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

• Published in: Journal of pathology informatics Vol. 13; no. 1; p. 5
• Main Authors: Foran, David J., Durbin, Eric B., Chen, Wenjin, Sadimin, Evita, Sharma, Ashish, Banerjee, Imon, Kurc, Tahsin, Li, Nan, Stroup, Antoinette M., Harris, Gerald, Gu, Annie, Schymura, Maria, Gupta, Rajarsi, Bremer, Erich, Balsamo, Joseph, DiPrima, Tammy, Wang, Feiqiao, Abousamra, Shahira, Samaras, Dimitris, Hands, Isaac, Ward, Kevin, Saltz, Joel H.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2022; Wolters Kluwer India Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Wolters Kluwer - Medknow; Elsevier
• Subjects: Algorithms; Automation; Cancer; Cancer registries; Computational imaging; Deep learning; Demographics; Digital imaging; Digital pathology; Digitization; Epidemiology; Image quality; Informatics; Lymphocytes; Machine learning; Medical imaging; Original; Pathology; Population (statistical); Repositories; Searching; Software; Tumors; United States > US; Cancer registries; Digital pathology; Deep-learning; Computational imaging; deep-learning; computational imaging; digital pathology

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 that is rep...