Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics
Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segment...
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| Veröffentlicht in: | Cancers 2022-11, Vol.14 (22), p.5507 |
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| creator | Ming, Wenlong Li, Fuyu Zhu, Yanhui Bai, Yunfei Gu, Wanjun Liu, Yun Liu, Xiaoan Sun, Xiao Liu, Hongde |
| description | Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from |
| doi_str_mv | 10.3390/cancers14225507 |
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| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9688868</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A745272811</galeid><sourcerecordid>A745272811</sourcerecordid><originalsourceid>FETCH-LOGICAL-c488t-8570b41596f1c230274e3e2feb5ead1fb14b4da24082fb07e7bf178ba514d5f43</originalsourceid><addsrcrecordid>eNptkk1v1DAQhiMEolXpmRuyxIVLWn8ldi5Iu2kLlQqVlvZsOc5k11XWXuxk0f4ffijOtpS2wj7Ynnnm9Xg8Wfae4BPGKnxqtDMQIuGUFgUWr7JDigXNy7Lir5_sD7LjGO9wGowRUYq32QErOZUlxofZ71sXxw2ErY3QopnT_S7aiOZ6OnqHzurz_NviEi10a_3amoguQA9jgIgWsAXdJ-xmFQDQd7-FHs0D6Digep8a-jE2w26T2F92WKEzGwfrTPL21lmje3Q9Dsavk1-7Fs2t7_1yb69XOmgzQJgiTHyXvel0H-H4YT3Kbi_Ob-qv-dX1l8t6dpUbLuWQy0LghpOiKjtiKMNUcGBAO2gK0C3pGsIb3mrKsaRdgwWIpiNCNrogvC06zo6yz_e6m7FZQ2vADUH3ahPsWoed8tqq5x5nV2rpt6oqpZSlTAKfHgSC_zlCHNTaRgN9rx34MaqUES4wZ3v04wv0zo8h1X-iWMVJUhT_qGWqtLKu8-leM4mqmeAFFVQSkqiT_1BptpC-zDvobLI_Czi9DzDBxxige3wjwWrqLfWit1LEh6eleeT_dhL7AzlJzOY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2739418887</pqid></control><display><type>article</type><title>Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>PubMed Central</source><creator>Ming, Wenlong ; Li, Fuyu ; Zhu, Yanhui ; Bai, Yunfei ; Gu, Wanjun ; Liu, Yun ; Liu, Xiaoan ; Sun, Xiao ; Liu, Hongde</creator><creatorcontrib>Ming, Wenlong ; Li, Fuyu ; Zhu, Yanhui ; Bai, Yunfei ; Gu, Wanjun ; Liu, Yun ; Liu, Xiaoan ; Sun, Xiao ; Liu, Hongde</creatorcontrib><description>Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers14225507</identifier><identifier>PMID: 36428600</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Bias ; Bioinformatics ; Biomarkers ; Breast cancer ; Cell cycle ; Clinical outcomes ; Datasets ; Diagnosis ; Extracellular matrix ; Fibroblasts ; Gene expression ; Magnetic resonance imaging ; Mammography ; Medical imaging ; Metastasis ; Methods ; Patient outcomes ; Patients ; Precision medicine ; Prognosis ; Radiomics ; Tumor microenvironment ; Tumors</subject><ispartof>Cancers, 2022-11, Vol.14 (22), p.5507</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c488t-8570b41596f1c230274e3e2feb5ead1fb14b4da24082fb07e7bf178ba514d5f43</citedby><cites>FETCH-LOGICAL-c488t-8570b41596f1c230274e3e2feb5ead1fb14b4da24082fb07e7bf178ba514d5f43</cites><orcidid>0000-0003-1048-7775 ; 0000-0001-8768-764X ; 0000-0002-4088-4117</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688868/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688868/$$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/36428600$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ming, Wenlong</creatorcontrib><creatorcontrib>Li, Fuyu</creatorcontrib><creatorcontrib>Zhu, Yanhui</creatorcontrib><creatorcontrib>Bai, Yunfei</creatorcontrib><creatorcontrib>Gu, Wanjun</creatorcontrib><creatorcontrib>Liu, Yun</creatorcontrib><creatorcontrib>Liu, Xiaoan</creatorcontrib><creatorcontrib>Sun, Xiao</creatorcontrib><creatorcontrib>Liu, Hongde</creatorcontrib><title>Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics</title><title>Cancers</title><addtitle>Cancers (Basel)</addtitle><description>Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.</description><subject>Bias</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Breast cancer</subject><subject>Cell cycle</subject><subject>Clinical outcomes</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Extracellular matrix</subject><subject>Fibroblasts</subject><subject>Gene expression</subject><subject>Magnetic resonance imaging</subject><subject>Mammography</subject><subject>Medical imaging</subject><subject>Metastasis</subject><subject>Methods</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Precision medicine</subject><subject>Prognosis</subject><subject>Radiomics</subject><subject>Tumor microenvironment</subject><subject>Tumors</subject><issn>2072-6694</issn><issn>2072-6694</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNptkk1v1DAQhiMEolXpmRuyxIVLWn8ldi5Iu2kLlQqVlvZsOc5k11XWXuxk0f4ffijOtpS2wj7Ynnnm9Xg8Wfae4BPGKnxqtDMQIuGUFgUWr7JDigXNy7Lir5_sD7LjGO9wGowRUYq32QErOZUlxofZ71sXxw2ErY3QopnT_S7aiOZ6OnqHzurz_NviEi10a_3amoguQA9jgIgWsAXdJ-xmFQDQd7-FHs0D6Digep8a-jE2w26T2F92WKEzGwfrTPL21lmje3Q9Dsavk1-7Fs2t7_1yb69XOmgzQJgiTHyXvel0H-H4YT3Kbi_Ob-qv-dX1l8t6dpUbLuWQy0LghpOiKjtiKMNUcGBAO2gK0C3pGsIb3mrKsaRdgwWIpiNCNrogvC06zo6yz_e6m7FZQ2vADUH3ahPsWoed8tqq5x5nV2rpt6oqpZSlTAKfHgSC_zlCHNTaRgN9rx34MaqUES4wZ3v04wv0zo8h1X-iWMVJUhT_qGWqtLKu8-leM4mqmeAFFVQSkqiT_1BptpC-zDvobLI_Czi9DzDBxxige3wjwWrqLfWit1LEh6eleeT_dhL7AzlJzOY</recordid><startdate>20221109</startdate><enddate>20221109</enddate><creator>Ming, Wenlong</creator><creator>Li, Fuyu</creator><creator>Zhu, Yanhui</creator><creator>Bai, Yunfei</creator><creator>Gu, Wanjun</creator><creator>Liu, Yun</creator><creator>Liu, Xiaoan</creator><creator>Sun, Xiao</creator><creator>Liu, Hongde</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T5</scope><scope>7TO</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1048-7775</orcidid><orcidid>https://orcid.org/0000-0001-8768-764X</orcidid><orcidid>https://orcid.org/0000-0002-4088-4117</orcidid></search><sort><creationdate>20221109</creationdate><title>Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics</title><author>Ming, Wenlong ; Li, Fuyu ; Zhu, Yanhui ; Bai, Yunfei ; Gu, Wanjun ; Liu, Yun ; Liu, Xiaoan ; Sun, Xiao ; Liu, Hongde</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c488t-8570b41596f1c230274e3e2feb5ead1fb14b4da24082fb07e7bf178ba514d5f43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Bias</topic><topic>Bioinformatics</topic><topic>Biomarkers</topic><topic>Breast cancer</topic><topic>Cell cycle</topic><topic>Clinical outcomes</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Extracellular matrix</topic><topic>Fibroblasts</topic><topic>Gene expression</topic><topic>Magnetic resonance imaging</topic><topic>Mammography</topic><topic>Medical imaging</topic><topic>Metastasis</topic><topic>Methods</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Precision medicine</topic><topic>Prognosis</topic><topic>Radiomics</topic><topic>Tumor microenvironment</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ming, Wenlong</creatorcontrib><creatorcontrib>Li, Fuyu</creatorcontrib><creatorcontrib>Zhu, Yanhui</creatorcontrib><creatorcontrib>Bai, Yunfei</creatorcontrib><creatorcontrib>Gu, Wanjun</creatorcontrib><creatorcontrib>Liu, Yun</creatorcontrib><creatorcontrib>Liu, Xiaoan</creatorcontrib><creatorcontrib>Sun, Xiao</creatorcontrib><creatorcontrib>Liu, Hongde</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Immunology Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cancers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ming, Wenlong</au><au>Li, Fuyu</au><au>Zhu, Yanhui</au><au>Bai, Yunfei</au><au>Gu, Wanjun</au><au>Liu, Yun</au><au>Liu, Xiaoan</au><au>Sun, Xiao</au><au>Liu, Hongde</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics</atitle><jtitle>Cancers</jtitle><addtitle>Cancers (Basel)</addtitle><date>2022-11-09</date><risdate>2022</risdate><volume>14</volume><issue>22</issue><spage>5507</spage><pages>5507-</pages><issn>2072-6694</issn><eissn>2072-6694</eissn><abstract>Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>36428600</pmid><doi>10.3390/cancers14225507</doi><orcidid>https://orcid.org/0000-0003-1048-7775</orcidid><orcidid>https://orcid.org/0000-0001-8768-764X</orcidid><orcidid>https://orcid.org/0000-0002-4088-4117</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; PubMed Central |
| subjects | Bias Bioinformatics Biomarkers Breast cancer Cell cycle Clinical outcomes Datasets Diagnosis Extracellular matrix Fibroblasts Gene expression Magnetic resonance imaging Mammography Medical imaging Metastasis Methods Patient outcomes Patients Precision medicine Prognosis Radiomics Tumor microenvironment Tumors |
| title | Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics |
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