Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data

Abstract Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmentation algorithm included three steps: ca...

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Autores principales:	Yiqiao Liu, Madhusudhana Gargesha, Mohammed Qutaish, Zhuxian Zhou, Peter Qiao, Zheng-Rong Lu, David L. Wilson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/53b589638ab74fa5a3ffbad5db734d76
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spelling	oai:doaj.org-article:53b589638ab74fa5a3ffbad5db734d762021-12-02T17:51:31ZQuantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data10.1038/s41598-021-96838-y2045-2322https://doaj.org/article/53b589638ab74fa5a3ffbad5db734d762021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96838-yhttps://doaj.org/toc/2045-2322Abstract Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmentation algorithm included three steps: candidate segmentation, candidate classification, and semi-automatic correction of the classification result. The candidate segmentation generated > 5000 candidates in each of the breast cancer-bearing mice. Random forest classifier with multi-scale CNN features and hand-crafted intensity and morphology features achieved 0.8645 ± 0.0858, 0.9738 ± 0.0074, and 0.9709 ± 0.0182 sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC), with fourfold cross validation. Classification results guided manual correction by an expert with our in-house MATLAB software. Finally, 225, 148, 165, and 344 metastases were identified in the four cancer mice. With CNN-based segmentation, the human intervention time was reduced from > 12 to ~ 2 h. We demonstrated that 4T1 breast cancer metastases spread to the lung, liver, bone, and brain. Assessing the size and distribution of metastases proves the usefulness and robustness of cryo-imaging and our software for evaluating new cancer imaging and therapeutics technologies. Application of the method with only minor modification to a pancreatic metastatic cancer model demonstrated generalizability to other tumor models.Yiqiao LiuMadhusudhana GargeshaMohammed QutaishZhuxian ZhouPeter QiaoZheng-Rong LuDavid L. WilsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yiqiao Liu Madhusudhana Gargesha Mohammed Qutaish Zhuxian Zhou Peter Qiao Zheng-Rong Lu David L. Wilson Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
description	Abstract Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmentation algorithm included three steps: candidate segmentation, candidate classification, and semi-automatic correction of the classification result. The candidate segmentation generated > 5000 candidates in each of the breast cancer-bearing mice. Random forest classifier with multi-scale CNN features and hand-crafted intensity and morphology features achieved 0.8645 ± 0.0858, 0.9738 ± 0.0074, and 0.9709 ± 0.0182 sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC), with fourfold cross validation. Classification results guided manual correction by an expert with our in-house MATLAB software. Finally, 225, 148, 165, and 344 metastases were identified in the four cancer mice. With CNN-based segmentation, the human intervention time was reduced from > 12 to ~ 2 h. We demonstrated that 4T1 breast cancer metastases spread to the lung, liver, bone, and brain. Assessing the size and distribution of metastases proves the usefulness and robustness of cryo-imaging and our software for evaluating new cancer imaging and therapeutics technologies. Application of the method with only minor modification to a pancreatic metastatic cancer model demonstrated generalizability to other tumor models.
format	article
author	Yiqiao Liu Madhusudhana Gargesha Mohammed Qutaish Zhuxian Zhou Peter Qiao Zheng-Rong Lu David L. Wilson
author_facet	Yiqiao Liu Madhusudhana Gargesha Mohammed Qutaish Zhuxian Zhou Peter Qiao Zheng-Rong Lu David L. Wilson
author_sort	Yiqiao Liu
title	Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
title_short	Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
title_full	Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
title_fullStr	Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
title_full_unstemmed	Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
title_sort	quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/53b589638ab74fa5a3ffbad5db734d76
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Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data

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