Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis

Abstract We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal;...

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Autores principales:	Jinwoo Son, Si Eun Lee, Eun-Kyung Kim, Sungwon Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/075f8ef6b92e451985836936ea3be9f4
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spelling	oai:doaj.org-article:075f8ef6b92e451985836936ea3be9f42021-12-02T15:11:53ZPrediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis10.1038/s41598-020-78681-92045-2322https://doaj.org/article/075f8ef6b92e451985836936ea3be9f42020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78681-9https://doaj.org/toc/2045-2322Abstract We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.Jinwoo SonSi Eun LeeEun-Kyung KimSungwon KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jinwoo Son Si Eun Lee Eun-Kyung Kim Sungwon Kim Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
description	Abstract We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.
format	article
author	Jinwoo Son Si Eun Lee Eun-Kyung Kim Sungwon Kim
author_facet	Jinwoo Son Si Eun Lee Eun-Kyung Kim Sungwon Kim
author_sort	Jinwoo Son
title	Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
title_short	Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
title_full	Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
title_fullStr	Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
title_full_unstemmed	Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
title_sort	prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/075f8ef6b92e451985836936ea3be9f4
work_keys_str_mv	AT jinwooson predictionofbreastcancermolecularsubtypesusingradiomicssignaturesofsyntheticmammographyfromdigitalbreasttomosynthesis AT sieunlee predictionofbreastcancermolecularsubtypesusingradiomicssignaturesofsyntheticmammographyfromdigitalbreasttomosynthesis AT eunkyungkim predictionofbreastcancermolecularsubtypesusingradiomicssignaturesofsyntheticmammographyfromdigitalbreasttomosynthesis AT sungwonkim predictionofbreastcancermolecularsubtypesusingradiomicssignaturesofsyntheticmammographyfromdigitalbreasttomosynthesis
_version_	1718387610736394240

Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis

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