Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction
Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by...
Gespeichert in:
| Veröffentlicht in: | European journal of radiology 2024-06, Vol.175, p.111441-111441, Article 111441 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 111441 |
|---|---|
| container_issue | |
| container_start_page | 111441 |
| container_title | European journal of radiology |
| container_volume | 175 |
| creator | Han, Xiaorui Gong, Zhengze Guo, Yuan Tang, Wenjie Wei, Xinhua |
| description | Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction.
Utilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer’s ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves.
Three distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P |
| doi_str_mv | 10.1016/j.ejrad.2024.111441 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3014012528</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0720048X24001578</els_id><sourcerecordid>3014012528</sourcerecordid><originalsourceid>FETCH-LOGICAL-c309t-aac6c8b50010c75f0c27c9ac8616564a7dcbd708075790d3c78b26bcdc0e8a0d3</originalsourceid><addsrcrecordid>eNp9kctuFDEQRS0EIpPAFyAhL9n0UHY_7FmwQFEeSJHYgMTOcperI4-67cHuHkF-gZ-OJx1YsrJ0ferW4zL2TsBWgOg-7re0T9ZtJchmK4RoGvGCbYRWslJKqpdsA0pCBY3-ccbOc94DQNvs5Gt2Vuu2Vh2oDftz9eswxmRnHwOPA7c8xODD0WZ_JF7sfZw8Zo6jzdkPnhIfYuJ9IptnjjZgUeZlKlrhUqRw9CmGicLpd6b7mPzD6m6D44cU70PMs0celxnjREUi5_FEvGGvBjtmevv8XrDv11ffLm-ru683Xy4_31VYw26urMUOdd8CCEDVDoBS4c6i7kTXdo1VDnunQINq1Q5cjUr3suvRIZC2RbhgH1bfMs3PhfJsJp-RxtEGiks2NYgGhGylLmi9omW1nBMN5pD8ZNNvI8CcUjB785SCOaVg1hRK1fvnBks_kftX8_fsBfi0AlTWPJajmoyeyi2dT4SzcdH_t8EjdhCeHA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3014012528</pqid></control><display><type>article</type><title>Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction</title><source>MEDLINE</source><source>ScienceDirect</source><creator>Han, Xiaorui ; Gong, Zhengze ; Guo, Yuan ; Tang, Wenjie ; Wei, Xinhua</creator><creatorcontrib>Han, Xiaorui ; Gong, Zhengze ; Guo, Yuan ; Tang, Wenjie ; Wei, Xinhua</creatorcontrib><description>Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction.
Utilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer’s ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves.
Three distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P < 0.05). The classifier, comprising 24 radiomic features, demonstrated high predictive accuracy (AUC of training set = 0.960, 95 % CI: 0.922, 0.997; AUC of testing set = 0.853, 95 % CI: 0.687, 1.000) in differentiating these TME states. Predictions from the classifier also correlated significantly with overall patient survival (P < 0.05).
This study offers a detailed analysis of the complex TME states in breast cancer and presents a reliable, noninvasive radiomics classifier for TME assessment. The classifer’s accurate prediction of TME status and its correlation with prognosis highlight its potential as a tool in personalized breast cancer treatment, paving the way for more individualized and less invasive therapeutic strategies.</description><identifier>ISSN: 0720-048X</identifier><identifier>EISSN: 1872-7727</identifier><identifier>DOI: 10.1016/j.ejrad.2024.111441</identifier><identifier>PMID: 38537607</identifier><language>eng</language><publisher>Ireland: Elsevier B.V</publisher><subject>Adult ; Aged ; Algorithms ; Breast Neoplasms ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - genetics ; Breast Neoplasms - pathology ; Contrast Media ; Female ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Magnetic Resonance Imaging - methods ; Middle Aged ; Prognosis ; Radiomics ; Tumor Microenvironment</subject><ispartof>European journal of radiology, 2024-06, Vol.175, p.111441-111441, Article 111441</ispartof><rights>2024</rights><rights>Copyright © 2024. Published by Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c309t-aac6c8b50010c75f0c27c9ac8616564a7dcbd708075790d3c78b26bcdc0e8a0d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejrad.2024.111441$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38537607$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Han, Xiaorui</creatorcontrib><creatorcontrib>Gong, Zhengze</creatorcontrib><creatorcontrib>Guo, Yuan</creatorcontrib><creatorcontrib>Tang, Wenjie</creatorcontrib><creatorcontrib>Wei, Xinhua</creatorcontrib><title>Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction</title><title>European journal of radiology</title><addtitle>Eur J Radiol</addtitle><description>Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction.
Utilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer’s ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves.
Three distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P < 0.05). The classifier, comprising 24 radiomic features, demonstrated high predictive accuracy (AUC of training set = 0.960, 95 % CI: 0.922, 0.997; AUC of testing set = 0.853, 95 % CI: 0.687, 1.000) in differentiating these TME states. Predictions from the classifier also correlated significantly with overall patient survival (P < 0.05).
This study offers a detailed analysis of the complex TME states in breast cancer and presents a reliable, noninvasive radiomics classifier for TME assessment. The classifer’s accurate prediction of TME status and its correlation with prognosis highlight its potential as a tool in personalized breast cancer treatment, paving the way for more individualized and less invasive therapeutic strategies.</description><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Breast Neoplasms</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Breast Neoplasms - genetics</subject><subject>Breast Neoplasms - pathology</subject><subject>Contrast Media</subject><subject>Female</subject><subject>Humans</subject><subject>Machine Learning</subject><subject>Magnetic Resonance Imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Middle Aged</subject><subject>Prognosis</subject><subject>Radiomics</subject><subject>Tumor Microenvironment</subject><issn>0720-048X</issn><issn>1872-7727</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kctuFDEQRS0EIpPAFyAhL9n0UHY_7FmwQFEeSJHYgMTOcperI4-67cHuHkF-gZ-OJx1YsrJ0ferW4zL2TsBWgOg-7re0T9ZtJchmK4RoGvGCbYRWslJKqpdsA0pCBY3-ccbOc94DQNvs5Gt2Vuu2Vh2oDftz9eswxmRnHwOPA7c8xODD0WZ_JF7sfZw8Zo6jzdkPnhIfYuJ9IptnjjZgUeZlKlrhUqRw9CmGicLpd6b7mPzD6m6D44cU70PMs0celxnjREUi5_FEvGGvBjtmevv8XrDv11ffLm-ru683Xy4_31VYw26urMUOdd8CCEDVDoBS4c6i7kTXdo1VDnunQINq1Q5cjUr3suvRIZC2RbhgH1bfMs3PhfJsJp-RxtEGiks2NYgGhGylLmi9omW1nBMN5pD8ZNNvI8CcUjB785SCOaVg1hRK1fvnBks_kftX8_fsBfi0AlTWPJajmoyeyi2dT4SzcdH_t8EjdhCeHA</recordid><startdate>202406</startdate><enddate>202406</enddate><creator>Han, Xiaorui</creator><creator>Gong, Zhengze</creator><creator>Guo, Yuan</creator><creator>Tang, Wenjie</creator><creator>Wei, Xinhua</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202406</creationdate><title>Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction</title><author>Han, Xiaorui ; Gong, Zhengze ; Guo, Yuan ; Tang, Wenjie ; Wei, Xinhua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c309t-aac6c8b50010c75f0c27c9ac8616564a7dcbd708075790d3c78b26bcdc0e8a0d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Breast Neoplasms</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Breast Neoplasms - genetics</topic><topic>Breast Neoplasms - pathology</topic><topic>Contrast Media</topic><topic>Female</topic><topic>Humans</topic><topic>Machine Learning</topic><topic>Magnetic Resonance Imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Middle Aged</topic><topic>Prognosis</topic><topic>Radiomics</topic><topic>Tumor Microenvironment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Han, Xiaorui</creatorcontrib><creatorcontrib>Gong, Zhengze</creatorcontrib><creatorcontrib>Guo, Yuan</creatorcontrib><creatorcontrib>Tang, Wenjie</creatorcontrib><creatorcontrib>Wei, Xinhua</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Han, Xiaorui</au><au>Gong, Zhengze</au><au>Guo, Yuan</au><au>Tang, Wenjie</au><au>Wei, Xinhua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction</atitle><jtitle>European journal of radiology</jtitle><addtitle>Eur J Radiol</addtitle><date>2024-06</date><risdate>2024</risdate><volume>175</volume><spage>111441</spage><epage>111441</epage><pages>111441-111441</pages><artnum>111441</artnum><issn>0720-048X</issn><eissn>1872-7727</eissn><abstract>Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction.
Utilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer’s ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves.
Three distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P < 0.05). The classifier, comprising 24 radiomic features, demonstrated high predictive accuracy (AUC of training set = 0.960, 95 % CI: 0.922, 0.997; AUC of testing set = 0.853, 95 % CI: 0.687, 1.000) in differentiating these TME states. Predictions from the classifier also correlated significantly with overall patient survival (P < 0.05).
This study offers a detailed analysis of the complex TME states in breast cancer and presents a reliable, noninvasive radiomics classifier for TME assessment. The classifer’s accurate prediction of TME status and its correlation with prognosis highlight its potential as a tool in personalized breast cancer treatment, paving the way for more individualized and less invasive therapeutic strategies.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>38537607</pmid><doi>10.1016/j.ejrad.2024.111441</doi><tpages>1</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0720-048X |
| ispartof | European journal of radiology, 2024-06, Vol.175, p.111441-111441, Article 111441 |
| issn | 0720-048X 1872-7727 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3014012528 |
| source | MEDLINE; ScienceDirect |
| subjects | Adult Aged Algorithms Breast Neoplasms Breast Neoplasms - diagnostic imaging Breast Neoplasms - genetics Breast Neoplasms - pathology Contrast Media Female Humans Machine Learning Magnetic Resonance Imaging Magnetic Resonance Imaging - methods Middle Aged Prognosis Radiomics Tumor Microenvironment |
| title | Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T17%3A20%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Exploration%20of%20a%20noninvasive%20radiomics%20classifier%20for%20breast%20cancer%20tumor%20microenvironment%20categorization%20and%20prognostic%20outcome%20prediction&rft.jtitle=European%20journal%20of%20radiology&rft.au=Han,%20Xiaorui&rft.date=2024-06&rft.volume=175&rft.spage=111441&rft.epage=111441&rft.pages=111441-111441&rft.artnum=111441&rft.issn=0720-048X&rft.eissn=1872-7727&rft_id=info:doi/10.1016/j.ejrad.2024.111441&rft_dat=%3Cproquest_cross%3E3014012528%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3014012528&rft_id=info:pmid/38537607&rft_els_id=S0720048X24001578&rfr_iscdi=true |