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  • Segmentation and Classification of Grade I and II Meningiomas from Magnetic Resonance Imaging: An Open Annotated Dataset (Meningioma-SEG-CLASS)

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Summary

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delay5
locationhttps://www.cancerimagingarchive.net/collection/meningioma-seg-class/

Excerpt

Image AddedThe study included 96 consecutive treatment naïve patients with intracranial meningiomas treated with surgical resection from 2010 to 2019. All patients had pre-operative T1, T1-CE, and T2-FLAIR MR images with subsequent subtotal or gross total resection of pathologically confirmed Grade grade I or grade II meningiomas. A neuropathology team reviewed histopathology, including two subspecialty trained neuropathologists and one neuropathology fellow. The meningioma grade was confirmed based on current classification guidelines, most recently described in the 2016 WHO Bluebook. Clinical information includes grade, subtype, type of surgery, tumor location, and atypical features. Meningioma labels on T1ce T1-CE and T2-FLAIR images will also be provided in DICOM format.

Other researchers can use the data to build deep learning models to predict meningioma grade I and II based on diagnostic MR images (T1CE and T2-FLAIR).

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

...

Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.

The hyperintense T1-contrast enhancing tumor and hyperintense T2-FLAIR and tumor were manually contoured on each MRI and reviewed by a central nervous system radiation oncologist specialist.




...

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Localtab
activetrue
titleData Access

Data Access

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General restricted license access

Note to curators! Only use the appropriate macro for your collection above, or the text below in the case of NCTN trials.  It will need to be tailored with the correct NCT#.

This is a limited access data set. To request access please register an account on the NCTN Data Archive.  After logging in, use the "Request Data" link in the left side menu.  Follow the on screen instructions, and enter NCT00352534 when asked which trial you want to request.  In step 2 of the Create Request form, be sure to select “Imaging
Data
Requested”. Please contact NCINCTNDataArchive@mail.nih.gov for any questions about access requests.
Data
TypeDownload all or Query/FilterLicense

Images

, Segmentations,

and Radiation Therapy Structures

/Doses/Plans

(DICOM,

XX

9.

X

0 GB)

<< latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >>



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urlhttps://wiki.cancerimagingarchive.net/download/attachments/133071972/Meningioma%20SEG%20Class%20February%202022%20manifest.tcia?api=v2



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urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Meningioma-SEG-CLASS



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Clinical data (CSV, 20 kB)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/133071972/Meningioma-SEG-Class%20Clinical%20Data.xlsx?api=v2



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Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

Note to curators! Use this any time you are linking to NCI's IDC/GDC/PDC resources.  The links below are examples and will need to be tailored to point to the specific dataset (see parameters in URLS).

Nci_crdc additional resources

Note to curators! The link below is an example for NCTN trials and will need to be tailored to the proper URL for the corresponding data on the NCTN Data Archive.nctn-additional-resources

Note to curators! Below are examples for what to do with other external resources/links that don't fit into the above categories.

The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

  • Software / Code on Github
  • Genomics data in DbGAP
  • Genomics data in Gene Expression Omnibus

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:

  • <add links to TCIA Analysis Result DOIs here>



Localtab
titleDetailed Description

Detailed Description

Image Statistics

Radiology Image StatisticsPathology Image Statistics

Modalities

MR, RTSTRUCT

Number of Patients

96

Number of Studies

180

Number of Series

674

Number of Images

47520

Images Size (GB)
<< Add any additional information that didn't fit or belong in the Summary section. >>
9 GB



Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Tcia limited license policy

Info
titleData Citation

DOI goes here. Create using Datacite with information from Collection Approval formVassantachart, A., Cao, Y., Shen, Z., Cheng, K., Gribble, M., Ye, J. C., Zada, G., Hurth, K., Mathew, A., Guzman, S., & Yang, W. (2023). Segmentation and Classification of Grade I and II Meningiomas from Magnetic Resonance Imaging: An Open Annotated Dataset (Meningioma-SEG-CLASS) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/0TKV-1A36


Info
titlePublication Citation

We ask on the proposal form if they have ONE traditional publication they'd like users to cite.

Info
titleAcknowledgement

Required acknowledgements only (ex:The CPTAC program requests that publications using data from this program...). If they just want to thank someone, that goes in the Acknowledgement section underneath the Summary.Vassantachart, A., Cao, Y., Gribble, M., Guzman, S., Ye, J. C., Hurth, K., Mathew, A., Zada, G., Fan, Z., Chang, E. L., & Yang, W. (2022). Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network. In Scientific Reports (Vol. 12, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41598-022-07859-0


Info
titleTCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013).   The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, . In Journal of Digital Imaging , Volume (Vol. 26, Number Issue 6, December, 2013, pp 1045-1057. DOI: , pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.


<< One or two sentences about what you changed since last version.  No note required for version 1. >> 
Localtab
titleVersions

Version

X

1 (Current): Updated

yyyy

2023/

mm

02/

dd

13

Data TypeDownload all or Query/FilterLicense

Images, Segmentations, and Radiation Therapy Structures

/Doses/Plans

(DICOM,

XX

9.

X

0 GB)

<< latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >>



Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/133071972/Meningioma%20SEG%20Class%20February%202022%20manifest.tcia?api=v2



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labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Meningioma-SEG-CLASS


(Download requires the NBIA Data Retriever)

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-cc-by

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restricted-license

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Clinical data (CSV)


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labelSearch

Tcia cc by 4

Tcia restricted license

Nctn_with_collab license only

Nctn_no_collab license only

Clinical data (CSV)
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Tcia cc by 4

Tcia restricted license

Nctn_with_collab license only

Nctn_no_collab license only

urlhttps://wiki.cancerimagingarchive.net/download/attachments/133071972/Meningioma-SEG-Class%20Clinical%20Data.xlsx?api=v2



Tcia cc by 4