Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer

•Radiomics models based on patients with rectal cancer receiving surgery alone for tumour-stroma ratio evaluation can be applied in patients with locally advanced rectal cancer treated with neoadjuvant therapy, which always damages the tumor microenvironment, resulting in a incapableness of accurate...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:European journal of radiology 2024-01, Vol.170, p.111254-111254, Article 111254
Hauptverfasser: Cai, Chongpeng, Hu, Tingdan, Rong, Zening, Gong, Jing, Tong, Tong
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 111254
container_issue
container_start_page 111254
container_title European journal of radiology
container_volume 170
creator Cai, Chongpeng
Hu, Tingdan
Rong, Zening
Gong, Jing
Tong, Tong
description •Radiomics models based on patients with rectal cancer receiving surgery alone for tumour-stroma ratio evaluation can be applied in patients with locally advanced rectal cancer treated with neoadjuvant therapy, which always damages the tumor microenvironment, resulting in a incapableness of accurate tumour-stroma ratio evaluation.•Radiomics model based on high-resolution T2WI and clinical-radiomics model for tumour-stroma ratio evaluation can predict DFS and OS in patients with locally advanced rectal cancer treated with neoadjuvant therapy. To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. A total of 178 patients (mean age: 59.35, range 20–85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients’ pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23–74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan–Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC
doi_str_mv 10.1016/j.ejrad.2023.111254
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2902974057</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0720048X23005685</els_id><sourcerecordid>2902974057</sourcerecordid><originalsourceid>FETCH-LOGICAL-c309t-529e2738655f607b08fe7a9c2329da64083d2afb6442deaedfa54427737a64083</originalsourceid><addsrcrecordid>eNp9kMtKxDAUhoMoznh5AkGydNPxJL2kXbgQ8QaCLhTchUxyqhnTZkzaAd_e1o4uXSUh339-zkfICYMFA1acrxa4CsosOPB0wRjjebZD5qwUPBGCi10yB8Ehgax8nZGDGFcAkGcV3yeztISKFQWfk4-n4N9aHzur6TqgsbqzvqUb5XqkvqbdO1LtbGu1csnQZn1jdaRd3_g-JLELvlE0qCFEbUudHzD3RZXZqFajoQF1pxzV4ysckb1auYjH2_OQvNxcP1_dJQ-Pt_dXlw-JTqHqkpxXyEVaFnleFyCWUNYoVKV5yiujigzK1HBVL4ss4wYVmlrlw1WIVEy_h-RsmrsO_rPH2MnGRo3OqRZ9HyWvgFcig1wMaDqhOvgYA9ZyHWyjwpdkIEfLciV_LMvRspwsD6nTbUG_bND8ZX61DsDFBOCw5sZikFFbHI3Y0Yg03v5b8A0QFY_d</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2902974057</pqid></control><display><type>article</type><title>Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Cai, Chongpeng ; Hu, Tingdan ; Rong, Zening ; Gong, Jing ; Tong, Tong</creator><creatorcontrib>Cai, Chongpeng ; Hu, Tingdan ; Rong, Zening ; Gong, Jing ; Tong, Tong</creatorcontrib><description>•Radiomics models based on patients with rectal cancer receiving surgery alone for tumour-stroma ratio evaluation can be applied in patients with locally advanced rectal cancer treated with neoadjuvant therapy, which always damages the tumor microenvironment, resulting in a incapableness of accurate tumour-stroma ratio evaluation.•Radiomics model based on high-resolution T2WI and clinical-radiomics model for tumour-stroma ratio evaluation can predict DFS and OS in patients with locally advanced rectal cancer treated with neoadjuvant therapy. To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. A total of 178 patients (mean age: 59.35, range 20–85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients’ pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23–74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan–Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077–2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281–4.396). We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.</description><identifier>ISSN: 0720-048X</identifier><identifier>EISSN: 1872-7727</identifier><identifier>DOI: 10.1016/j.ejrad.2023.111254</identifier><identifier>PMID: 38091662</identifier><language>eng</language><publisher>Ireland: Elsevier B.V</publisher><subject>Magnetic resonance imaging ; Predictive model ; Radiomics ; Rectal neoplasms ; Tumour-stroma ratio</subject><ispartof>European journal of radiology, 2024-01, Vol.170, p.111254-111254, Article 111254</ispartof><rights>2023 Elsevier B.V.</rights><rights>Copyright © 2023 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c309t-529e2738655f607b08fe7a9c2329da64083d2afb6442deaedfa54427737a64083</cites><orcidid>0000-0002-9180-8181</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejrad.2023.111254$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38091662$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cai, Chongpeng</creatorcontrib><creatorcontrib>Hu, Tingdan</creatorcontrib><creatorcontrib>Rong, Zening</creatorcontrib><creatorcontrib>Gong, Jing</creatorcontrib><creatorcontrib>Tong, Tong</creatorcontrib><title>Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer</title><title>European journal of radiology</title><addtitle>Eur J Radiol</addtitle><description>•Radiomics models based on patients with rectal cancer receiving surgery alone for tumour-stroma ratio evaluation can be applied in patients with locally advanced rectal cancer treated with neoadjuvant therapy, which always damages the tumor microenvironment, resulting in a incapableness of accurate tumour-stroma ratio evaluation.•Radiomics model based on high-resolution T2WI and clinical-radiomics model for tumour-stroma ratio evaluation can predict DFS and OS in patients with locally advanced rectal cancer treated with neoadjuvant therapy. To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. A total of 178 patients (mean age: 59.35, range 20–85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients’ pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23–74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan–Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077–2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281–4.396). We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.</description><subject>Magnetic resonance imaging</subject><subject>Predictive model</subject><subject>Radiomics</subject><subject>Rectal neoplasms</subject><subject>Tumour-stroma ratio</subject><issn>0720-048X</issn><issn>1872-7727</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKxDAUhoMoznh5AkGydNPxJL2kXbgQ8QaCLhTchUxyqhnTZkzaAd_e1o4uXSUh339-zkfICYMFA1acrxa4CsosOPB0wRjjebZD5qwUPBGCi10yB8Ehgax8nZGDGFcAkGcV3yeztISKFQWfk4-n4N9aHzur6TqgsbqzvqUb5XqkvqbdO1LtbGu1csnQZn1jdaRd3_g-JLELvlE0qCFEbUudHzD3RZXZqFajoQF1pxzV4ysckb1auYjH2_OQvNxcP1_dJQ-Pt_dXlw-JTqHqkpxXyEVaFnleFyCWUNYoVKV5yiujigzK1HBVL4ss4wYVmlrlw1WIVEy_h-RsmrsO_rPH2MnGRo3OqRZ9HyWvgFcig1wMaDqhOvgYA9ZyHWyjwpdkIEfLciV_LMvRspwsD6nTbUG_bND8ZX61DsDFBOCw5sZikFFbHI3Y0Yg03v5b8A0QFY_d</recordid><startdate>202401</startdate><enddate>202401</enddate><creator>Cai, Chongpeng</creator><creator>Hu, Tingdan</creator><creator>Rong, Zening</creator><creator>Gong, Jing</creator><creator>Tong, Tong</creator><general>Elsevier B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-9180-8181</orcidid></search><sort><creationdate>202401</creationdate><title>Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer</title><author>Cai, Chongpeng ; Hu, Tingdan ; Rong, Zening ; Gong, Jing ; Tong, Tong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c309t-529e2738655f607b08fe7a9c2329da64083d2afb6442deaedfa54427737a64083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Magnetic resonance imaging</topic><topic>Predictive model</topic><topic>Radiomics</topic><topic>Rectal neoplasms</topic><topic>Tumour-stroma ratio</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cai, Chongpeng</creatorcontrib><creatorcontrib>Hu, Tingdan</creatorcontrib><creatorcontrib>Rong, Zening</creatorcontrib><creatorcontrib>Gong, Jing</creatorcontrib><creatorcontrib>Tong, Tong</creatorcontrib><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>Cai, Chongpeng</au><au>Hu, Tingdan</au><au>Rong, Zening</au><au>Gong, Jing</au><au>Tong, Tong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer</atitle><jtitle>European journal of radiology</jtitle><addtitle>Eur J Radiol</addtitle><date>2024-01</date><risdate>2024</risdate><volume>170</volume><spage>111254</spage><epage>111254</epage><pages>111254-111254</pages><artnum>111254</artnum><issn>0720-048X</issn><eissn>1872-7727</eissn><abstract>•Radiomics models based on patients with rectal cancer receiving surgery alone for tumour-stroma ratio evaluation can be applied in patients with locally advanced rectal cancer treated with neoadjuvant therapy, which always damages the tumor microenvironment, resulting in a incapableness of accurate tumour-stroma ratio evaluation.•Radiomics model based on high-resolution T2WI and clinical-radiomics model for tumour-stroma ratio evaluation can predict DFS and OS in patients with locally advanced rectal cancer treated with neoadjuvant therapy. To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. A total of 178 patients (mean age: 59.35, range 20–85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients’ pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23–74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan–Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077–2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281–4.396). We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>38091662</pmid><doi>10.1016/j.ejrad.2023.111254</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-9180-8181</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0720-048X
ispartof European journal of radiology, 2024-01, Vol.170, p.111254-111254, Article 111254
issn 0720-048X
1872-7727
language eng
recordid cdi_proquest_miscellaneous_2902974057
source Elsevier ScienceDirect Journals Complete
subjects Magnetic resonance imaging
Predictive model
Radiomics
Rectal neoplasms
Tumour-stroma ratio
title Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T10%3A30%3A10IST&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=Prognostic%20prediction%20value%20of%20the%20clinical-radiomics%20tumour-stroma%20ratio%20in%20locally%20advanced%20rectal%20cancer&rft.jtitle=European%20journal%20of%20radiology&rft.au=Cai,%20Chongpeng&rft.date=2024-01&rft.volume=170&rft.spage=111254&rft.epage=111254&rft.pages=111254-111254&rft.artnum=111254&rft.issn=0720-048X&rft.eissn=1872-7727&rft_id=info:doi/10.1016/j.ejrad.2023.111254&rft_dat=%3Cproquest_cross%3E2902974057%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=2902974057&rft_id=info:pmid/38091662&rft_els_id=S0720048X23005685&rfr_iscdi=true