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Pilot study to eval...
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Elfer, KatherineUnited States Food and Drug Administration
(author)
Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms
- Article/chapterEnglish2022
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LIBRIS-ID:oai:lup.lub.lu.se:4eac308b-8e3d-4754-a8fd-8fe072b34b4b
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https://lup.lub.lu.se/record/4eac308b-8e3d-4754-a8fd-8fe072b34b4bURI
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https://doi.org/10.1117/1.JMI.9.4.047501DOI
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Language:English
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Summary in:English
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Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.
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Dudgeon, SarahYale University
(author)
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Garcia, VictorUnited States Food and Drug Administration
(author)
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Blenman, KimYale University
(author)
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Hytopoulos, EvangelosiRhythm Technologies Inc.
(author)
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Wen, SiUnited States Food and Drug Administration
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Li, XiaoxianEmory University
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Ly, AmyMassachusetts General Hospital
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Werness, BruceInova Health System
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Sheth, Manasi SUnited States Food and Drug Administration
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Amgad, Mohamed
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Gupta, Rajarsi
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Saltz, Joel
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Hanna, Matthew G
(author)
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Ehinger, AnnaLund University,Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Patologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Pathology, Lund,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Region Skåne(Swepub:lu)pat-amm
(author)
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Peeters, Dieter
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Salgado, Roberto
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Gallas, Brandon D
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United States Food and Drug AdministrationYale University
(creator_code:org_t)
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In:Journal of Medical Imaging9:4, s. 1-142329-4302
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Elfer, Katherine
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Dudgeon, Sarah
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Garcia, Victor
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Blenman, Kim
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Hytopoulos, Evan ...
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Wen, Si
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Li, Xiaoxian
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Ly, Amy
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Werness, Bruce
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Sheth, Manasi S
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Amgad, Mohamed
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Gupta, Rajarsi
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Saltz, Joel
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Hanna, Matthew G
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Ehinger, Anna
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Peeters, Dieter
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Salgado, Roberto
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Gallas, Brandon ...
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Lund University