Summary
This dataset comprises four prevalent AML subtypes with defining genetic abnormalities and typical morphological features according to the WHO 2022 classification: (i) APL with PML::RARA fusion, (ii) AML with NPM1 mutation, (iii) AML with CBFB::MYH11 fusion (without NPM1 mutation), and (iv) AML with RUNX1::RUNX1T1 fusion, as well as a control group of healthy stem cell donors. A total of 242 peripheral blood smears from the Munich Leukemia Laboratory (MLL) database from the years 2009 to 2020 were digitized and consist of 99 - 500 automatically selected individual white blood cell images per patient.
First, all blood smears were scanned with 10x magnification and an overview image was created. Using the Metasystems Metafer platform, cell detection was performed automatically using a segmentation threshold and logarithmic color transformation. Further analysis regarding the quality of the region within the blood smear was performed automatically. White blood cells with the highest scores were then scanned in 40x magnification via oil immersion microscopy in .TIF format, corresponding to 24,9μm x 24,9μm (144x144 pixels). For this, a CMOS Color Camera from MetaSystems with a resolution of 4096x3000px and a pixel size of 3,45μm x 3,45μm was used. Four pixels were binned into one, leading to a size of 6.9μm x 6.9μm, and a resolution of 6.9μm / 40 (1px = 0,1725μm).
Additional information about patient age, sex and blood counts are provided in a separate .csv file.
To our knowledge, this dataset covers the morphological complexity of acute myeloid leukemia in peripheral blood smears in unseen quality and quantity. With 4 different types of AML with defining genetic abnormalities and healthy controls, this dataset exceeds existing other datasets and thus brings the scientific community one step closer to real world hematology. We believe that our data can help scientists all over the world to develop new models and combine the data with other data sources to ameliorate AML diagnostics.
Acknowledgements
- All samples were collected, diagnosed and scanned at the Munich Leukemia Laboratory (MLL). Carsten Marr has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 866411). Matthias Hehr acknowledges support from Deutsche José Carreras-Leukämie Stiftung.
Data Access
Data Type | Download all or Query/Filter | License |
---|---|---|
Tissue Slide Images (SVS, XX.X GB) | (Download requires Aspera plugin) | |
Clinical data (CSV) |
Click the Versions tab for more info about data releases.
Additional Resources for this Dataset
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>
Detailed Description
Image Statistics | Pathology Image Statistics |
---|---|
Modalities | |
Number of Patients | |
Number of Images | |
Images Size (GB) |
<< Add any additional information that didn't fit or belong in the Summary section. >>
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
draft DOI: https://doi.org/10.7937/6ppe-4020
Publication Citation
Manuscript under review: https://journals.plos.org/digitalhealth/
TCIA 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 (Vol. 26, Issue 6, 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.