The Warren Alpert Center for Digital and Computational Pathology at MSK

Department of Pathology and Laboratory Medicine
Our Expertise
Our Providers
Pathology Consultations
Fellowships
Fine Needle Aspiration Biopsy Clinic
The Fred W. Stewart Award
The William L. Gerald Award
The Warren Alpert Center for Digital and Computational Pathology at MSK
Center for Laboratory Medicine
The MSK Pathology & Laboratory Medicine Review ​
Last Wish Program

The Warren Alpert Center for Digital and Computational Pathology at MSK

Share
Share
The Warren Alpert Center at Memorial Sloan Kettering

The Warren Alpert Center for Digital and Computational Pathology was established in 2017 as an innovation center for novel research and development in pathology digital imaging and algorithmic computational pathology for clinical cancer care and research. The support from the Warren Alpert Foundation has positioned Memorial Sloan Kettering Cancer Center (MSK) as a major center for pathology digital imaging and computational pathology, providing a key component to MSK’s institutional focus on precision medicine. It also serves as a hub for existing digital pathology efforts to establish a fully digital workflow in MSK’s Department of Pathology and Laboratory Medicine.

Completely dedicated to cancer diagnosis and research, MSK’s Department of Pathology and Laboratory Medicine is one of the largest in the world, with a massive archive of glass slides and pathology information dating back more than 40 years. The department’s faculty consists of world-renowned, subspecialized anatomic and clinical pathologists and researchers with expertise in all cancer pathology. 

The digital pathology infrastructure underpins clinical, educational, and research endeavors in the Department of Pathology and Laboratory Medicine. This infrastructure is composed of three integral components: people, processes, and technology. Through comprehensive staff onboarding, specialized training, and continuous adaptation, this core provides support for the initiatives realized by The Warren Alpert Center. Innovative technologies have been developed to revolutionize conventional workflows, thereby positioning the core infrastructure as a driving force behind advancements in digital and computational pathology. 

The Warren Alpert Center combines these existing synergies and builds the resources critical for new developments in pathology. The overarching goal of The Warren Alpert Center is to lead the way in transforming pathology from a subjective, qualitative discipline to an objective, quantitative one, pioneering computer-augmented cancer diagnosis and building artificial intelligence (AI) analytics for pathology. The technology will further enrich our knowledge of disease by providing opportunities and integrating computational data from pathology slides with other specimen-related data (genomics, proteomics, radiographic imaging, etc.) to bring an unprecedented breadth and depth of information to each individual case, yielding a comprehensive, multidimensional analysis that would otherwise be impossible.

Leadership and Steering Committee Members

Kojo Elenitoba-Johnson, MD
Steering Committee Member, The Warren Alpert Center for Digital and Computational Pathology

Chair, Department of Pathology and Laboratory Medicine

Meera Hameed, MD
Co-Director and Steering Committee Member, The Warren Alpert Center for Digital and Computational Pathology

Chief, Surgical Pathology Service

Ahmet Dogan, MD, PhD
Co-Director and Steering Committee Member, The Warren Alpert Center for Digital and Computational Pathology

Chief, Hematopathology Service

Victor E. Reuter, MD
Steering Committee Member, The Warren Alpert Center for Digital and Computational Pathology

Team Leader, Genitourinary Service, Department of Pathology and Laboratory Medicine

Orly Ardon, PhD, MBA
Steering Committee Member, The Warren Alpert Center for Digital and Computational Pathology

Director, Digital Pathology Operation, Memorial Sloan Kettering Cancer Center

Members and Collaborators - MSK

Activities at The Warren Alpert Center

The Warren Alpert Center for Digital and Computational Pathology relies on data acquired by fully digital workflows. The digital pathology core provides innovative imaging solutions that allow the downstream use of the digital images in multiple collaborative research projects. This, coupled with recent breakthroughs in machine learning and improvements in scanning technologies, have placed Memorial Sloan Kettering Cancer Center (MSK) at the forefront to build decision-support systems that use deep learning and artificial intelligence (AI) at an unprecedented scale.

Digital Pathology Infrastructure Development Core

Matthew G. Hanna, MD, (former member) was the Director of Digital Pathology Informatics in the Department of Pathology and Laboratory Medicine. He pioneered operationalization of whole slide scanners and digital tool development at MSK. He believes the essence of fully harnessing the potential of analog assets lies in employing the most suitable technology for each distinct facet of patient care. A notable example is the internally developed “HoBBiT,” a tool that acts as an honest broker to facilitate de-identification and transfer of pathology images and report data. This system has acted as a catalyst for the enterprise digital pathology transformation connecting the pathology laboratory information system and imaging database to collaborative research projects, genomic analysis through cBioPortal, clinical molecular pathology via MPATH, the tissue biorepository, and even an education portal.

Orly Ardon, PhD, MBA,  is the Director of Digital Pathology Operations in the Department of Pathology and Laboratory Medicine and develops the operational aspects related to imaging hardware and software associated with digital pathology. She currently leads the Digital Pathology Core and scanning operations. 

At the heart of the scanning operation, is the Digital Pathology Operation scan technicians who perform the different tasks that are required to produce high quality and reliable digital images. The technicians are trained internally on all technologies used and are also offered a Digital Pathology Certification course. Dr. Ardon also oversees the commercial aspects, quality improvement, and advancement of digital pathology diagnostics.  

Lung Cancer Biomarker Discovery and Exploration of Tumor Microenvironment with Deep Learning

Chad Vanderbilt, MD is an Assistant Attending Pathologist in the Department of Pathology and Laboratory Medicine with a clinical subspecialty in molecular diagnostics. Dr. Vanderbilt’s research focuses on computational biomarker discovery, both from genomic data and from computer vision, using deep learning methods working with hematoxylin and eosin (H&E) slides. Dr. Vanderbilt is specifically focused on improving the clinical workflow for the diagnosis and management of non-small cell lung cancer.  

Dr. Vanderbilt’s laboratory has a track record of publications in computer vision conferences and clinical journals that have been well received by the pathology community. These technical works form the foundation for models that are in the process of being implemented into clinical workflows. Beyond his active research projects, Dr. Vanderbilt teaches both trainees and junior pathology attendings deep learning techniques and how to use the vast resources available at MSK. The work is enabled by the generous funding by the Warren Alpert Foundation and the use of the best-in-class computational cluster dedicated to work in computational pathology.

Integrating Clinical Genome Sequencing and Digital Pathology from 100,000 Patients: MSK Mind/Computational Oncology

Sohrab Shah, PhD, is Chief of Computational Oncology in the Department of Epidemiology and Biostatistics. Dr. Shah’s research focuses on developing and using computational methods to understand cancer evolution and treatment response. At MSK, Dr. Shah is pursuing multimodal data integration of genomics and pathology imaging, high-resolution single-cell genomics, and transcriptomics to accelerate the next generation of diagnostics and predictive models for cancer patients. He initiated the MSK MIND (Multi-Modal Integration of Data) program aimed at advancing personalized medicine through AI-driven analysis of multimodal real-world patient data.

Publications

  1. Campanella, Gabriele; Kwan, Ricky; Fluder, Eugene; Zeng, Jennifer; Stock, Aryeh; Veremis, Brandon; Polydorides, Alexandros D; Hedvat, Cyrus; Schoenfeld, Adam; Vanderbilt, Chad; Computational Pathology at Health System Scale—Self-Supervised Foundation Models from Three Billion Images, arXiv preprint arXiv:2310.07033, Oct 2023\
  2. Ardon, O., Labasin, M, Friedlander, M., Manzo, A., Corsale, L., Ntiamoah, P., Wright, J., Elenitoba-Johnson, K., Reuter, V.E., Hameed, M., P., Hanna, M.G. (2023) Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center. https://doi.org/10.1016/j.labinv.2023.100246. (Accepted Laboratory Investigation Aug 28, 2023)  
  3. Ardon, O., Klein, E., Manzo, A., Corsale, L. England, C., Mazzella, A., Geneslaw, L., Philip, J., Ntiamoah, P., Wright, J., Sirintrapun, S.J., Lin, O., Elenitoba-Johnson, K., Reuter, V.E., Hameed, M., P., Hanna, M.G. (2023) Digital Pathology Operations at a Tertiary Cancer Center: Infrastructure Requirements and Operational Cost. https://doi.org/10.1016/j.jpi.2023.100318 (Accepted JPI May 12 2023)
  4. Ho, David Joon; Chui, M Herman; Vanderbilt, Chad M; Jung, Jiwon; Robson, Mark E; Park, Chan-Sik; Roh, Jin; Fuchs, Thomas J; Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation, Journal of Pathology Informatics, vol. 14, 100160, 2023, Elsevier
  5. Hanna, M.G., Ardon, O. Digital pathology systems enabling quality patient care. Genes, Chromosomes and Cancer. 2023. doi:10.1002/gcc.23192
  6. Schüffler, P.J., Stamelos, E., Ahmed, E., Yarlagadda, D.V.G., Ardon, O., Hanna, M.G., Reuter, V.E., Klimstra, D.S., Hameed, M. (2022) Efficient visualization of whole slide images in web-based viewers for digital pathology. Arch Pathol Lab Med. 46(10):273–1280. doi: 10.5858/arpa.2021-0197-OA
  7. Campanella, Gabriele; Ho, David; Häggström, Ida; Becker, Anton S; Chang, Jason; Vanderbilt, Chad; Fuchs, Thomas J; H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma, arXiv preprint arXiv:2206.10573, June 2022
  8. Hanna, M.G., Ardon, O., Reuter, V.E., Sirintrapun, S.J., England, C., Klimstra, D., Hameed, M. (2022) Integrating digital pathology into clinical practice. Modern Pathology 35:152 – 164. doi 10.1038/s41379-021-00929-0
  9. Schüffler, P.J., Geneslaw, L., Yarlagadda, D.V.K., Hanna, M.G., Samboy, J., Stamelos, E., Vanderbilt, C., Philip, J., Jean, M.H., Corsale, L., Manzo, A., Paramasivam, N.H.G., Ziegler, J.S., Gao, J., Perin, J.C., Kim, Y.S., Bhanot, U.K., Roehrl, M.H.A., Ardon, O., Chiang, S., Giri DD., Sigel, C.S., Tan, L.K., Murray, M., Virgo, C., England, C., Yagi, Y., Sirintrapun, S.J., Klimstra, D., Hameed, M, Reuter, VE, Fuchs, TJ. (2021) Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center. J Am Med Inform Assoc 28(9):1874-1884. doi: 10.1093/jamia/ocab085.
  10. Ardon, O., Reuter, V.E., Hameed, M., Corsale, L. Manzo, A., Sirintrapun, S.J., Ntiamoah, P., Schüffler, P.J., England, C., Klimstra D.S., Hanna, M.G. (2021) Digital Pathology Operations at a NYC Tertiary Cancer Center During the First 4 Months of COVID-19 Pandemic Response. Acad. Pathol.8: January 2021 doi:10.1177/23742895211010276
  11. Lujan, G., Quigley, J.C., Hartman, D., Parwani, A., Roehmholdt, B., Van Meter, B., Ardon, O., Hanna, M.G., Kelly, D., Sowards,C., Montalto, M., Bui, M., Zarella, M.D., Slootweg, G., Retamero, J.A., Lloyd, M.C., Madory, J., Bowman, D. (2021) Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J. Pathol. Inform. 12:17 doi: 10.4103/jpi.jpi_67_20
  12. Matthew G Hanna, Niels H Olson, Mark Zarella, Rajesh C Dash, Markus D Herrmann, Larissa V. Furtado, Michelle N Stram, Patricia M Raciti, Lewis Hassell, Alex Mays, Liron Pantanowitz, Joseph S. Sirintrapun, Savitri Krishnamurthy, Anil Parwani, Giovanni Lujan, Andrew Evans, Eric F Glassy, Marilyn M Bui, Rajendra Singh, Rhona J Souers, Monica E de Baca, Jansen N Seheult. Recommendations for performance evaluation of machine learning in pathology: A Concept Paper From the College of American Pathologists. In Press.  
  13. Katherine Elfer, Sarah Dudgeon, Victor Garcia, Kim Blenman, Evangelos Hytopoulos, Si Wen, Xiaoxian Li, Amy Ly, Bruce Werness, Manasi S Sheth, Mohamed Amgad, Rajarsi Gupta, Joel Saltz, Matthew G Hanna, Anna Ehinger, Dieter Peeters, Roberto Salgado, Brandon D Gallas. Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms. J Med Imaging (Bellingham). 2022 Jul;9(4):047501.
  14. Feng, Chao; Ho, David Joon; Chui, Michael Herman; Campanella, Gabriele; Xie, Chensu; Vanderbilt, Chad; Fuchs, Thomas J; Toward Spatial and Quantitative Modelling of Tumor Microenvironment in Whole Slide Histopathology Images, 2022
  15. Victor Garcia, Katherine Elfer, Dieter J E Peeters, Anna Ehinger, Bruce Werness, Amy Ly, Xiaoxian Li, Matthew G Hanna, Kim R M Blenman, Roberto Salgado, Brandon D Gallas. Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer. Cancers (Basel). 2022 May 17;14(10):2467.
  16. Xie, Chensu; Vanderbilt, Chad; Feng, Chao; Ho, David; Campanella, Gabrielle; Egger, Jacklynn; Plodkowski, Andrew; Girshman, Jeffrey; Sawan, Peter; Arbour, Kathryn; Computational biomarker predicts lung ICI response via deep learning-driven hierarchical spatial modelling from H&E, 2022
  17. Matthew G Hanna¸ Maria H Hanna. Current applications and challenges of artificial intelligence in pathology. Human Pathology Reports. 2022 Mar;27:300596.
  18. Ho, David J; Agaram, Narasimhan P; Jean, Marc-Henri; Suser, Stephanie D; Chu, Cynthia; Vanderbilt, Chad M; Meyers, Paul A; Wexler, Leonard H; Healey, John H; Fuchs, Thomas J; Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction, The American Journal of Pathology, 193 3 341-349 2023  
  19. Peter J Schüffler, Evangelos Stamelos, Ishtiaque Ahmed, D Vijay K Yarlagadda, Matthew G Hanna, Victor E Reuter, David S Klimstra, Meera Hameed. Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology. Arch Pathol Lab Med. 2022 Jan 3.
  20. Ho, David; Agaram, Narasimhan; Jean, Marc-Henri; Vanderbilt, Chad; Healey, John; Meyers, Paul; Fuchs, Thomas; Hameed, Meera; Osteosarcoma Patient Stratification Based on Objective and Reproducible Post-Therapy Necrosis Assessment by Pixel-wise Deep Segmentation, LABORATORY INVESTIGATION, vol.102, SUPPL 1, 1075-1076, 2022, SPRINGERNATURE CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
  21. Kevin M Boehm, Emily A Aherne, Lora Ellenson, Ines Nikolovski, Mohammed Alghamdi, Ignacio Vázquez-García, Dmitriy Zamarin, Kara Long Roche, Ying Liu, Druv Patel, Andrew Aukerman , Arfath Pasha, Doori Rose, Pier Selenica, Pamela I Causa Andrieu, Chris Fong, Marinela Capanu, Jorge S Reis-Filho, Rami Vanguri, Harini Veeraraghavan, Natalie Gangai, Ramon Sosa, Samantha Leung, Andrew McPherson, JianJiong Gao; MSK MIND Consortium; Yulia Lakhman, Sohrab P Shah; Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer, Nat Cancer, 2022 Jun;3(6):723-733.doi:10.1038/s43018-022-00388-9. Epub 2002 Jun 28.
  22. Rami S Vanguri, Jia Luo, Andrew T Aukerman, Jacklynn V Egger, Christopher J Fong, Natally Horvat, Andrew Pagano, Jose de Arimateia Batista Araujo-Filho, Luke Geneslaw, Hira Rizvi, Ramon Sosa, Kevin M Boehm, Soo-Ryum Yang, Francis M Bodd, Katia Ventura, Travis J Hollmann, Michelle S Ginsberg, Jianjiong Gao; MSK MIND Consortium; Matthew D Hellmann, Jennifer L Sauter, Sohrab P Shah, Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer, Nat Cancer. 2022 Oct;3(10):1151-1164. doi: 10.1038/s43018-022-00416-8. Epub 2022 Aug 29.
  23. Ignacio Vázquez-García , Florian Uhlitz, Nicholas Ceglia, Jamie L P Lim, Michelle Wu, Neeman Mohibullah, Juliana Niyazov, Arvin Eric B Ruiz, Kevin M Boehm , Viktoria Bojilova, Christopher J Fong, Tyler Funnell, Diljot Grewal, Eliyahu Havasov, Samantha Leung, Arfath Pasha, Druv M Patel, Maryam Pourmaleki, Nicole Rusk, Hongyu Shi, Rami Vanguri, Marc J Williams, Allen W Zhang, Vance Broach , Dennis S Chi, Arnaud Da Cruz Paula, Ginger J Gardner, Sarah H Kim , Matthew Lennon , Kara Long Roche, Yukio Sonoda, Oliver Zivanovic, Ritika Kundra, Agnes Viale, Fatemeh N Derakhshan, Luke Geneslaw, Shirin Issa Bhaloo, Ana Maroldi , Rahelly Nunez, Fresia Pareja, Anthe Stylianou, Mahsa Vahdatinia, Yonina Bykov, Rachel N Grisham, Ying L Liu, Yulia Lakhman, Ines Nikolovski, Daniel Kelly, Jianjiong Gao, Andrea Schietinger, Travis J Hollmann, Samuel F Bakhoum , Robert A Soslow, Lora H Ellenson, Nadeem R Abu-Rustum , Carol Aghajanian  Claire F Friedman , Andrew McPherson , Britta Weigelt, Dmitriy Zamarin, Sohrab P Shah, Ovarian cancer mutational processes drive site-specific immune evasion, 2022 Dec;612(7941):778-786. doi: 10.1038/s41586-022-05496-1. Epub 2022 Dec 14.
  24. Ignacio Vázquez-García , Florian Uhlitz, Nicholas Ceglia, Jamie L P Lim, Michelle Wu, Neeman Mohibullah, Juliana Niyazov, Arvin Eric B Ruiz, Kevin M Boehm , Viktoria Bojilova, Christopher J Fong, Tyler Funnell, Diljot Grewal, Eliyahu Havasov, Samantha Leung, Arfath Pasha, Druv M Patel, Maryam Pourmaleki, Nicole Rusk, Hongyu Shi, Rami Vanguri, Marc J Williams, Allen W Zhang, Vance Broach , Dennis S Chi, Arnaud Da Cruz Paula, Ginger J Gardner, Sarah H Kim , Matthew Lennon , Kara Long Roche, Yukio Sonoda, Oliver Zivanovic, Ritika Kundra, Agnes Viale, Fatemeh N Derakhshan, Luke Geneslaw, Shirin Issa Bhaloo, Ana Maroldi , Rahelly Nunez, Fresia Pareja, Anthe Stylianou, Mahsa Vahdatinia, Yonina Bykov, Rachel N Grisham, Ying L Liu, Yulia Lakhman, Ines Nikolovski, Daniel Kelly, Jianjiong Gao, Andrea Schietinger, Travis J Hollmann, Samuel F Bakhoum , Robert A Soslow, Lora H Ellenson, Nadeem R Abu-Rustum , Carol Aghajanian  Claire F Friedman , Andrew McPherson , Britta Weigelt, Dmitriy Zamarin, Sohrab P Shah, Ovarian cancer mutational processes drive site-specific immune evasion, 2022 Dec;612(7941):778-786. doi: 10.1038/s41586-022-05496-1. Epub 2022 Dec 14.
  25. Ho, David J; D’Alfonso, Timothy; Tan, Lee; Agaram, Narasimhan; Hameed, Meera; Chang, Jason; Vanderbilt, Chad; Travis, William; Fuchs, Thomas; Deep learning-based whole slide image segmentation for efficient and reproducible assistance in pathology, Journal of Pathology Informatics, vol. 13, 100022, 2022
  26. Kevin M Boehm, Pegah Khosravi, Rami Vanguri, Jianjiong Gao, Sohrab P Shah; Harnessing multimodal data integration to advance precision oncology; 2022 Feb;22(2):114-126. doi: 10.1038/s41568-021-00408-3. Epub 2021 Oct 18.
  27. Sarah N Dudgeon, Si Wen, Matthew G Hanna, Rajarsi Gupta, Mohamed Amgad, Manasi Sheth, Hetal Marble, Richard Huang, Markus D Herrmann, Clifford H Szu, Darick Tong, Bruce Werness, Evan Szu, Denis Larsimont, Anant Madabhushi, Evangelos Hytopoulos, Weijie Chen, Rajendra Singh, Steven N Hart, Ashish Sharma, Joel Saltz, Roberto Salgado, Brandon D Gallas. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. J Pathol Inform. 2021 Nov 15;12:45.  
  28. James H. Harrison Jr, John Gilbertson, Matthew G. Hanna, Niels H. Olson, Jansen N. Seheult, James M. Sorace, Michelle Stram. Introduction to Artificial Intelligence and Machine Learning for Pathology. Archives of Pathology & Laboratory Medicine. 2021 Jan 25.  
  29. Peter J. Schüffler, Luke Geneslaw, D. Vijay K. Yarlagadda, Matthew G. Hanna, Jennifer Samboy, Evangelos Stamelos, Chad Vanderbilt, John Philip, Marc-Henri Jean, Lorraine Corsale, Allyne Manzo, Neeraj H. G. Paramasivam, John S. Ziegler, Jianjiong Gao, Juan C. Perin, Young Suk Kim, Umeshkumar K. Bhanot, Michael H. A. Roehrl, Sarah Chiang, Dilip D. Giri, Carlie S. Sigel, Lee K. Tan, Melissa Murray, Christina Virgo, Christine England, Yukako Yagi, S. Joseph Sirintrapun, David Klimstra, Meera Hameed, Victor E. Reuter, Thomas J. Fuchs.  Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center. Journal of the American Medical Informatics Association. 2021 Aug 13;28(9):1874-1884.
  30. Schüffler, Peter J; Geneslaw, Luke; Yarlagadda, D Vijay K; Hanna, Matthew G; Samboy, Jennifer; Stamelos, Evangelos; Vanderbilt, Chad; Philip, John; Jean, Marc-Henri; Corsale, Lorraine; Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center, Journal of the American Medical Informatics Association vol. 28, 9, 1874-1884, 2021, Oxford University Press
  31. Feng, Chao; Vanderbilt, Chad; Fuchs, Thomas; Nuc2vec: Learning representations of nuclei in histopathology images with contrastive loss; Medical Imaging with Deep Learning, 179 – 189, 2021

Contact

The Warren Alpert Center for Digital and Computational Pathology 
Memorial Sloan Kettering Cancer Center

1275 York Avenue,  
New York, NY 10065 

[email protected] 

Catherine Kinlock-Brooks
Project Manager
[email protected]