Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score.
ER+ breast cancer
Magee equation
Oncotype DX score
deep learning-based algorithm
digital pathology
Journal
Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047
Informations de publication
Date de publication:
2022
2022
Historique:
received:
28
02
2022
accepted:
18
05
2022
entrez:
1
7
2022
pubmed:
2
7
2022
medline:
2
7
2022
Statut:
epublish
Résumé
Oncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations. We retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features. The Pearson's correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 ( Our results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.
Sections du résumé
Background
UNASSIGNED
Oncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations.
Methods
UNASSIGNED
We retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features.
Results
UNASSIGNED
The Pearson's correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (
Conclusion
UNASSIGNED
Our results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.
Identifiants
pubmed: 35775006
doi: 10.3389/fmed.2022.886763
pmc: PMC9239530
doi:
Types de publication
Journal Article
Langues
eng
Pagination
886763Subventions
Organisme : NCI NIH HHS
ID : U01 CA242936
Pays : United States
Informations de copyright
Copyright © 2022 Li, Wang, Li, Dababneh, Wang, Zhao, Smith, Teodoro, Li, Kong and Li.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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