Development of Computer-aided Detection and Diagnosis From Imaging Techniques

Cancer Clinical Trial

Official title:

Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Radiologic Images

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT00057252
• Other study ID #: 030128
• Secondary ID: 03-CC-0128
• Status: Completed
• Start date: March 20, 2003
• Est. completion date: October 2, 2020

Study information

• Verified date: January 2021
• Source: National Institutes of Health Clinical Center (CC)
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Other known NCT identifiers

• NCT00851097

Recruitment information / eligibility

• Status: Completed
• Enrollment: 139692
• Est. completion date: October 2, 2020
• Est. primary completion date: October 2, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

• 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.

Related Conditions & MeSH terms

• Cancer

Locations

Country	Name	City	State
United States	National Institutes of Health Clinical Center, 9000 Rockville Pike	Bethesda	Maryland

Sponsors (1)

Lead Sponsor	Collaborator
National Institutes of Health Clinical Center (CC)

Country where clinical trial is conducted

United States, 

References & Publications (3)

Burns JE, Yao J, Wiese TS, Muñoz 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. Erratum in: Radiology. 2013 Oct;269(1):311. — View 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. — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	New computer-aided detection methods--algorithms	computer-aided detection methods	Various

External Links

•   NIH Clinical Center Detailed Web Page