Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.


Journal

The American journal of pathology
ISSN: 1525-2191
Titre abrégé: Am J Pathol
Pays: United States
ID NLM: 0370502

Informations de publication

Date de publication:
07 2020
Historique:
received: 25 12 2019
revised: 28 02 2020
accepted: 19 03 2020
pubmed: 12 4 2020
medline: 18 8 2020
entrez: 12 4 2020
Statut: ppublish

Résumé

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.

Identifiants

pubmed: 32277893
pii: S0002-9440(20)30188-7
doi: 10.1016/j.ajpath.2020.03.012
pmc: PMC7369575
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

1491-1504

Subventions

Organisme : NCI NIH HHS
ID : UG3 CA225021
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM009239
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA180924
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM008444
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA215109
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM011119
Pays : United States

Informations de copyright

Copyright © 2020 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

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Auteurs

Han Le (H)

Department of Computer Science, Stony Brook University, Stony Brook, New York. Electronic address: hdle@cs.stonybrook.edu.

Rajarsi Gupta (R)

Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York; Department of Pathology, Stony Brook University Hospital, Stony Brook, New York.

Le Hou (L)

Department of Computer Science, Stony Brook University, Stony Brook, New York.

Shahira Abousamra (S)

Department of Computer Science, Stony Brook University, Stony Brook, New York.

Danielle Fassler (D)

Department of Pathology, Stony Brook University Hospital, Stony Brook, New York.

Luke Torre-Healy (L)

Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York.

Richard A Moffitt (RA)

Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York; Department of Pathology, Stony Brook University Hospital, Stony Brook, New York.

Tahsin Kurc (T)

Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York.

Dimitris Samaras (D)

Department of Computer Science, Stony Brook University, Stony Brook, New York.

Rebecca Batiste (R)

Department of Pathology, Stony Brook University Hospital, Stony Brook, New York.

Tianhao Zhao (T)

Department of Pathology, Stony Brook University Hospital, Stony Brook, New York.

Arvind Rao (A)

Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan.

Alison L Van Dyke (AL)

Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.

Ashish Sharma (A)

Department of Biomedical Informatics, Emory University, Atlanta, Georgia.

Erich Bremer (E)

Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York.

Jonas S Almeida (JS)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.

Joel Saltz (J)

Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, New York.

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