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Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes
BACKGROUND: The abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028733/ https://www.ncbi.nlm.nih.gov/pubmed/33827490 http://dx.doi.org/10.1186/s12885-021-08122-x |
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author | Arefan, Dooman Hausler, Ryan M. Sumkin, Jules H. Sun, Min Wu, Shandong |
author_facet | Arefan, Dooman Hausler, Ryan M. Sumkin, Jules H. Sun, Min Wu, Shandong |
author_sort | Arefan, Dooman |
collection | PubMed |
description | BACKGROUND: The abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict the abundance of various cell types in the TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell types in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging. METHODS: We performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell type abundance, we performed linear regression on each radiomic feature/cell type abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell type infiltration status (i.e. “high” vs “low”) prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models’ performance was measured via area under the receiver operating characteristic curve (AUC). RESULTS: Univariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell type invasion predictions. CONCLUSIONS: On two independent breast cancer cohorts, breast MRI-derived radiomics are associated with the tumor’s microenvironment in terms of the abundance of several cell types. Further evaluation with larger cohorts is needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08122-x. |
format | Online Article Text |
id | pubmed-8028733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80287332021-04-08 Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes Arefan, Dooman Hausler, Ryan M. Sumkin, Jules H. Sun, Min Wu, Shandong BMC Cancer Research Article BACKGROUND: The abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict the abundance of various cell types in the TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell types in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging. METHODS: We performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell type abundance, we performed linear regression on each radiomic feature/cell type abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell type infiltration status (i.e. “high” vs “low”) prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models’ performance was measured via area under the receiver operating characteristic curve (AUC). RESULTS: Univariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell type invasion predictions. CONCLUSIONS: On two independent breast cancer cohorts, breast MRI-derived radiomics are associated with the tumor’s microenvironment in terms of the abundance of several cell types. Further evaluation with larger cohorts is needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08122-x. BioMed Central 2021-04-07 /pmc/articles/PMC8028733/ /pubmed/33827490 http://dx.doi.org/10.1186/s12885-021-08122-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Arefan, Dooman Hausler, Ryan M. Sumkin, Jules H. Sun, Min Wu, Shandong Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes |
title | Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes |
title_full | Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes |
title_fullStr | Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes |
title_full_unstemmed | Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes |
title_short | Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes |
title_sort | predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028733/ https://www.ncbi.nlm.nih.gov/pubmed/33827490 http://dx.doi.org/10.1186/s12885-021-08122-x |
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