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Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center.

Authors :
Schüffler PJ
Geneslaw L
Yarlagadda DVK
Hanna MG
Samboy J
Stamelos E
Vanderbilt C
Philip J
Jean MH
Corsale L
Manzo A
Paramasivam NHG
Ziegler JS
Gao J
Perin JC
Kim YS
Bhanot UK
Roehrl MHA
Ardon O
Chiang S
Giri DD
Sigel CS
Tan LK
Murray M
Virgo C
England C
Yagi Y
Sirintrapun SJ
Klimstra D
Hameed M
Reuter VE
Fuchs TJ
Source :
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2021 Aug 13; Vol. 28 (9), pp. 1874-1884.
Publication Year :
2021

Abstract

Objective: Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.<br />Materials and Methods: We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.<br />Results: The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence-driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.<br />Conclusions: We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.<br /> (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Details

Language :
English
ISSN :
1527-974X
Volume :
28
Issue :
9
Database :
MEDLINE
Journal :
Journal of the American Medical Informatics Association : JAMIA
Publication Type :
Academic Journal
Accession number :
34260720
Full Text :
https://doi.org/10.1093/jamia/ocab085