Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center.
J Am Med Inform Assoc
; 28(9): 1874-1884, 2021 08 13.
Article
em En
| MEDLINE
| ID: mdl-34260720
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 ANDMETHODS:
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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Patologia Clínica
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Informática Médica
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COVID-19
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Neoplasias
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
Limite:
Humans
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Male
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article