Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer pat...

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Autores principales:	Mary Katherine Montgomery, John David, Haikuo Zhang, Sripad Ram, Shibing Deng, Vidya Premkumar, Lisa Manzuk, Ziyue Karen Jiang, Anand Giddabasappa
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9e68815adf6744f5993de637520b8051
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spelling	oai:doaj.org-article:9e68815adf6744f5993de637520b80512021-12-02T20:10:30ZMouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.1932-620310.1371/journal.pone.0252950https://doaj.org/article/9e68815adf6744f5993de637520b80512021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252950https://doaj.org/toc/1932-6203Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.Mary Katherine MontgomeryJohn DavidHaikuo ZhangSripad RamShibing DengVidya PremkumarLisa ManzukZiyue Karen JiangAnand GiddabasappaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252950 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mary Katherine Montgomery John David Haikuo Zhang Sripad Ram Shibing Deng Vidya Premkumar Lisa Manzuk Ziyue Karen Jiang Anand Giddabasappa Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
description	Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.
format	article
author	Mary Katherine Montgomery John David Haikuo Zhang Sripad Ram Shibing Deng Vidya Premkumar Lisa Manzuk Ziyue Karen Jiang Anand Giddabasappa
author_facet	Mary Katherine Montgomery John David Haikuo Zhang Sripad Ram Shibing Deng Vidya Premkumar Lisa Manzuk Ziyue Karen Jiang Anand Giddabasappa
author_sort	Mary Katherine Montgomery
title	Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
title_short	Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
title_full	Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
title_fullStr	Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
title_full_unstemmed	Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
title_sort	mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9e68815adf6744f5993de637520b8051
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Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

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