Summary
In 2004 when presenting the NCI Executive Committee the ACRIN proposal to conduct the CTC trial, a case was made that publicly accessible image data sharing would offer a valuable research asset to a wide image processing research community. Adding to the many merits of that proposal, the data-sharing component was strongly endorsed. ACRIN completed the trial expeditiously and its results were published in NEJM in fall 2008 to wide interest. Please see their NEJM publication for their published results (N Engl J Med. 2008 Sep 18;359(12):1207-17) and the ACRIN website itself for a more complete protocol description (http://www.acrin.org/TabID/151/Default.aspx). ACRIN has graciously allowed the wider research community access to a portion of the data from that trial.
Thus TCIA has made accessible a limited portion of DICOM image cases for download, de-identified of private health information, with spreadsheets identifying positive and negative polyp cases. The collection is offered to enlarge the broader research community's ability to develop CAD algorithms for polyp detection. CTC strengthened by competitive CAD methods stands to offer a more efficient public health tool for reducing the incidence of colon cancer. Below please find a user's outline for this resource.
Redirect | ||||
---|---|---|---|---|
|
Main Objective: To clinically validate widespread use of computerized tomographic colonography (CTC) in a screening population for the detection of colorectal neoplasia.
Participants: Male and female outpatients, aged 50 years or older, scheduled for screening colonoscopy, who have not had a colonoscopy in the past five years.
Study Design Summary: The study addresses aspects of central importance to the clinical application of CTC in several interrelated but independent parts that will be conducted in parallel. In Part I, the clinical performance of the CTC examination will be prospectively compared in a blinded fashion to colonoscopy. In Part II, optimization of the CT technique will be performed in view of new technological advances in CT technology. In Part III, lesion detection will be optimized by studying the morphologic features of critical lesion types and in the development of a database for computer-assisted diagnosis. In Part IV, patient preferences and cost-effectiveness implications of observed performance outcomes will be evaluated using a predictive model.
Localtab Group | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
...
|
...
|
...
|
Data Access
...
Collection Statistics
...
...
Modalities
...
CT
...
Number of Patients
...
825
...
Number of Studies
...
836
...
Number of Series
...
3,451
...
Number of Images
...