Digital Pathology

Tumor budding is an independent predictor of lymph node metastases in pT1 colorectal cancer and predictor of survival in stage II colorectal cancer¹.   

Goal: automatic scoring

Partners: joint effort with the Institute of Pathology of the University Hospital Erlangen

Current Status:

• developed automatic analytics App for an immunohistochemistry staining (pan-cytokeratin – PCK) that is able
• to detect tumor invasion front
• to detect tumor buds
• to detect hot spots and determine low/intermediate/high budding based on cut offs

• algorithm trained on 40x PCK-stained whole-slides

Database: over 100 whole-slides with over 50.000 annotated tumor buds

Solution:

1. Detect tumor bud candidates using classical image processing that evaluates size, color, and distance to tumor
2. Weed out false positives using a Convolutional Neural Network trained to classify tumor buds

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¹Lugli et al, Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016

Goal: early detection of cancer metastasis by detecting even single disseminated tumor cells in a lymph node.

Partners: joint effort with the Fraunhofer ITEM and Fraunhofer IPA

Current Status:

• Development of single components (tissue grinder, scanning and image analysis, single cell DNA amplification, laboratory notebook software) finished
• Evaluation ongoing

Database:

• ~50 slides of melanoma and lung cancer
  

Solution:

• Prior work by University Tübingen and Fraunhofer ITEM has shown that even a presence of only a handful of disseminated cancer cells per million lymphocytes has a negative impact on survival
• Fraunhofer IPA has developed TissueGrinder for dissolving a lymph node into a cell suspension
• Immunocytological staining using melanoma marker (gp100)
• Scanning of cell suspension with Fraunhofer IIS’s SCube scanning platform
• Fraunhofer IIS image analysis: detection of tumor cell candidates and tumor vs. artefact classification with Convolutional Neural Network
  
• Molecular single cell analysis with assay developed by Fraunhofer ITEM (commercially available as Ampli1TM WGA)
  

Seeking Commercialization Partners

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Further Reading:
1. Ulmer A., et al., Quantitative measurement of melanoma spread in sentinel lymph nodes and survival. PLoS Med, 2014. 11(2): e1001604.
2. Ulmer A., et al., The sentinel lymph node spread determines quantitatively melanoma seeding to non-sentinel lymph nodes and survival. Eur J Cancer, 2018. 91: p. 1-10
3. Werner-Klein M., et al. Genetic alterations driving metastatic colony formation are acquired outside of the primary tumour in melanoma. Nat Commun, 2018. 9(1): p. 595

Goal: turn AI algorithms used in Digital Pathology from a black box into a glass box

Partners: University of Bamberg (Cognitive Systems, Prof. Dr. Ute Schmid), Fraunhofer HHI

Current Status:

• Research and evaluation of technologies to make AI algorithms more explainable
  

Solution:

• Display results from neural networks in an easily comprehensible and transparent manner
• Take into account uncertainties from the annotated training data as well from the model itself

Funded by Federal Ministry of Education and Research (BMBF)
Transparent Medical Expert Companion (TraMeExCo)

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Literature:
Schmid, Wittenberg (2019) TraMeExCo - Medical Expert Companion (Vortrag). All-Hands-Meeting (5. Juni 2019, Dortmund), Förderschwerpunkt Maschinelles Lernen des BMBF. -- [Slides], [Poster]
Rieger, Finzel, Seuß, Wittenberg, Schmid (2019) Make Pain Estimation Transparent: A Roadmap to Fuse Bayesian Deep Learning and Inductive Logic Programming (Poster). 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC, 23.-27. Juli 2019, Berlin)