CancerNCT00057252
Development of Computer-aided Detection and Diagnosis From Imaging Techniques
An observational study of Cancer, run by National Institutes of Health Clinical Center (CC) at 1 site in United States.
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cancer
- How many people
- 139,692 participants
- Where
- 1 siteUnited States
- Study dates
- 20 Mar 2003to 2 Oct 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with medical imaging records
Who may be able to join
- 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.
Who cannot join
- There are no exclusion criteria.
Trial locations and contacts
1 site in 1 country.
United States1 site
National Institutes of Health Clinical Center, 9000 Rockville Pike
Bethesda, Maryland, 20892
About this study
Official title: Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Radiologic Images
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.
Read the 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.
- Conditions
- Cancer
- Keywords
- Diagnostic Radiology, Imaging, Algorithms, Healthy Volunteer, HV
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 1 | Patients with medical imaging records |
What is being measured
The primary outcome is the main question the study is designed to answer.
New computer-aided detection methods--algorithms
computer-aided detection methods
Measured: Various
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Mar 2003 (estimated)
- Study start20 Mar 2003
- Primary completion2 Oct 2020
- Study completion2 Oct 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 139,692 participants (actual)
Sponsor and investigators
- Lead sponsor
- National Institutes of Health Clinical Center (CC)
- Sponsor type
- US National Institutes of Health
- Responsible party
- Sponsor
- Investigators
- Ronald M Summers, M.D., Principal investigator, National Institutes of Health Clinical Center (CC)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT00057252
- Sponsor's protocol ID
- 030128
- Other IDs
- 03-CC-0128
- Status last verified
- Jan 2021
- First submitted
- 13 Jul 2006
- Last updated
- 22 Jan 2021
- Results posted
- No
Publications
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.
PubMed 24835180Zhang 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.
PubMed 23807437Burns 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.
PubMed 23449957
Study at a glance
From the registry record, last updated by the sponsor on 22 Jan 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Evaluation of Techniques for Computer Aided Detection and Diagnosis From Radiologic Images
- ClinicalTrials.gov ID
- NCT00057252
- Acronym / sponsor ID
- 030128
- Conditions
- Cancer
- Keywords
- Diagnostic Radiology, Imaging, Algorithms, Healthy Volunteer, HV
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 139,692 participants (actual)
- Lead sponsor
- National Institutes of Health Clinical Center (CC)US National Institutes of Health
- Study start
- 20 Mar 2003 (actual)
- Primary completion
- 2 Oct 2020 (actual)
- Study completion
- 2 Oct 2020 (actual)
- First posted
- 28 Mar 2003 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 1 | – | –Patients with medical imaging records |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with medical imaging records
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- There are no exclusion criteria.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
New computer-aided detection methods--algorithms
Time frame: Various
computer-aided detection methods
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jan 2021.
- Background: PubMed 24835180Liu 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.
- Background: PubMed 23807437Zhang 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.
- Background: PubMed 23449957Burns 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.
- NIH Clinical Center Detailed Web PageLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ronald M Summers, M.D. principal investigator
National Institutes of Health Clinical Center (CC)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cancer, run by National Institutes of Health Clinical Center (CC) at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 22 Jan 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
New computer-aided detection methods--algorithms (time frame: Various). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT00057252, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jan 2021.