Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National C...

Full description

Bibliographic Details
Main Authors:	Li, Hui, Zhu, Yitan, Burnside, Elizabeth S, Huang, Erich, Drukker, Karen, Hoadley, Katherine A, Fan, Cheng, Conzen, Suzanne D, Zuley, Margarita, Net, Jose M, Sutton, Elizabeth, Whitman, Gary J, Morris, Elizabeth, Perou, Charles M, Ji, Yuan, Giger, Maryellen L
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5108580/

id	pubmed-5108580
recordtype	oai_dc
spelling	pubmed-51085802016-11-14 Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set Li, Hui Zhu, Yitan Burnside, Elizabeth S Huang, Erich Drukker, Karen Hoadley, Katherine A Fan, Cheng Conzen, Suzanne D Zuley, Margarita Net, Jose M Sutton, Elizabeth Whitman, Gary J Morris, Elizabeth Perou, Charles M Ji, Yuan Giger, Maryellen L Article Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine. Nature Publishing Group 2016-05-11 /pmc/articles/PMC5108580/ /pubmed/27853751 http://dx.doi.org/10.1038/npjbcancer.2016.12 Text en Copyright © 2016 Breast Cancer Research Foundation/Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Li, Hui Zhu, Yitan Burnside, Elizabeth S Huang, Erich Drukker, Karen Hoadley, Katherine A Fan, Cheng Conzen, Suzanne D Zuley, Margarita Net, Jose M Sutton, Elizabeth Whitman, Gary J Morris, Elizabeth Perou, Charles M Ji, Yuan Giger, Maryellen L
spellingShingle	Li, Hui Zhu, Yitan Burnside, Elizabeth S Huang, Erich Drukker, Karen Hoadley, Katherine A Fan, Cheng Conzen, Suzanne D Zuley, Margarita Net, Jose M Sutton, Elizabeth Whitman, Gary J Morris, Elizabeth Perou, Charles M Ji, Yuan Giger, Maryellen L Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
author_facet	Li, Hui Zhu, Yitan Burnside, Elizabeth S Huang, Erich Drukker, Karen Hoadley, Katherine A Fan, Cheng Conzen, Suzanne D Zuley, Margarita Net, Jose M Sutton, Elizabeth Whitman, Gary J Morris, Elizabeth Perou, Charles M Ji, Yuan Giger, Maryellen L
author_sort	Li, Hui
title	Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
title_short	Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
title_full	Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
title_fullStr	Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
title_full_unstemmed	Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
title_sort	quantitative mri radiomics in the prediction of molecular classifications of breast cancer subtypes in the tcga/tcia data set
description	Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5108580/
_version_	1613726187061772288

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Similar Items