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Sökning: id:"swepub:oai:lup.lub.lu.se:f857aedf-2b61-4237-b41d-b6b2bc064e1d" > Development of Trai...

  • Garcia, VictorUnited States Food and Drug Administration (författare)

Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer

  • Artikel/kapitelEngelska2022

Förlag, utgivningsår, omfång ...

  • 2022-05-17
  • MDPI AG,2022

Nummerbeteckningar

  • LIBRIS-ID:oai:lup.lub.lu.se:f857aedf-2b61-4237-b41d-b6b2bc064e1d
  • https://lup.lub.lu.se/record/f857aedf-2b61-4237-b41d-b6b2bc064e1dURI
  • https://doi.org/10.3390/cancers14102467DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:art swepub-publicationtype
  • Ämneskategori:ref swepub-contenttype

Anmärkningar

  • The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Elfer, KatherineUnited States Food and Drug Administration (författare)
  • Peeters, Dieter J.E.Sint-Maarten Hospital (författare)
  • 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 (författare)
  • Werness, BruceInova Health System (författare)
  • Ly, AmyMassachusetts General Hospital (författare)
  • Li, XiaoxianEmory University (författare)
  • Hanna, Matthew G.Memorial Sloan-Kettering Cancer Center (författare)
  • Blenman, Kim R.M.Yale University (författare)
  • Salgado, RobertoPeter MacCallum Cancer Centre (författare)
  • Gallas, Brandon D.United States Food and Drug Administration (författare)
  • United States Food and Drug AdministrationSint-Maarten Hospital (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Cancers: MDPI AG14:10, s. 1-142072-6694

Internetlänk

Hitta via bibliotek

  • Cancers (Sök värdpublikationen i LIBRIS)

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