Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center. (14th July 2021)

• Record Type: Journal Article
• Authors: 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; Manzo, Allyne; Paramasivam, Neeraj H G; Ziegler, John S; Gao, Jianjiong; Perin, Juan C; Kim, Young Suk; Bhanot, Umeshkumar K; Roehrl, Michael H A; Ardon, Orly; Chiang, Sarah; Giri, Dilip D; Sigel, Carlie S; Tan, Lee K; Murray, Melissa; Virgo, Christina; England, Christine; Yagi, Yukako; Sirintrapun, S Joseph; Klimstra, David; Hameed, Meera; Reuter, Victor E; Fuchs, Thomas J;
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 9(2021)
• Page Start: 1874
• Page End: 1884
• Publication Date: 2021-07-14
• Subjects: digital pathology -- whole slide imaging -- computational pathology -- artificial intelligence -- honest broker, pathology; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab085
• Languages: English
• ISSNs: 1067-5027

Abstract

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. 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. 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. 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.