arterial calcification due to deficiency of CD73, or ACDC. Patients with ACDC have progressive vascular calcification in the extremities and the joints of the hands and feet, with symptoms of pain and cramping in the extremities as early as their twenties. As a collaborator on this protocol, we perform ultra-high resolution CT scans on a yearly basis for the patients enrolled in the protocol, and make CT-based measurements to assess the progression or regression of arterial calcification in the lower extremity of the patients.
This year we performed data analysis of the amount of calcification in the lower extremity and the growth of the front edge of the calcification towards the ankles. We found that these two measures may not be associated with each other. In several discussions with Dr. Moehm’s team, we decided to modify the CT analysis and focus on measuring the total amount of calcification in the lower extremity as well as the total volume of calcified tissue. We then developed a semi-automated software pipeline for these measurements. The ongoing work is the retrospective measurements of CT scans dating back to 2011 in all patients to establish baseline trend, followed by yearly update of the measurements for the annual or bi-annual follow-ups. These data are expected to be part of any future trials of potential medical treatment for these patients.
Additionally we participated in the clinical protocol 19-CC-0070 “Translational Development of Photon-Counting CT Imaging” as a collaborator of Dr. Ashkan Malayeri of Clinical Center Radiology. We previously performed scan protocol construction, optimization and regular calibration of the photon-counting CT scanner for the weekly scans of renal cancer patients under this protocol. This year the work is published in Investigative Radiology.
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