Clinical trial NCT00057252: Development of Computer-aided Detection and Diagnosis From Imaging Techniques
Official title: Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Radiologic Images
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Cancer
Keywords: Diagnostic Radiology
Imaging
Algorithms
Healthy Volunteer
HV
Lead sponsor: National Institutes of Health Clinical Center (CC)
Sponsor class: NIH
First posted: 2003-03-28
Enrollment: 139692
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with medical imaging records

Brief summary: This study will develop and evaluate new techniques for computer-aided detection and diagnosis (CAD) of medical problems using images from diagnostic tests such as computed tomography (CT), ultrasound, nuclear medicine and x-ray images. The Food and Drug Administration has approved CAD techniques for detecting masses and calcifications on mammography and lung nodules using chest x-rays. Many other applications of CAD would potentially benefit patients. This study will explore additional uses of CAD.

The study will use imaging data, demographic information, and other medical information from the medical charts of Clinical Center patients to test and evaluate new CAD applications. Such applications include detection of subcutaneous (under the skin) lesions in melanoma patients, bone lesions in patients with advanced cancer, and pulmonary emboli (blood clot lodged in a lung artery) in patients who are known to have pulmonary emboli, and other uses.

Detailed description: Radiologic images are becoming more and more complex, and utilization of radiologic techniques is accelerating. Radiologists and other clinicians are being inundated with radiologic data. Computer aided detection and diagnosis (CAD) have the potential to improve patient care by increasing sensitivity of diagnostic tests, reducing false positives and improving physician efficiency. Computer aided detection and diagnosis have been under development for many years yet there is still much work to be done to move it from the bench to the bedside. The purpose of this project is to develop and evaluate techniques for CAD using the existing radiologic data available in the Clinical Center's Department of Diagnostic Radiology. Such techniques include but are not limited to automated detection of melanoma, bone metastases and pulmonary emboli. The outcome of this study will be algorithms and software that accurately detect lesions on radiologic studies.

Eligibility criteria: * INCLUSION CRITERIA:

Inclusion criteria are the availability of radiologic examinations in the clinical PACS (picture archiving system) in the Clinical Center. Existing Patient scans with and without the target lesion will be included. Examples of target lesions include subcutaneous and bone lesions and pulmonary emboli, although patient scans with other disorders depicted on radiologic studies may be included when appropriate. Patient scans without the target lesion may be included to determine the specificity of the computer aided detection or diagnosis algorithm.

EXCLUSION CRITERIA:

There are no exclusion criteria.

Primary outcomes:
measure: New computer-aided detection methods--algorithms
timeFrame: Various
Secondary outcomes:
Other outcomes:
Locations:
city: Bethesda
country: United States
facility: National Institutes of Health Clinical Center, 9000 Rockville Pike
state: Maryland
Study officials:
affiliation: National Institutes of Health Clinical Center (CC)
name: Ronald M Summers, M.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Liu J, Wang S, Linguraru MG, Yao J, Summers RM. Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT. Med Image Anal. 2014 Jul;18(5):725-39. doi: 10.1016/j.media.2014.04.001. Epub 2014 Apr 18.
citation: Zhang W, Liu J, Yao J, Louie A, Nguyen TB, Wank S, Nowinski WL, Summers RM. Mesenteric vasculature-guided small bowel segmentation on 3-D CT. IEEE Trans Med Imaging. 2013 Nov;32(11):2006-21. doi: 10.1109/TMI.2013.2271487. Epub 2013 Jun 27.
citation: Burns JE, Yao J, Wiese TS, Munoz HE, Jones EC, Summers RM. Automated detection of sclerotic metastases in the thoracolumbar spine at CT. Radiology. 2013 Jul;268(1):69-78. doi: 10.1148/radiol.13121351. Epub 2013 Feb 28.