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

Oxford University Press (OUP) - Tập 28 Số 9 - Trang 1874-1884 - 2021
Peter J. Schüffler1,2, Luke Geneslaw1, Dig Vijay Kumar Yarlagadda1, Matthew G. Hanna1, Jennifer Samboy1, Evangelos Stamelos1, Chad Vanderbilt1, John Philip3,1, Marc-Henri Jean1, Lorraine Corsale1, Allyne Manzo1, Neeraj H G Paramasivam4, John Ziegler1, Jianjiong Gao5, Juan C. Perín4, Young Kim6, Umesh Bhanot1, Michael H. A. Roehrl1,7, Orly Ardon1, Sarah Chiang1, Dilip D. Giri1, Carlie Sigel1, Lee K. Tan1, Melissa P. Murray1, Christina Virgo1, Christine England1, Yukako Yagi1, S. Joseph Sirintrapun1, David S. Klimstra1, Meera Hameed1, Victor E. Reuter1, Thomas J. Fuchs8,1
1Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
2Institute of Pathology, Technical University of Munich, Munich, Germany
3Department of Health Informatics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
4Department of Information Systems, Memorial Sloan Kettering Cancer Center, New York, New York, USA
5Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
6School of Medicine, Stanford University, Stanford, California, USA
7Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
8Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

Tóm tắt

AbstractObjectiveBroad 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 MethodsWe 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.ResultsThe 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.ConclusionsWe 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.

Từ khóa


Tài liệu tham khảo

Cheng, 2016, Enabling digital pathology in the diagnostic setting: navigating through the implementation journey in an academic medical centre, J Clin Pathol, 69, 784, 10.1136/jclinpath-2015-203600

Stathonikos, 2019, Being fully digital: perspective of a Dutch academic pathology laboratory, Histopathology, 75, 621, 10.1111/his.13953

Evans, 2017, Implementation of whole slide imaging for clinical purposes: issues to consider from the perspective of early adopters, Arch Pathol Lab Med, 141, 944, 10.5858/arpa.2016-0074-OA

Wilbur, 2009, Whole-slide imaging digital pathology as a platform for teleconsultation: a pilot study using paired subspecialist correlations, Arch Pathol Lab Med, 133, 1949, 10.5858/133.12.1949

Têtu, 2014, The Eastern Québec Telepathology Network: a three-year experience of clinical diagnostic services, Diagn Pathol, 9 Suppl 1

Chong, 2019, The California Telepathology Service: UCLA’s experience in deploying a regional digital pathology subspecialty consultation network, J Pathol Inform, 10, 31, 10.4103/jpi.jpi_22_19

Mpunga, 2016, Implementation and validation of telepathology triage at cancer referral center in rural Rwanda, J Glob Oncol, 2, 76, 10.1200/JGO.2015.002162

Pantanowitz, 2012, Experience with multimodality telepathology at the University of Pittsburgh Medical Center, J Pathol Inform, 3, 45, 10.4103/2153-3539.104907

Vodovnik, 2015, Distance reporting in digital pathology: a study on 950 cases, J Pathol Inform, 6, 18, 10.4103/2153-3539.156168

Chen, 2014, A nationwide telepathology consultation and quality control program in China: implementation and result analysis, Diagn Pathol, 9 (Suppl 1, S2, 10.1186/1746-1596-9-S1-S2

Sirintrapun, 2017, Robotic telecytology for remote cytologic evaluation without an on-site cytotechnologist or cytopathologist: a tale of implementation and review of constraints, J Pathol Inform, 8, 32, 10.4103/jpi.jpi_26_17

Pantanowitz, 2010, Digital images and the future of digital pathology, J Pathol Inform, 1, 15, 10.4103/2153-3539.68332

Williams, 2017, Future-proofing pathology: the case for clinical adoption of digital pathology, J Clin Pathol, 70, 1010, 10.1136/jclinpath-2017-204644

Pantanowitz, 2013, Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center, Arch Pathol Lab Med, 137, 1710, 10.5858/arpa.2013-0093-CP

Yagi, 2005, Digital imaging in pathology: the case for standardization, J Telemed Telecare, 11, 109, 10.1258/1357633053688705

Borowsky, 2020, Digital whole slide imaging compared with light microscopy for primary diagnosis in surgical pathology, Arch Pathol Lab Med, 144, 1245, 10.5858/arpa.2019-0569-OA

Mukhopadhyay, 2018, Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (Pivotal Study), Am J Surg Pathol, 42, 39, 10.1097/PAS.0000000000000948

Al-Janabi, 2012, Whole slide images as a platform for initial diagnostics in histopathology in a medium-sized routine laboratory, J Clin Pathol, 65, 1107, 10.1136/jclinpath-2012-200878

Alassiri, 2020, Whole slide imaging compared with light microscopy for primary diagnosis in surgical neuropathology: a validation study, Ann Saudi Med, 40, 36, 10.5144/0256-4947.2020.36

Eccher, 2020, Current state of whole slide imaging use in cytopathology: Pros and pitfalls, Cytopathology, 31, 372, 10.1111/cyt.12806

Girolami, 2020, Diagnostic concordance between whole slide imaging and conventional light microscopy in cytopathology: a systematic review, Cancer Cytopathol, 128, 17, 10.1002/cncy.22195

Amin, 2019, A validation study of whole slide imaging for primary diagnosis of lymphoma, Pathol Int, 69, 341, 10.1111/pin.12808

Cima, 2018, Validation of remote digital frozen sections for cancer and transplant intraoperative services, J Pathol Inform, 9, 34, 10.4103/jpi.jpi_52_18

Bauer, 2013, Validation of whole slide imaging for primary diagnosis in surgical pathology, Arch Pathol Lab Med, 137, 518, 10.5858/arpa.2011-0678-OA

Brunelli, 2014, iPathology cockpit diagnostic station: validation according to College of American Pathologists Pathology and Laboratory Quality Center recommendation at the Hospital Trust and University of Verona, Diagn Pathol, 9, S12, 10.1186/1746-1596-9-S1-S12

Buck, 2014, Validation of a whole slide imaging system for primary diagnosis in surgical pathology: a community hospital experience, J Pathol Inform, 5, 43, 10.4103/2153-3539.145731

Campbell, 2012, Concordance between whole-slide imaging and light microscopy for routine surgical pathology, Hum Pathol, 43, 1739, 10.1016/j.humpath.2011.12.023

Gilbertson, 2006, Primary histologic diagnosis using automated whole slide imaging: a validation study, BMC Clin Pathol, 6, 4, 10.1186/1472-6890-6-4

Goacher, 2017, The diagnostic concordance of whole slide imaging and light microscopy: a systematic review, Arch Pathol Lab Med, 141, 151, 10.5858/arpa.2016-0025-RA

Houghton, 2014, Concordance between digital pathology and light microscopy in general surgical pathology: a pilot study of 100 cases, J Clin Pathol, 67, 1052, 10.1136/jclinpath-2014-202491

Jukić, 2011, Clinical examination and validation of primary diagnosis in anatomic pathology using whole slide digital images, Arch Pathol Lab Med, 135, 372, 10.5858/2009-0678-OA.1

Snead, 2016, Validation of digital pathology imaging for primary histopathological diagnosis, Histopathology, 68, 1063, 10.1111/his.12879

Tabata, 2017, Whole-slide imaging at primary pathological diagnosis: Validation of whole-slide imaging-based primary pathological diagnosis at twelve Japanese academic institutes, Pathol Int, 67, 547, 10.1111/pin.12590

Reyes, 2014, Intra-observer reproducibility of whole slide imaging for the primary diagnosis of breast needle biopsies, J Pathol Inform, 5, 5, 10.4103/2153-3539.127814

Elmore, 2017, A randomized study comparing digital imaging to traditional glass slide microscopy for breast biopsy and cancer diagnosis, J Pathol Inform, 8, 12, 10.4103/2153-3539.201920

Vodovnik, 2018, Complete routine remote digital pathology services, J Pathol Inform, 9, 36, 10.4103/jpi.jpi_34_18

Retamero, 2020, Complete digital pathology for routine histopathology diagnosis in a multicenter hospital network, Arch Pathol Lab Med, 144, 221, 10.5858/arpa.2018-0541-OA

Chang, 2019, Artificial intelligence in pathology, J Pathol Transl Med, 53, 1, 10.4132/jptm.2018.12.16

Bera, 2019, Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology, Nat Rev Clin Oncol, 16, 703, 10.1038/s41571-019-0252-y

Niazi, 2019, Digital pathology and artificial intelligence, Lancet Oncol, 20, e253–61, 10.1016/S1470-2045(19)30154-8

Lu, 2020, A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study, Lancet Digit Health, 2, e594–606

Pantanowitz, 2020, An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study, Lancet Digit Health, 2, e407–16

Echle, 2021, Deep learning in cancer pathology: a new generation of clinical biomarkers, Br J Cancer, 124, 686, 10.1038/s41416-020-01122-x

Kalra, 2020, Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence, NPJ Digit Med, 3, 31, 10.1038/s41746-020-0238-2

Campanella, 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat Med, 25, 1301, 10.1038/s41591-019-0508-1

He, 2020, Integrating spatial gene expression and breast tumour morphology via deep learning, Nat Biomed Eng, 4, 827, 10.1038/s41551-020-0578-x

Liu, 2020, Predict Ki-67 positive cells in H&E-stained images using deep learning independently from IHC-stained images, Front Mol Biosci, 7, 10.3389/fmolb.2020.00183

Hanna, 2019, Implementation of digital pathology offers clinical and operational increase in efficiency and cost savings, Arch Pathol Lab Med, 143, 1545, 10.5858/arpa.2018-0514-OA

Gupta, 2004, Evaluation of a Deidentification (De-Id) software engine to share pathology reports and clinical documents for research, Am J Clin Pathol, 121, 176, 10.1309/E6K33GBPE5C27FYU

Gilbert, 2020

Sellaro, 2013, Relationship between magnification and resolution in digital pathology systems, J Pathol Inform, 4, 21, 10.4103/2153-3539.116866

Hanna, 2019, Whole slide imaging equivalency and efficiency study: experience at a large academic center, Mod Pathol, 32, 916, 10.1038/s41379-019-0205-0

Hanna, 2020, Validation of a digital pathology system including remote review during the COVID-19 pandemic, Mod Pathol, 33, 2115, 10.1038/s41379-020-0601-5

Cerami, 2012, The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data, Cancer Discov, 2, 401, 10.1158/2159-8290.CD-12-0095

Gao, 2013, Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal, Sci Signal, 6, pl1, 10.1126/scisignal.2004088

Cheng, 2015, Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), J Mol Diagn, 17, 251, 10.1016/j.jmoldx.2014.12.006

Ho, 2020, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 540, 10.1007/978-3-030-59722-1_52

Schaumberg, 2017, DeepScope: nonintrusive whole slide saliency annotation and prediction from pathologists at the microscope, Comput Intell Methods Bioinforma Biostat, 10477, 42

Schüffler, 2016, Proceedings of the 1st Machine Learning for Healthcare Conference, 191

Muhammad

Grabenstetter, 2019, Accuracy of intraoperative frozen section of sentinel lymph nodes after neoadjuvant chemotherapy for breast carcinoma, Am J Surg Pathol, 43, 1377, 10.1097/PAS.0000000000001311

Campanella, 2018, Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology, Comput Med Imaging Graph, 65, 142, 10.1016/j.compmedimag.2017.09.001

Schüffler, 2021, Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images, J Pathol Inform, 12, 9, 10.4103/jpi.jpi_85_20

Xie, 2019, VOCA: cell nuclei detection in histopathology images by vector oriented confidence accumulation, Proc Mach Learn Res, 527

Ho

Goode, 2013, OpenSlide: a vendor-neutral software foundation for digital pathology, J Pathol Inform, 27

Thông tin
Thông tin xuất bản

Oxford University Press (OUP)

Tập 28 Số 9

1874-1884

Thông tin tác giả