Page History

Note: The following download links will download the entire collection.

See the Detailed Description section of this page for information about subsets specific to the PROSTATEx challenges.

Click the Versions tab for more info about data releases.

• Imaging Data Commons (IDC) (Imaging Data)

Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

• High Resolution Prostate Segmentations for the ProstateX-Challenge 
• PROSTATEx Zone Segmentations

PROSTATEx Challenge (November 21, 2016 to February 16, 2017)

SPIE, along with the support of the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI), conducted a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of clinically significant prostate lesions.  For more details, go to https://prostatex.grand-challenge.org/ .  

Training and test cohorts along with supplemental Ktrans images and lesion information can be obtained separately using the following options for download:

The images come in two encodings. The acquired MR is provided in DICOM encoding. Additionally Ktrans images are provided. They come in mhd format. Ktrans is a key pharmacokinetic parameter computed from the available Dynamic contrast enhanced T1-weighted series. Each patient has one study with several DICOM images and one Ktrans image. The Ktrans image is encoded in two files ProstateX-[ProxID]-Ktrans.[mhd/zraw], where ProxID is the ProstateX patient identifier. The DICOM images comprise several Series each comprising several Instances. The DICOM files are documented in the ProstateX-Images.csv file. The columns in that file encode the following:

• ProxID – ProstateX patient identifier.

• Name – Series Description

• Studydate – Study Date

• fid – Finding ID

• Pos – Scanner Coordinate position of the finding

• WorldMatrix – Matrix describing image orientation and scaling

• ijk – image col,row,slice coordinate of finding

• ImageUID – Image Identifier

• TopLevel

  • 0 - Series forms one image

  • 1 – A set of Series forms a 4D image (e.g. Dynamic MR)

  • NA – Series form one image, but is part of a Level 1 4D image

• SpacingBetweenSlices – Scalar Spacing between slices

• VoxelSpacing – Vector with x,y,z spacing scalars

• Dim – Vector with 4D dimensions of the image

• DCMSerDescr – The original DICOM Series Description

• DCMSerUID – The DICOM Series UID

• DCMSerNum – The DICOM Series Number

• InstanceUIDList – DICOM Instances that make up this series

• ImageUIDList – TopLevel-NA Images the make up this Toplevel 1 image

For example, to get the ADC image of Patient ProstateX-0123 do the following. After you imported the DICOM files into your environment, go to patient ProstateX-0123 and find the series with ADC in it. In this case it is ‘ep2d_diff_tra_DYNDIST_ADC’. It has SeriesNumber 8. The DICOM images in that series form the ADC image for this challenge. Image slice j at coordinate i,j contains a finding fid. See findings for more details.

Findings

The findings are documented in the ProstateX-Findings.csv table. Documentation for the columns in that table is as follows:

• ProxID – ProstateX patient identifier

• fid - Finding ID

• pos - Scanner Coordinate position of the finding

• ClinSig – Identifier available in training set that identifies whether this is a clinically significant finding. Either the biopsy GleasonScore was 7 or higher. Findings with a PIRADS score 2 were not biopsied and are not considered clinically significant. In our center the occurrence of clinically significant cancer in PIRADS 2 lesions is less than 5%.

Note, ProxID is the PROSTATEx case ID, and fid is the finding (i.e., lesion) ID in both the ProstateX-Findings.csv file and the ProstateX-Images.csv file.  The Findings spreadsheet has one row per lesion (if a case has only one lesion, then the only fid for that case will be “1” , alternately if a case has two lesions, then there will be an fid of “1” and an fid of “2” for that case).  The Images spreadsheet has a row for every image that contains the lesion - that is why there are multiple rows with the same (ProxID, fid) combination.

Other Publications Using This Data

TCIA maintains a list of publications

that leverage our data.

If you have a publication you'd like to add, please contact

TCIA's Helpdesk.

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Added Test Reference Standard document

Version 1: Updated 2017/03/29