7  Makaleler

7.1 Makaleler ve Kodlar

• Artificial Intelligence-Based Single-Cell Analysis as a Next-Generation Histologic Grading Approach in Colorectal Cancer: Prognostic Role and Tumor Biology Assessment (Barroso et al. 2025)

https://github.com/cpath-ukk/crc_cells

• Deep learning inference of cell type-specific gene expression from breast tumor histopathology (Wang et al. 2025)

https://www.biorxiv.org/content/10.1101/2025.05.04.652089v1

• Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics Alber et al. (2025)

https://arxiv.org/abs/2501.05409

• AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics Dippel et al. (2024)

https://ai.nejm.org/doi/full/10.1056/AIoa2400468

• Tumor Bud Classification in Colorectal Cancer Using Attention-Based Deep Multiple Instance Learning and Domain-Specific Foundation Models Şeker et al. (2025)

• Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods Fisher et al. (2021)

https://github.com/petebankhead/qupath-budding-scripts

• Multimodal histopathologic models stratify hormone receptor-positive early breast cancer Boehm et al. (2025)

https://www.nature.com/articles/s41467-025-57283-x

https://github.com/kmboehm/orpheus

https://github.com/KatherLab/STAMP

https://github.com/KatherLab/marugoto

https://gist.github.com/kmboehm/aea77f24a9cdbb1f246dacaae812053d

https://www.synapse.org/Synapse:syn53649589/files/

https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(24)00038-6 | Virtual staining for histology by deep learning: Trends in Biotechnology https://www.nature.com/articles/s41377-023-01104-7 | Deep learning-enabled virtual histological staining of biological samples | Light: Science & Applications https://www.cell.com/patterns/fulltext/S2666-3899(23)00065-X | The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility: Patterns https://www.nature.com/articles/s42256-024-00889-5 | Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling | Nature Machine Intelligence https://www.nature.com/articles/s41746-024-01106-8 | Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy | npj Digital Medicine https://www.nature.com/articles/s41379-021-00919-2 | Digital pathology and artificial intelligence in translational medicine and clinical practice | Modern Pathology

https://impdiagnostics.com/wp/wp-content/uploads/2024/01/02_Annotating-for-Artificial-Intelligence-Applications-in-Digital-Pathology_-A-Practical-Guide-for-Pathologists-and-Researchers-2023.pdf | impdiagnostics.com/wp/wp-content/uploads/2024/01/02_Annotating-for-Artificial-Intelligence-Applications-in-Digital-Pathology_-A-Practical-Guide-for-Pathologists-and-Researchers-2023.pdf

https://pubmed.ncbi.nlm.nih.gov/36788085/ | Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers - PubMed

https://pubmed.ncbi.nlm.nih.gov/40222652/ | Artificial Intelligence-Based Single-Cell Analysis as a Next-Generation Histologic Grading Approach in Colorectal Cancer: Prognostic Role and Tumor Biology Assessment - PubMed

Clinical USe

Triage

Tumor Area Detection

• Automated determination of tumor cell percentages in whole slide images: A nuclear classification study for molecular pathology tests

https://www.sciencedirect.com/science/article/pii/S2153353925000367

https://doi.org/10.1016/j.jpi.2025.100451

• Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline

https://link.springer.com/article/10.1007/s00428-024-03794-9

https://doi.org/10.1007/s00428-024-03794-9

QuANTUM

• Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology

https://link.springer.com/article/10.1038/s41598-024-75175-w

https://doi.org/10.1038/s41598-024-75175-w

QuANTUM

7.2 Concept Papers

• https://digitalpathologyassociation.org/concept-papers

• A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association Zarella et al. (2018)

  https://pubmed.ncbi.nlm.nih.gov/30307746/

• Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association Aeffner et al. (2019)

  https://pubmed.ncbi.nlm.nih.gov/30984469/

• Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association Abels et al. (2019)

  https://pubmed.ncbi.nlm.nih.gov/31355445/

• Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association Lujan et al. (2021)

  https://pubmed.ncbi.nlm.nih.gov/34221633/

• Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association Lara et al. (2021)

  https://pubmed.ncbi.nlm.nih.gov/33734106/

• Bridging the Gap: The Critical Role of Regulatory Affairs and Clinical Affairs in the Total Product Life Cycle of Pathology Imaging Devices and Software Kearney et al. (2021)

  https://pubmed.ncbi.nlm.nih.gov/34869473/

• Rules of engagement: Promoting academic-industry partnership in the era of digital pathology and artificial intelligence Pantanowitz et al. (2022)

  https://pubmed.ncbi.nlm.nih.gov/35669406/

• Pathology Education Powered by Virtual and Digital Transformation: Now and the Future Hassell et al. (2022)

  https://pubmed.ncbi.nlm.nih.gov/35878400/

• Artificial intelligence and digital pathology: clinical promise and deployment considerations Zarella et al. (2023)

  https://pubmed.ncbi.nlm.nih.gov/37528811/

• Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making Ardon et al. (2024)

  https://pubmed.ncbi.nlm.nih.gov/38736870/

• Ethical and Regulatory Perspectives on Generative Artificial Intelligence in Pathology Jackson et al. (2024)

  https://pubmed.ncbi.nlm.nih.gov/39299704/

• Harnessing the Power of Generative Artificial Intelligence in Pathology Education: Opportunities, Challenges, and Future Directions Cecchini et al. (2024)

  https://pubmed.ncbi.nlm.nih.gov/39343982/

• Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward McCaffrey et al. (2024)

  https://pubmed.ncbi.nlm.nih.gov/39384182/

• Introduction to Generative Artificial Intelligence: Contextualizing the Future Singh et al. (2024)

  https://pubmed.ncbi.nlm.nih.gov/39631430/

• Generative Artificial Intelligence in Anatomic Pathology Brodsky et al. (2025)

  https://pubmed.ncbi.nlm.nih.gov/39836377/

• Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging and Classification Hassell et al. (2025)

  https://doi.org/10.1016/j.modpat.2025.100765

• Pathology in the Age of Artificial Intelligence (AI): Redefining Roles and Responsibilities for Tomorrow’s Practitioners

https://pmc.ncbi.nlm.nih.gov/articles/PMC11008776/

• Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance

https://pmc.ncbi.nlm.nih.gov/articles/PMC10572440/

• Lymphoma triage from H&E using AI for improved clinical management (Tsakiroglou et al. 2023)

https://jcp.bmj.com/content/78/1/28

7.3 Literatür Haritaları

LitMaps

NotebookLM - Concept Papers

NotebookLM-Mind-Map-Concept-Papers

7.4 Kılavuzlar

• Leica Digital Pathology & Artificial Intelligence Resources

• CAP Artificial Intelligence (AI) in Pathology

• RCPA Artificial Intelligence and Machine Learning

Abels, Esther, Liron Pantanowitz, Famke Aeffner, Mark D Zarella, Jeroen van der Laak, Marilyn M Bui, Venkata NP Vemuri, et al. 2019. “Computational Pathology Definitions, Best Practices, and Recommendations for Regulatory Guidance: A White Paper from the Digital Pathology Association.” The Journal of Pathology 249 (3): 286–94. https://doi.org/10.1002/path.5331.

Aeffner, Famke, Mark D. Zarella, Nathan Buchbinder, Marilyn M. Bui, Matthew R. Goodman, Douglas J. Hartman, Giovanni M. Lujan, et al. 2019. “Introduction to Digital Image Analysis in Whole-Slide Imaging: A White Paper from the Digital Pathology Association.” Journal of Pathology Informatics 10 (1): 9. https://doi.org/10.4103/jpi.jpi_82_18.

Alber, Maximilian, Stephan Tietz, Jonas Dippel, Timo Milbich, Timothée Lesort, Panos Korfiatis, Moritz Krügener, et al. 2025. “Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics.” https://doi.org/10.48550/ARXIV.2501.05409.

Ardon, Orly, Sylvia L. Asa, Mark C. Lloyd, Giovanni Lujan, Anil Parwani, Juan C. Santa-Rosario, Bryan Van Meter, et al. 2024. “Understanding the Financial Aspects of Digital Pathology: A Dynamic Customizable Return on Investment Calculator for Informed Decision-Making.” Journal of Pathology Informatics 15 (December): 100376. https://doi.org/10.1016/j.jpi.2024.100376.

Barroso, Vincenzo Mitchell, Zhilong Weng, Lennert Glamann, Marcus Bauer, Claudia Wickenhauser, Thomas Zander, Reinhard Büttner, Alexander Quaas, and Yuri Tolkach. 2025. “Artificial IntelligenceBased Single-Cell Analysis as a Next-Generation Histologic Grading Approach in Colorectal Cancer: Prognostic Role and Tumor Biology Assessment.” Modern Pathology 38 (7): 100771. https://doi.org/10.1016/j.modpat.2025.100771.

Boehm, Kevin M., Omar S. M. El Nahhas, Antonio Marra, Michele Waters, Justin Jee, Lior Braunstein, Nikolaus Schultz, et al. 2025. “Multimodal Histopathologic Models Stratify Hormone Receptor-Positive Early Breast Cancer.” Nature Communications 16 (1). https://doi.org/10.1038/s41467-025-57283-x.

Brodsky, Victor, Ehsan Ullah, Andrey Bychkov, Andrew H. Song, Eric E. Walk, Peter Louis, Ghulam Rasool, et al. 2025. “Generative Artificial Intelligence in Anatomic Pathology.” Archives of Pathology & Laboratory Medicine 149 (4): 298–318. https://doi.org/10.5858/arpa.2024-0215-ra.

Cecchini, Matthew J., Michael J. Borowitz, Eric F. Glassy, Rama R. Gullapalli, Steven N. Hart, Lewis A. Hassell, Robert J. Homer, Ronald Jackups, Jeffrey L. McNeal, and Scott R. Anderson. 2024. “Harnessing the Power of Generative Artificial Intelligence in Pathology Education: Opportunities, Challenges, and Future Directions.” Archives of Pathology & Laboratory Medicine 149 (2): 142–51. https://doi.org/10.5858/arpa.2024-0187-ra.

Dippel, Jonas, Niklas Prenißl, Julius Hense, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, et al. 2024. “AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics.” NEJM AI 1 (11). https://doi.org/10.1056/aioa2400468.

Fisher, Natalie C, Maurice B Loughrey, Helen G Coleman, Melvin D Gelbard, Peter Bankhead, and Philip D Dunne. 2021. “Development of a Semi-Automated Method for Tumour Budding Assessment in Colorectal Cancer and Comparison with Manual Methods.” Histopathology 80 (3): 485–500. https://doi.org/10.1111/his.14574.

Hassell, Lewis A., Syeda Fatima Absar, Chhavi Chauhan, Suzanne Dintzis, Carol F. Farver, Samreen Fathima, Eric F. Glassy, et al. 2022. “Pathology Education Powered by Virtual and Digital Transformation: Now and the Future.” Archives of Pathology & Laboratory Medicine 147 (4): 474–91. https://doi.org/10.5858/arpa.2021-0473-ra.

Hassell, Lewis A., Marika L. Forsythe, Ami Bhalodia, Thanh Lan, Tasnuva Rashid, Astin Powers, Marilyn M. Bui, et al. 2025. “Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging and Classification.” Modern Pathology, April, 100765. https://doi.org/10.1016/j.modpat.2025.100765.

Jackson, Brian R., Hooman H. Rashidi, Jochen K. Lennerz, and M. E. de Baca. 2024. “Ethical and Regulatory Perspectives on Generative Artificial Intelligence in Pathology.” Archives of Pathology & Laboratory Medicine 149 (2): 123–29. https://doi.org/10.5858/arpa.2024-0205-ra.

Kearney, Staci J., Amanda Lowe, Jochen K. Lennerz, Anil Parwani, Marilyn M. Bui, Katy Wack, Gina Giannini, and Esther Abels. 2021. “Bridging the Gap: The Critical Role of Regulatory Affairs and Clinical Affairs in the Total Product Life Cycle of Pathology Imaging Devices and Software.” Frontiers in Medicine 8 (November). https://doi.org/10.3389/fmed.2021.765385.

Lara, Haydee, Zaibo Li, Esther Abels, Famke Aeffner, Marilyn M. Bui, Ehab A. ElGabry, Cleopatra Kozlowski, et al. 2021. “Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.” Applied Immunohistochemistry & Molecular Morphology 29 (7): 479–93. https://doi.org/10.1097/pai.0000000000000930.

Lujan, Giovanni, Jennifer C. Quigley, Douglas Hartman, Anil Parwani, Brian Roehmholdt, Bryan Van Meter, Orly Ardon, et al. 2021. “Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association.” Journal of Pathology Informatics 12 (1): 17. https://doi.org/10.4103/jpi.jpi_67_20.

McCaffrey, Peter, Ronald Jackups, Jansen Seheult, Mark A. Zaydman, Ulysses Balis, Harshwardhan M. Thaker, Hooman Rashidi, and Rama R. Gullapalli. 2024. “Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward.” Archives of Pathology & Laboratory Medicine 149 (2): 130–41. https://doi.org/10.5858/arpa.2024-0208-ra.

Pantanowitz, Liron, Marilyn M. Bui, Chhavi Chauhan, Ehab ElGabry, Lewis Hassell, Zaibo Li, Anil V. Parwani, et al. 2022. “Rules of Engagement: Promoting Academic-Industry Partnership in the Era of Digital Pathology and Artificial Intelligence.” Academic Pathology 9 (1): 100026. https://doi.org/10.1016/j.acpath.2022.100026.

Şeker, Mesut, M. Khalid Khan Niazi, Wei Chen, Wendy L. Frankel, and Metin N. Gurcan. 2025. “Tumor Bud Classification in Colorectal Cancer Using Attention-Based Deep Multiple Instance Learning and Domain-Specific Foundation Models.” Cancers 17 (7): 1245. https://doi.org/10.3390/cancers17071245.

Singh, Rajendra, Ji Yeon Kim, Eric F. Glassy, Rajesh C. Dash, Victor Brodsky, Jansen Seheult, M. E. de Baca, Qiangqiang Gu, Shannon Hoekstra, and Bobbi S. Pritt. 2024. “Introduction to Generative Artificial Intelligence: Contextualizing the Future.” Archives of Pathology & Laboratory Medicine 149 (2): 112–22. https://doi.org/10.5858/arpa.2024-0221-ra.

Tsakiroglou, Anna Maria, Chris M Bacon, Daniel Shingleton, Gabrielle Slavin, Prokopios Vogiatzis, Richard Byers, Christopher Carey, and Martin Fergie. 2023. “Lymphoma Triage from H&E Using AI for Improved Clinical Management.” Journal of Clinical Pathology, November. https://doi.org/10.1136/jcp-2023-209186.

Wang, Andrew T, Saugato Rahman Dhruba, Kun Wang, Emma M Campagnolo, Eldad D Shulman, and Eytan Ruppin. 2025. “Deep Learning Inference of Cell Type-Specific Gene Expression from Breast Tumor Histopathology.” http://dx.doi.org/10.1101/2025.05.04.652089.

Zarella, Mark D., Douglas Bowman;, Famke Aeffner, Navid Farahani, Albert Xthona;, Syeda Fatima Absar, Anil Parwani, Marilyn Bui, and Douglas J. Hartman. 2018. “A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association.” Archives of Pathology & Laboratory Medicine 143 (2): 222–34. https://doi.org/10.5858/arpa.2018-0343-ra.

Zarella, Mark D., David S. McClintock, Harsh Batra, Rama R. Gullapalli, Michael Valante, Vivian O. Tan, Shubham Dayal, et al. 2023. “Artificial Intelligence and Digital Pathology: Clinical Promise and Deployment Considerations.” Journal of Medical Imaging 10 (05). https://doi.org/10.1117/1.jmi.10.5.051802.