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

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

Garcia, Victor (author): United States Food and Drug Administration

Elfer, Katherine (author): United States Food and Drug Administration

Peeters, Dieter J.E. (author): Sint-Maarten Hospital

Ehinger, Anna (author): Lund 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

Werness, Bruce (author): Inova Health System

Ly, Amy (author): Massachusetts General Hospital

Li, Xiaoxian (author): Emory University

Hanna, Matthew G. (author): Memorial Sloan-Kettering Cancer Center

Blenman, Kim R.M. (author): Yale University

Salgado, Roberto (author): Peter MacCallum Cancer Centre

Gallas, Brandon D. (author): United States Food and Drug Administration

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(creator_code:org_t)

2022-05-17
2022
English.
In: Cancers. - : MDPI AG. - 2072-6694. ; 14:10, s. 1-14

Related links:: http://dx.doi.org/10... (free); show more...; https://lup.lub.lu.s...; https://doi.org/10.3...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Keyword

biomarker
expert panel
pathologist training/education
tumor-infiltrating lymphocytes
validation dataset

Publication and Content Type

art (subject category)
ref (subject category)

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By the author/editor: Garcia, Victor; Elfer, Katherine; Peeters, Dieter ...; Ehinger, Anna; Werness, Bruce; Ly, Amy; show more...; Li, Xiaoxian; Hanna, Matthew G ...; Blenman, Kim R.M ...; Salgado, Roberto; Gallas, Brandon ...; show less...

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MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Cancer and Oncol ...

Articles in the publication: Cancers

By the university: Lund University

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