Digital and computational pathology
The working group "Digital and computational pathology" under the guidance of Priv.-Doz. Dr. Yuri Tolkach has research interests in following areas:
- Diagnostic, prognostic, and predictive tools for pathology/oncology
- Multi-modal data integration (molecular characterization)
- Intratumoral heterogeneity and cancer evolution
- Large language models and reasoning
- Quality control in digital pathology
More details can be found via the homepage of the group: www.tolklab.de.
1. Kreten F, Büttner R, Peifer M, Harder C, Hillmer AM, Abedpour N, Bovier A, Tolkach Y. Tumor architecture and emergence of strong genetic alterations are bottlenecks for clonal evolution in primary prostate cancer. Cell Systems 2024: Nov 7 2405-4712(24)00302-8.
2. Kludt C, Wang Y, Ahmad W, Bychkov A, Fukuoka J, Gaisa N, Kühnel M, Jonigk D, Pryalukhin A, Mairinger F, Klein F, Schultheis AM, Seper A, Hulla W, Brägelmann J, Michels S, Klein S, Quaas A, Büttner R, Tolkach Y. Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms. Cell Reports Medicine 2024; 5(9):101697.
3. Weng Z, Seper A, Pryalukhin A, Mairinger F, Wickenhauser C, Bauer M, Glamann L, Bläker H, Lingscheidt T, Hulla W, Jonigk D, Schallenberg S, Bychkov A, Fukuoka J, Braun M, Schömig-Markiefka B, Klein S, Thiel A, Bozek K, Netto GJ, Quaas A, Büttner R, Tolkach Y. GrandQC: A radical solution to quality control in digital pathology. Nature Communications 2024 15: 10685.
4. Harder C, Pryalukhin A, Quaas A, Eich ML, Tretiakova M, Klein S, Seper A, Heidenreich A, Netto GJ, Hulla W, Büttner R, Bozek K, Tolkach Y. Enhancing Prostate Cancer Diagnosis: AI-Driven Virtual Biopsy for Optimal MRI-Targeted Biopsy Approach and Gleason Grading Strategy. Modern Pathology 2024 Jul 17:100564.
5. Griem J, Eich ML, Schallenberg S, Pryalukhin A, Bychkov A, Fukuoka J, Zayats V, Hulla W, Munkhdelger J, Seper A, Tsvetkov T, Mukhopadhyay A, Sanner A, Stieber J, Fuchs M, Babendererde N, Schömig-Markiefka B, Klein S, Buettner R, Quaas A, Tolkach Y. Clinical-grade tumor detection and quantitative tissue analysis in colorectal specimens using artificial intelligence. Modern Pathology 2023;36:100327.
6. Tolkach Y, Ovtcharov V, Pryalukhin A, Eich ML, Gaisa NT, Braun M, Radzhabov A, Quaas A, Hammerer P, Dellmann A, Hulla W, Haffner MC, Reis H, Fahoum I, Samarska I, Borbat A, Pham H, Heidenreich A, Klein S, Netto G, Caie P, Büttner R. An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading. NPJ Precis Oncol (Nature). 2023; 7:77.
7. Tolkach Y, Wolgast LM, Damanakis A, Pryalukhin A, Schallenberg S, Hulla W, Eich ML, Schroeder W, Mukhopadhyay A, Fuchs M, Klein S, Bruns C, Büttner R, Gebauer F, Schömig-Markiefka B, Quaas A. Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study. Lancet Digital Health. 2023; 5:e265-e275.
8. Tolkach Y, Dohmgörgen T, Toma M, Kristiansen G. High-accuracy prostate cancer pathology using deep learning. Nature Machine Intelligence 2020, 2:411-418.
9. Tolkach Y, Zarbl R, Bauer S, Ritter M, Ellinger J, Hauser S, Hüser L, Klauck SM, Altevogt P, Sültmann H, Dietrich D, Kristiansen G. DNA Promoter Methylation and ERG Regulate the Expression of CD24 in Prostate Cancer. American Journal of Pathology 2021 Apr;191(4):618-630.
10. Tolkach Y, Thomann S, Kristiansen G. Three-dimensional reconstruction of prostate cancer architecture with serial immunohistochemical sections: hallmarks of tumour growth, tumour compartmentalisation, and implications for grading and heterogeneity. Histopathology. 2018; 72:1051-1059.
- Digitization of clinically and molecular-genetically annotated pathological data for diagnostic, prognostic, and predictive profiling of malignant tumors (DIGI-PATH). REACT EU / EFRE North Rhine-Westphalia.
- Objectivization of prostate cancer pathology through artificial intelligence-based analysis and development of new prognostic and predictive tools. Wilhelm Sander Foundation, Germany.
- Federated learning for diagnostic and prognostic applications in digital oncopathology (FED-PATH). Federal Ministry of Research, Technology and Space (BMBF)
- Digital Pathology Innovations in Cross-Border Connectivity, AI and Mass Spectrometry (DigiPathConnect). Interreg Meuse-Rhein EU / EFRE North Rhine-Westphalia.
- Validation of a clinically accessible prognostic biomarker for oropharynx cancer using molecular and spatial data. NIH (USA).
- Multi-Omics and AI-based characterization of tertiary lymphoid structures in malignant tumors for prognostic and predictive applications. German Cancer Aid.
