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SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment

Purpose: The Breast Pathology Quantitative Biomarkers (BreastPathQ) Challenge was a Grand Challenge organized jointly by the international society for optics and photonics (SPIE), the American Association of Physicists in Medicine (AAPM), the U.S. National Cancer Institute (NCI), and the U.S. Food a...

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Autores principales: Petrick, Nicholas, Akbar, Shazia, Cha, Kenny H., Nofech-Mozes, Sharon, Sahiner, Berkman, Gavrielides, Marios A., Kalpathy-Cramer, Jayashree, Drukker, Karen, Martel, Anne L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107263/
https://www.ncbi.nlm.nih.gov/pubmed/33987451
http://dx.doi.org/10.1117/1.JMI.8.3.034501
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author Petrick, Nicholas
Akbar, Shazia
Cha, Kenny H.
Nofech-Mozes, Sharon
Sahiner, Berkman
Gavrielides, Marios A.
Kalpathy-Cramer, Jayashree
Drukker, Karen
Martel, Anne L.
author_facet Petrick, Nicholas
Akbar, Shazia
Cha, Kenny H.
Nofech-Mozes, Sharon
Sahiner, Berkman
Gavrielides, Marios A.
Kalpathy-Cramer, Jayashree
Drukker, Karen
Martel, Anne L.
author_sort Petrick, Nicholas
collection PubMed
description Purpose: The Breast Pathology Quantitative Biomarkers (BreastPathQ) Challenge was a Grand Challenge organized jointly by the international society for optics and photonics (SPIE), the American Association of Physicists in Medicine (AAPM), the U.S. National Cancer Institute (NCI), and the U.S. Food and Drug Administration (FDA). The task of the BreastPathQ Challenge was computerized estimation of tumor cellularity (TC) in breast cancer histology images following neoadjuvant treatment. Approach: A total of 39 teams developed, validated, and tested their TC estimation algorithms during the challenge. The training, validation, and testing sets consisted of 2394, 185, and 1119 image patches originating from 63, 6, and 27 scanned pathology slides from 33, 4, and 18 patients, respectively. The summary performance metric used for comparing and ranking algorithms was the average prediction probability concordance (PK) using scores from two pathologists as the TC reference standard. Results: Test PK performance ranged from 0.497 to 0.941 across the 100 submitted algorithms. The submitted algorithms generally performed well in estimating TC, with high-performing algorithms obtaining comparable results to the average interrater PK of 0.927 from the two pathologists providing the reference TC scores. Conclusions: The SPIE-AAPM-NCI BreastPathQ Challenge was a success, indicating that artificial intelligence/machine learning algorithms may be able to approach human performance for cellularity assessment and may have some utility in clinical practice for improving efficiency and reducing reader variability. The BreastPathQ Challenge can be accessed on the Grand Challenge website.
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spelling pubmed-81072632022-05-08 SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment Petrick, Nicholas Akbar, Shazia Cha, Kenny H. Nofech-Mozes, Sharon Sahiner, Berkman Gavrielides, Marios A. Kalpathy-Cramer, Jayashree Drukker, Karen Martel, Anne L. J Med Imaging (Bellingham) Computer-Aided Diagnosis Purpose: The Breast Pathology Quantitative Biomarkers (BreastPathQ) Challenge was a Grand Challenge organized jointly by the international society for optics and photonics (SPIE), the American Association of Physicists in Medicine (AAPM), the U.S. National Cancer Institute (NCI), and the U.S. Food and Drug Administration (FDA). The task of the BreastPathQ Challenge was computerized estimation of tumor cellularity (TC) in breast cancer histology images following neoadjuvant treatment. Approach: A total of 39 teams developed, validated, and tested their TC estimation algorithms during the challenge. The training, validation, and testing sets consisted of 2394, 185, and 1119 image patches originating from 63, 6, and 27 scanned pathology slides from 33, 4, and 18 patients, respectively. The summary performance metric used for comparing and ranking algorithms was the average prediction probability concordance (PK) using scores from two pathologists as the TC reference standard. Results: Test PK performance ranged from 0.497 to 0.941 across the 100 submitted algorithms. The submitted algorithms generally performed well in estimating TC, with high-performing algorithms obtaining comparable results to the average interrater PK of 0.927 from the two pathologists providing the reference TC scores. Conclusions: The SPIE-AAPM-NCI BreastPathQ Challenge was a success, indicating that artificial intelligence/machine learning algorithms may be able to approach human performance for cellularity assessment and may have some utility in clinical practice for improving efficiency and reducing reader variability. The BreastPathQ Challenge can be accessed on the Grand Challenge website. Society of Photo-Optical Instrumentation Engineers 2021-05-08 2021-05 /pmc/articles/PMC8107263/ /pubmed/33987451 http://dx.doi.org/10.1117/1.JMI.8.3.034501 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Computer-Aided Diagnosis
Petrick, Nicholas
Akbar, Shazia
Cha, Kenny H.
Nofech-Mozes, Sharon
Sahiner, Berkman
Gavrielides, Marios A.
Kalpathy-Cramer, Jayashree
Drukker, Karen
Martel, Anne L.
SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
title SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
title_full SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
title_fullStr SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
title_full_unstemmed SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
title_short SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
title_sort spie-aapm-nci breastpathq challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
topic Computer-Aided Diagnosis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107263/
https://www.ncbi.nlm.nih.gov/pubmed/33987451
http://dx.doi.org/10.1117/1.JMI.8.3.034501
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