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MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma
To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS). In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting...
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Published in: | Frontiers in oncology 2024-05, Vol.14, p.1287479-1287479 |
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creator | Blocker, Stephanie J Mowery, Yvonne M Everitt, Jeffrey I Cook, James Cofer, Gary Price Qi, Yi Bassil, Alex M Xu, Eric S Kirsch, David G Badea, Cristian T Johnson, G Allan |
description | To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).
In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast
MRI, three-dimensional (3D) multi-contrast high-resolution
MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of |
doi_str_mv | 10.3389/fonc.2024.1287479 |
format | article |
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In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast
MRI, three-dimensional (3D) multi-contrast high-resolution
MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.
Three features correlated with
apparent diffusion coefficient (ADC), and no features correlated with
ADC. Six features demonstrated significant linear relationships with
T2*, and fifteen features correlated significantly with
T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.
Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.</description><identifier>ISSN: 2234-943X</identifier><identifier>EISSN: 2234-943X</identifier><identifier>DOI: 10.3389/fonc.2024.1287479</identifier><identifier>PMID: 38884083</identifier><language>eng</language><publisher>Switzerland: Frontiers Media S.A</publisher><subject>histology ; image registration ; MRI ; multi-modal ; Oncology ; preclinical ; sarcoma</subject><ispartof>Frontiers in oncology, 2024-05, Vol.14, p.1287479-1287479</ispartof><rights>Copyright © 2024 Blocker, Mowery, Everitt, Cook, Cofer, Qi, Bassil, Xu, Kirsch, Badea and Johnson.</rights><rights>Copyright © 2024 Blocker, Mowery, Everitt, Cook, Cofer, Qi, Bassil, Xu, Kirsch, Badea and Johnson 2024 Blocker, Mowery, Everitt, Cook, Cofer, Qi, Bassil, Xu, Kirsch, Badea and Johnson</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c348t-15a486daa2a4a665ec17a55ca63003910a4c4f18f5d65bc0d36b3b11d38744b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11176416/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11176416/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38884083$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Blocker, Stephanie J</creatorcontrib><creatorcontrib>Mowery, Yvonne M</creatorcontrib><creatorcontrib>Everitt, Jeffrey I</creatorcontrib><creatorcontrib>Cook, James</creatorcontrib><creatorcontrib>Cofer, Gary Price</creatorcontrib><creatorcontrib>Qi, Yi</creatorcontrib><creatorcontrib>Bassil, Alex M</creatorcontrib><creatorcontrib>Xu, Eric S</creatorcontrib><creatorcontrib>Kirsch, David G</creatorcontrib><creatorcontrib>Badea, Cristian T</creatorcontrib><creatorcontrib>Johnson, G Allan</creatorcontrib><title>MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma</title><title>Frontiers in oncology</title><addtitle>Front Oncol</addtitle><description>To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).
In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast
MRI, three-dimensional (3D) multi-contrast high-resolution
MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.
Three features correlated with
apparent diffusion coefficient (ADC), and no features correlated with
ADC. Six features demonstrated significant linear relationships with
T2*, and fifteen features correlated significantly with
T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.
Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.</description><subject>histology</subject><subject>image registration</subject><subject>MRI</subject><subject>multi-modal</subject><subject>Oncology</subject><subject>preclinical</subject><subject>sarcoma</subject><issn>2234-943X</issn><issn>2234-943X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVkU1P3DAQhqOqVUGUH9BL5WMvu_grjnOqKtSPlUCVEIferIk9yRo5MbUT0P77etkFgQ-e8XjmmdG8VfWZ0bUQur3o42TXnHK5Zlw3smnfVaecC7lqpfj7_pV_Up3nfEfLUTVlVHysToTWWlItTqvH6xuy9XmOIQ47kvABIWQy-5wXJD3CvCTMpMOpuFuyxRlTHMorLplc32xI9sMEgfiJ7KOztxDCjuA0-AkxoSNjycRyOyzc2JMMycYRPlUf-tIJz4_2rLr9-eP28vfq6s-vzeX3q5UVUs8rVoPUygFwkKBUjZY1UNcWlKBUtIyCtLJnuq-dqjtLnVCd6BhzoqxEduKs2hywLsKduU9-hLQzEbx5CsQ0GEhl6oDGAtNSIRcdbSVtrLbF6Lrm6DqruCusbwfW_dKN6CxOc4LwBvr2Z_JbM8QHwxhrlGSqEL4eCSn-WzDPZvTZYgjwtFAjqGpZIxqmSyo7pNoUc07Yv_Rh1Oz1N3v9zV5_c9S_1Hx5PeBLxbPa4j_Nl675</recordid><startdate>20240531</startdate><enddate>20240531</enddate><creator>Blocker, Stephanie J</creator><creator>Mowery, Yvonne M</creator><creator>Everitt, Jeffrey I</creator><creator>Cook, James</creator><creator>Cofer, Gary Price</creator><creator>Qi, Yi</creator><creator>Bassil, Alex M</creator><creator>Xu, Eric S</creator><creator>Kirsch, David G</creator><creator>Badea, Cristian T</creator><creator>Johnson, G Allan</creator><general>Frontiers Media S.A</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20240531</creationdate><title>MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma</title><author>Blocker, Stephanie J ; Mowery, Yvonne M ; Everitt, Jeffrey I ; Cook, James ; Cofer, Gary Price ; Qi, Yi ; Bassil, Alex M ; Xu, Eric S ; Kirsch, David G ; Badea, Cristian T ; Johnson, G Allan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-15a486daa2a4a665ec17a55ca63003910a4c4f18f5d65bc0d36b3b11d38744b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>histology</topic><topic>image registration</topic><topic>MRI</topic><topic>multi-modal</topic><topic>Oncology</topic><topic>preclinical</topic><topic>sarcoma</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Blocker, Stephanie J</creatorcontrib><creatorcontrib>Mowery, Yvonne M</creatorcontrib><creatorcontrib>Everitt, Jeffrey I</creatorcontrib><creatorcontrib>Cook, James</creatorcontrib><creatorcontrib>Cofer, Gary Price</creatorcontrib><creatorcontrib>Qi, Yi</creatorcontrib><creatorcontrib>Bassil, Alex M</creatorcontrib><creatorcontrib>Xu, Eric S</creatorcontrib><creatorcontrib>Kirsch, David G</creatorcontrib><creatorcontrib>Badea, Cristian T</creatorcontrib><creatorcontrib>Johnson, G Allan</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Frontiers in oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Blocker, Stephanie J</au><au>Mowery, Yvonne M</au><au>Everitt, Jeffrey I</au><au>Cook, James</au><au>Cofer, Gary Price</au><au>Qi, Yi</au><au>Bassil, Alex M</au><au>Xu, Eric S</au><au>Kirsch, David G</au><au>Badea, Cristian T</au><au>Johnson, G Allan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma</atitle><jtitle>Frontiers in oncology</jtitle><addtitle>Front Oncol</addtitle><date>2024-05-31</date><risdate>2024</risdate><volume>14</volume><spage>1287479</spage><epage>1287479</epage><pages>1287479-1287479</pages><issn>2234-943X</issn><eissn>2234-943X</eissn><abstract>To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).
In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast
MRI, three-dimensional (3D) multi-contrast high-resolution
MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.
Three features correlated with
apparent diffusion coefficient (ADC), and no features correlated with
ADC. Six features demonstrated significant linear relationships with
T2*, and fifteen features correlated significantly with
T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.
Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.</abstract><cop>Switzerland</cop><pub>Frontiers Media S.A</pub><pmid>38884083</pmid><doi>10.3389/fonc.2024.1287479</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | histology image registration MRI multi-modal Oncology preclinical sarcoma |
title | MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma |
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