Summary
In 2004 when presenting the ACRIN proposal to conduct the CTC trial to the NCI Executive Committee, a case was made that image data sharing, publicly accessible through an as yet to be developed NCI Image Archive (NCIA) would be 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 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 NCIA offers a limited set (827) 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 provide the broad research community an ability to develop CAD algorithms for polyp detection. CTC strengthened by competitive CAD methods stands to offer an efficient public health tool for reducing the incidence of colon cancer.
The present "CT Colonography" (CTC) image collection, publicly downloadable in DICOM format at https://imaging.nci.nih.gov/ncia/ contains 827 cases from the well conducted, optical colonoscopy (OC) validated, ACRIN clinical trial whose subjects underwent same-day sequential CTC followed by OC. The attached documents in this Wiki offer separate lists of those OC verified cases that contain: [Imaging:CT Colonography^NCIA CTC no polyp found.xls]; [69 cases possessing 6 to 9 mm polyps|Imaging:CT Colonography^NCIA CTC 6 to 9 mm polyps.xls]; and [35 cases that possess at least one polyp "larger than 10 mm."|Imaging:CT Colonography^NCIA CTC large 10 mm polyps.xls]
Notably, this collection, though a subset of the ACRIN 2600+ total cases, nonetheless proportionally represents the incidence and prevalence of polyp found in the general population.
Accrual Details
Submission Software - Field Center & Clinical Trial Processor (CTP)
De-identification Schema - *insert da.script*
Collection Security - Publicly available to all registered users
Collection status - Complete (827 cases)
Modality types - CT
Contact Sites - ACRIN
Public Notification
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-->I sent out this announcement to the 609 NCIA regiestered users who had downloaded from the earlier smaller size original CTC collection to let them know that additional cases available. There are no plans to add any more cases since the composition of this 827 subset should suffice for use as a CAD training set. I'm still trying to get some colleagues to analyze and publish the x.y.z polyp positions of the positive cases in the collection to make them even more useful to cross-disciplinary researchers.
Carl
The NCI "CT Colonography" (CTC) image collection, downloadable as DICOM studies at https://imaging.nci.nih.gov/ncia/ has now been enlarged to a total of 827 cases acquiredfrom the optically (OC)-validated ACRIN CTC trial in which subjects underwent same-day CTC and OC. A description of the collection can be found at:
https://wiki.nci.nih.gov/download/attachments/13502514/NCIA+CTC+collection+explained.doc A key to the OC validation tables can be found at:
https://wiki.nci.nih.gov/download/attachments/13502514/Polyp+description+key+table.tiff Three polyp-size-grouped case lists can be found as:
'No polyp found' (243 cases);
https://wiki.nci.nih.gov/download/attachments/13502514/NCIA+CTC+no+polyp+found.xls 6 to 9 mm polyps (69 cases);
https://wiki.nci.nih.gov/download/attachments/13502514/NCIA+CTC+6+to+9+mm+polyps.xls and "larger than 10 mm" (35 cases) https://wiki.nci.nih.gov/download/attachments/13502514/NCIA+CTC+large+10+mm+polyps.xls This subset of the ACRIN 2600+ total trial-accrued cases http://acrin.org/TabID/151/Default.aspxrecapitulates the incidence and polyp size proportions found in the general population. As a result of data-sharing by the ACRIN organization, NCI provides this collection tothe biomedical research community as a resource that may be used as a training set to improve current day computational methods for colon polyp screening,
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