Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures

Abstract High positive margin rates in oncologic breast-conserving surgery are a pressing clinical problem. Volumetric X-ray scanning is emerging as a powerful ex vivo specimen imaging technique for analyzing resection margins, but X-rays lack contrast between non-malignant and malignant fibrous tis...

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Autores principales:	Samuel S. Streeter, Brady Hunt, Rebecca A. Zuurbier, Wendy A. Wells, Keith D. Paulsen, Brian W. Pogue
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/95368d22530d4f61a126c507f5ffdab1
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spelling	oai:doaj.org-article:95368d22530d4f61a126c507f5ffdab12021-11-14T12:19:52ZDeveloping diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures10.1038/s41598-021-01414-z2045-2322https://doaj.org/article/95368d22530d4f61a126c507f5ffdab12021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01414-zhttps://doaj.org/toc/2045-2322Abstract High positive margin rates in oncologic breast-conserving surgery are a pressing clinical problem. Volumetric X-ray scanning is emerging as a powerful ex vivo specimen imaging technique for analyzing resection margins, but X-rays lack contrast between non-malignant and malignant fibrous tissues. In this study, combined micro-CT and wide-field optical image radiomics were developed to classify malignancy of breast cancer tissues, demonstrating that X-ray/optical radiomics improve malignancy classification. Ninety-two standardized features were extracted from co-registered micro-CT and optical spatial frequency domain imaging samples extracted from 54 breast tumors exhibiting seven tissue subtypes confirmed by microscopic histological analysis. Multimodal feature sets improved classification performance versus micro-CT alone when adipose samples were included (AUC = 0.88 vs. 0.90; p-value = 3.65e−11) and excluded, focusing the classification task on exclusively non-malignant fibrous versus malignant tissues (AUC = 0.78 vs. 0.85; p-value = 9.33e−14). Extending the radiomics approach to high-dimensional optical data—termed “optomics” in this study—offers a promising optical image analysis technique for cancer detection. Radiomic feature data and classification source code are publicly available.Samuel S. StreeterBrady HuntRebecca A. ZuurbierWendy A. WellsKeith D. PaulsenBrian W. PogueNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samuel S. Streeter Brady Hunt Rebecca A. Zuurbier Wendy A. Wells Keith D. Paulsen Brian W. Pogue Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
description	Abstract High positive margin rates in oncologic breast-conserving surgery are a pressing clinical problem. Volumetric X-ray scanning is emerging as a powerful ex vivo specimen imaging technique for analyzing resection margins, but X-rays lack contrast between non-malignant and malignant fibrous tissues. In this study, combined micro-CT and wide-field optical image radiomics were developed to classify malignancy of breast cancer tissues, demonstrating that X-ray/optical radiomics improve malignancy classification. Ninety-two standardized features were extracted from co-registered micro-CT and optical spatial frequency domain imaging samples extracted from 54 breast tumors exhibiting seven tissue subtypes confirmed by microscopic histological analysis. Multimodal feature sets improved classification performance versus micro-CT alone when adipose samples were included (AUC = 0.88 vs. 0.90; p-value = 3.65e−11) and excluded, focusing the classification task on exclusively non-malignant fibrous versus malignant tissues (AUC = 0.78 vs. 0.85; p-value = 9.33e−14). Extending the radiomics approach to high-dimensional optical data—termed “optomics” in this study—offers a promising optical image analysis technique for cancer detection. Radiomic feature data and classification source code are publicly available.
format	article
author	Samuel S. Streeter Brady Hunt Rebecca A. Zuurbier Wendy A. Wells Keith D. Paulsen Brian W. Pogue
author_facet	Samuel S. Streeter Brady Hunt Rebecca A. Zuurbier Wendy A. Wells Keith D. Paulsen Brian W. Pogue
author_sort	Samuel S. Streeter
title	Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
title_short	Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
title_full	Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
title_fullStr	Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
title_full_unstemmed	Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
title_sort	developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/95368d22530d4f61a126c507f5ffdab1
work_keys_str_mv	AT samuelsstreeter developingdiagnosticassessmentofbreastlumpectomytissuesusingradiomicandopticalsignatures AT bradyhunt developingdiagnosticassessmentofbreastlumpectomytissuesusingradiomicandopticalsignatures AT rebeccaazuurbier developingdiagnosticassessmentofbreastlumpectomytissuesusingradiomicandopticalsignatures AT wendyawells developingdiagnosticassessmentofbreastlumpectomytissuesusingradiomicandopticalsignatures AT keithdpaulsen developingdiagnosticassessmentofbreastlumpectomytissuesusingradiomicandopticalsignatures AT brianwpogue developingdiagnosticassessmentofbreastlumpectomytissuesusingradiomicandopticalsignatures
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Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures

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