An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities

An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities

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Background: Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case...

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Bibliographic Details
Main Authors: David J. Foran, Eric B. Durbin, Wenjin Chen, Evita Sadimin, Ashish Sharma, Imon Banerjee, Tahsin Kurc, Nan Li, Antoinette M. Stroup, Gerald Harris, Annie Gu, Maria Schymura, Rajarsi Gupta, Erich Bremer, Joseph Balsamo, Tammy DiPrima, Feiqiao Wang, Shahira Abousamra, Dimitris Samaras, Isaac Hands, Kevin Ward, Joel H. Saltz
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:Journal of Pathology Informatics
Subjects:
Cancer registries
Computational imaging
Deep-learning
Digital pathology
Online Access:http://www.sciencedirect.com/science/article/pii/S2153353922007611

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http://www.sciencedirect.com/science/article/pii/S2153353922007611

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