Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.

TitleBuilding Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.
Publication TypeJournal Article
Year of Publication2022
AuthorsRosenthal J, Carelli R, Omar M, Brundage D, Halbert E, Nyman J, Hari SN, Van Allen EM, Marchionni L, Umeton R, Loda M
JournalMol Cancer Res
Volume20
Issue2
Pagination202-206
Date Published2022 02
ISSN1557-3125
KeywordsArtificial Intelligence, Humans, Machine Learning, Neoplasms, Research Design
Abstract

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.

DOI10.1158/1541-7786.MCR-21-0665
Alternate JournalMol Cancer Res
PubMed ID34880124
Grant ListF31 CA250136 / CA / NCI NIH HHS / United States
P50 CA090381 / CA / NCI NIH HHS / United States
U01 CA231776 / CA / NCI NIH HHS / United States
P50 CA211024 / CA / NCI NIH HHS / United States
Related Faculty: 
Luigi Marchionni, M.D., Ph.D. Massimo Loda, M.D.

Pathology & Laboratory Medicine 1300 York Avenue New York, NY 10065 Phone: (212) 746-6464
Surgical Pathology: (212) 746-2700