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Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab
Early evaluation of the efficacy of first-line chemotherapy combined with bevacizumab in patients with colorectal cancer liver metastasis (CRLM) remains challenging. This study used 2-month post-chemotherapy spectral computed tomography (CT) to predict the overall survival (OS) and response of CRLM...
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Published in: | Cancer imaging 2023-03, Vol.23 (1), p.30-30, Article 30 |
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creator | Li, Shenglin Yuan, Long Yue, Mengying Xu, Yuan Liu, Suwei Wang, Feng Liu, Xiaoqin Wang, Fengyan Deng, Juan Sun, Qiu Liu, Xianwang Xue, Caiqiang Lu, Ting Zhang, Wenjuan Zhou, Junlin |
description | Early evaluation of the efficacy of first-line chemotherapy combined with bevacizumab in patients with colorectal cancer liver metastasis (CRLM) remains challenging. This study used 2-month post-chemotherapy spectral computed tomography (CT) to predict the overall survival (OS) and response of CRLM patients with bevacizumab-containing therapy.
This retrospective analysis was performed in 104 patients with pathologically confirmed CRLM between April 2017 and October 2021. Patients were treated with 5-fluorouracil, leucovorin, oxaliplatin or irinotecan with bevacizumab. Portal venous phase spectral CT was performed on the target liver lesion within 2 months of commencing chemotherapy to demonstrate the iodine concentration (IoD) of the target liver lesion. The patients were classified as responders (R +) or non-responders (R -) according to the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 at 6 months. Multivariate analysis was performed to determine the relationships of the spectral CT parameters, tumor markers, morphology of target lesions with OS and response. The differences in portal venous phase spectral CT parameters between the R + and R - groups were analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of spectral CT parameters.
Of the 104 patients (mean age ± standard deviation: 57.73 years ± 12.56; 60 men) evaluated, 28 (26.9%) were classified as R + . Cox multivariate analysis identified the iodine concentration (hazard ratio [HR]: 1.238; 95% confidence interval [95% CI]: 1.089-1.408; P |
doi_str_mv | 10.1186/s40644-023-00547-w |
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fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_4da1945126634477be524cd3c82731c8</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A742978096</galeid><doaj_id>oai_doaj_org_article_4da1945126634477be524cd3c82731c8</doaj_id><sourcerecordid>A742978096</sourcerecordid><originalsourceid>FETCH-LOGICAL-c595t-5a29180a054553fd633a53fc86cbc11156eff71080d9b96dba3b25d0fc44e2e63</originalsourceid><addsrcrecordid>eNptkt1qFDEAhQdRbK2-gBcSEIo3U_OfyZWU0tWFhQWp4F3IJJndLDOTNclsqa_hC5vt1toVmYGE5JwvyeFU1VsELxBq-MdEIae0hpjUEDIq6ttn1SmiAtaCEPj8yfykepXSBkIsGyleVieES045EqfVr2sd-zvgdrqfdPZhBKEDvd-5CAaXdSq_T2BKflyBtHUmR92DqxuQA9hGZ73JIEzZhMEBP4JtQbgxJ3Dr8xqY0IdYLMVh9GgKMkens7OH7dlyMVt-n3-dAz1a0JYrGP9zGnT7unrR6T65Nw_jWfVtdn1z9aVeLD_Pry4XtWGS5ZppLFEDdXk6Y6SznBBdRtNw0xqEEOOu6wSCDbSyldy2mrSYWdgZSh12nJxV8wPXBr1R2-gHHe9U0F7dL4S4Ujpmb3qnqNVIUoYw54RSIVrHMDWWmAYLgkxTWJ8OrO3UDs6akkJJ6gh6vDP6tVqFnUIQElnAhfDhgRDDj8mlrAafjOt7PbowJYWFREQwRGmRvv9HuglTHEtWCjeQQMYgQX9VK11e4MculIPNHqouBcVSNFDuQ7j4j6p81g3ehNF1vqwfGc6fGNZO93mdQj_ty5OOhfggNDGkFF33mAaCal9gdSiwKgVW9wVWt8X07mmOj5Y_jSW_AQdW6zg</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2803055031</pqid></control><display><type>article</type><title>Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Li, Shenglin ; Yuan, Long ; Yue, Mengying ; Xu, Yuan ; Liu, Suwei ; Wang, Feng ; Liu, Xiaoqin ; Wang, Fengyan ; Deng, Juan ; Sun, Qiu ; Liu, Xianwang ; Xue, Caiqiang ; Lu, Ting ; Zhang, Wenjuan ; Zhou, Junlin</creator><creatorcontrib>Li, Shenglin ; Yuan, Long ; Yue, Mengying ; Xu, Yuan ; Liu, Suwei ; Wang, Feng ; Liu, Xiaoqin ; Wang, Fengyan ; Deng, Juan ; Sun, Qiu ; Liu, Xianwang ; Xue, Caiqiang ; Lu, Ting ; Zhang, Wenjuan ; Zhou, Junlin</creatorcontrib><description>Early evaluation of the efficacy of first-line chemotherapy combined with bevacizumab in patients with colorectal cancer liver metastasis (CRLM) remains challenging. This study used 2-month post-chemotherapy spectral computed tomography (CT) to predict the overall survival (OS) and response of CRLM patients with bevacizumab-containing therapy.
This retrospective analysis was performed in 104 patients with pathologically confirmed CRLM between April 2017 and October 2021. Patients were treated with 5-fluorouracil, leucovorin, oxaliplatin or irinotecan with bevacizumab. Portal venous phase spectral CT was performed on the target liver lesion within 2 months of commencing chemotherapy to demonstrate the iodine concentration (IoD) of the target liver lesion. The patients were classified as responders (R +) or non-responders (R -) according to the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 at 6 months. Multivariate analysis was performed to determine the relationships of the spectral CT parameters, tumor markers, morphology of target lesions with OS and response. The differences in portal venous phase spectral CT parameters between the R + and R - groups were analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of spectral CT parameters.
Of the 104 patients (mean age ± standard deviation: 57.73 years ± 12.56; 60 men) evaluated, 28 (26.9%) were classified as R + . Cox multivariate analysis identified the iodine concentration (hazard ratio [HR]: 1.238; 95% confidence interval [95% CI]: 1.089-1.408; P < 0.001), baseline tumor longest diameter (BLD) (HR: 1.022; 95% CI: 1.005-1.038, P = 0.010), higher baseline CEA (HR: 1.670; 95% CI: 1.016-2.745, P = 0.043), K-RAS mutation (HR: 2.027; 95% CI: 1.192-3.449; P = 0.009), and metachronous liver metastasis (HR: 1.877; 95% CI: 1.179-2.988; P = 0.008) as independent risk factors for patient OS. Logistic multivariate analysis identified the IoD (Odds Ratio [OR]: 2.243; 95% CI: 1.405-4.098; P = 0.002) and clinical N stage of the primary tumor (OR: 4.998; 95% CI: 1.210-25.345; P = 0.035) as independent predictor of R + . Using IoD cutoff values of 4.75 (100ug/cm
) the area under the ROC curve was 0.916, sensitivity and specificity were 80.3% and 96.4%, respectively.
Spectral CT IoD can predict the OS and response of patients with CRLM after 2 months of treatment with bevacizumab-containing therapy.</description><identifier>ISSN: 1470-7330</identifier><identifier>ISSN: 1740-5025</identifier><identifier>EISSN: 1470-7330</identifier><identifier>DOI: 10.1186/s40644-023-00547-w</identifier><identifier>PMID: 36964617</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Abdomen ; Analysis ; Antineoplastic Combined Chemotherapy Protocols - therapeutic use ; Bevacizumab ; Bevacizumab - therapeutic use ; Biomarkers ; Cancer ; Cancer therapies ; Chemotherapy ; Clinical medicine ; Colorectal cancer ; Colorectal Neoplasms - pathology ; Computed tomography ; CT imaging ; Efficacy evaluation ; Evaluation ; Fluorouracil ; FOLFOXIRI ; Growth factors ; Hospitals ; Humans ; Iodine ; Leucovorin ; Liver ; Liver metastasis ; Liver Neoplasms - diagnostic imaging ; Liver Neoplasms - drug therapy ; Liver Neoplasms - secondary ; Male ; Medical imaging ; Medical prognosis ; Medical research ; Medicine, Experimental ; Metastasis ; Morphology ; Multivariate analysis ; Neoplasm Metastasis - drug therapy ; Parameters ; Patient outcomes ; Patients ; Prognosis ; Retrospective Studies ; Risk factors ; Spectral CT ; Tomography ; Tomography, X-Ray Computed - methods ; Tumors</subject><ispartof>Cancer imaging, 2023-03, Vol.23 (1), p.30-30, Article 30</ispartof><rights>2023. The Author(s).</rights><rights>COPYRIGHT 2023 BioMed Central Ltd.</rights><rights>2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c595t-5a29180a054553fd633a53fc86cbc11156eff71080d9b96dba3b25d0fc44e2e63</citedby><cites>FETCH-LOGICAL-c595t-5a29180a054553fd633a53fc86cbc11156eff71080d9b96dba3b25d0fc44e2e63</cites><orcidid>0000-0001-8109-6347</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/PMC10039512/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2803055031?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36964617$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Shenglin</creatorcontrib><creatorcontrib>Yuan, Long</creatorcontrib><creatorcontrib>Yue, Mengying</creatorcontrib><creatorcontrib>Xu, Yuan</creatorcontrib><creatorcontrib>Liu, Suwei</creatorcontrib><creatorcontrib>Wang, Feng</creatorcontrib><creatorcontrib>Liu, Xiaoqin</creatorcontrib><creatorcontrib>Wang, Fengyan</creatorcontrib><creatorcontrib>Deng, Juan</creatorcontrib><creatorcontrib>Sun, Qiu</creatorcontrib><creatorcontrib>Liu, Xianwang</creatorcontrib><creatorcontrib>Xue, Caiqiang</creatorcontrib><creatorcontrib>Lu, Ting</creatorcontrib><creatorcontrib>Zhang, Wenjuan</creatorcontrib><creatorcontrib>Zhou, Junlin</creatorcontrib><title>Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab</title><title>Cancer imaging</title><addtitle>Cancer Imaging</addtitle><description>Early evaluation of the efficacy of first-line chemotherapy combined with bevacizumab in patients with colorectal cancer liver metastasis (CRLM) remains challenging. This study used 2-month post-chemotherapy spectral computed tomography (CT) to predict the overall survival (OS) and response of CRLM patients with bevacizumab-containing therapy.
This retrospective analysis was performed in 104 patients with pathologically confirmed CRLM between April 2017 and October 2021. Patients were treated with 5-fluorouracil, leucovorin, oxaliplatin or irinotecan with bevacizumab. Portal venous phase spectral CT was performed on the target liver lesion within 2 months of commencing chemotherapy to demonstrate the iodine concentration (IoD) of the target liver lesion. The patients were classified as responders (R +) or non-responders (R -) according to the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 at 6 months. Multivariate analysis was performed to determine the relationships of the spectral CT parameters, tumor markers, morphology of target lesions with OS and response. The differences in portal venous phase spectral CT parameters between the R + and R - groups were analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of spectral CT parameters.
Of the 104 patients (mean age ± standard deviation: 57.73 years ± 12.56; 60 men) evaluated, 28 (26.9%) were classified as R + . Cox multivariate analysis identified the iodine concentration (hazard ratio [HR]: 1.238; 95% confidence interval [95% CI]: 1.089-1.408; P < 0.001), baseline tumor longest diameter (BLD) (HR: 1.022; 95% CI: 1.005-1.038, P = 0.010), higher baseline CEA (HR: 1.670; 95% CI: 1.016-2.745, P = 0.043), K-RAS mutation (HR: 2.027; 95% CI: 1.192-3.449; P = 0.009), and metachronous liver metastasis (HR: 1.877; 95% CI: 1.179-2.988; P = 0.008) as independent risk factors for patient OS. Logistic multivariate analysis identified the IoD (Odds Ratio [OR]: 2.243; 95% CI: 1.405-4.098; P = 0.002) and clinical N stage of the primary tumor (OR: 4.998; 95% CI: 1.210-25.345; P = 0.035) as independent predictor of R + . Using IoD cutoff values of 4.75 (100ug/cm
) the area under the ROC curve was 0.916, sensitivity and specificity were 80.3% and 96.4%, respectively.
Spectral CT IoD can predict the OS and response of patients with CRLM after 2 months of treatment with bevacizumab-containing therapy.</description><subject>Abdomen</subject><subject>Analysis</subject><subject>Antineoplastic Combined Chemotherapy Protocols - therapeutic use</subject><subject>Bevacizumab</subject><subject>Bevacizumab - therapeutic use</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Chemotherapy</subject><subject>Clinical medicine</subject><subject>Colorectal cancer</subject><subject>Colorectal Neoplasms - pathology</subject><subject>Computed tomography</subject><subject>CT imaging</subject><subject>Efficacy evaluation</subject><subject>Evaluation</subject><subject>Fluorouracil</subject><subject>FOLFOXIRI</subject><subject>Growth factors</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Iodine</subject><subject>Leucovorin</subject><subject>Liver</subject><subject>Liver metastasis</subject><subject>Liver Neoplasms - diagnostic imaging</subject><subject>Liver Neoplasms - drug therapy</subject><subject>Liver Neoplasms - secondary</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medical prognosis</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Metastasis</subject><subject>Morphology</subject><subject>Multivariate analysis</subject><subject>Neoplasm Metastasis - drug therapy</subject><subject>Parameters</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Retrospective Studies</subject><subject>Risk factors</subject><subject>Spectral CT</subject><subject>Tomography</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Tumors</subject><issn>1470-7330</issn><issn>1740-5025</issn><issn>1470-7330</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkt1qFDEAhQdRbK2-gBcSEIo3U_OfyZWU0tWFhQWp4F3IJJndLDOTNclsqa_hC5vt1toVmYGE5JwvyeFU1VsELxBq-MdEIae0hpjUEDIq6ttn1SmiAtaCEPj8yfykepXSBkIsGyleVieES045EqfVr2sd-zvgdrqfdPZhBKEDvd-5CAaXdSq_T2BKflyBtHUmR92DqxuQA9hGZ73JIEzZhMEBP4JtQbgxJ3Dr8xqY0IdYLMVh9GgKMkens7OH7dlyMVt-n3-dAz1a0JYrGP9zGnT7unrR6T65Nw_jWfVtdn1z9aVeLD_Pry4XtWGS5ZppLFEDdXk6Y6SznBBdRtNw0xqEEOOu6wSCDbSyldy2mrSYWdgZSh12nJxV8wPXBr1R2-gHHe9U0F7dL4S4Ujpmb3qnqNVIUoYw54RSIVrHMDWWmAYLgkxTWJ8OrO3UDs6akkJJ6gh6vDP6tVqFnUIQElnAhfDhgRDDj8mlrAafjOt7PbowJYWFREQwRGmRvv9HuglTHEtWCjeQQMYgQX9VK11e4MculIPNHqouBcVSNFDuQ7j4j6p81g3ehNF1vqwfGc6fGNZO93mdQj_ty5OOhfggNDGkFF33mAaCal9gdSiwKgVW9wVWt8X07mmOj5Y_jSW_AQdW6zg</recordid><startdate>20230324</startdate><enddate>20230324</enddate><creator>Li, Shenglin</creator><creator>Yuan, Long</creator><creator>Yue, Mengying</creator><creator>Xu, Yuan</creator><creator>Liu, Suwei</creator><creator>Wang, Feng</creator><creator>Liu, Xiaoqin</creator><creator>Wang, Fengyan</creator><creator>Deng, Juan</creator><creator>Sun, Qiu</creator><creator>Liu, Xianwang</creator><creator>Xue, Caiqiang</creator><creator>Lu, Ting</creator><creator>Zhang, Wenjuan</creator><creator>Zhou, Junlin</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8109-6347</orcidid></search><sort><creationdate>20230324</creationdate><title>Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab</title><author>Li, Shenglin ; Yuan, Long ; Yue, Mengying ; Xu, Yuan ; Liu, Suwei ; Wang, Feng ; Liu, Xiaoqin ; Wang, Fengyan ; Deng, Juan ; Sun, Qiu ; Liu, Xianwang ; Xue, Caiqiang ; Lu, Ting ; Zhang, Wenjuan ; Zhou, Junlin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c595t-5a29180a054553fd633a53fc86cbc11156eff71080d9b96dba3b25d0fc44e2e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Abdomen</topic><topic>Analysis</topic><topic>Antineoplastic Combined Chemotherapy Protocols - therapeutic use</topic><topic>Bevacizumab</topic><topic>Bevacizumab - therapeutic use</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Chemotherapy</topic><topic>Clinical medicine</topic><topic>Colorectal cancer</topic><topic>Colorectal Neoplasms - pathology</topic><topic>Computed tomography</topic><topic>CT imaging</topic><topic>Efficacy evaluation</topic><topic>Evaluation</topic><topic>Fluorouracil</topic><topic>FOLFOXIRI</topic><topic>Growth factors</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Iodine</topic><topic>Leucovorin</topic><topic>Liver</topic><topic>Liver metastasis</topic><topic>Liver Neoplasms - diagnostic imaging</topic><topic>Liver Neoplasms - drug therapy</topic><topic>Liver Neoplasms - secondary</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Medical prognosis</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Metastasis</topic><topic>Morphology</topic><topic>Multivariate analysis</topic><topic>Neoplasm Metastasis - drug therapy</topic><topic>Parameters</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Prognosis</topic><topic>Retrospective Studies</topic><topic>Risk factors</topic><topic>Spectral CT</topic><topic>Tomography</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Shenglin</creatorcontrib><creatorcontrib>Yuan, Long</creatorcontrib><creatorcontrib>Yue, Mengying</creatorcontrib><creatorcontrib>Xu, Yuan</creatorcontrib><creatorcontrib>Liu, Suwei</creatorcontrib><creatorcontrib>Wang, Feng</creatorcontrib><creatorcontrib>Liu, Xiaoqin</creatorcontrib><creatorcontrib>Wang, Fengyan</creatorcontrib><creatorcontrib>Deng, Juan</creatorcontrib><creatorcontrib>Sun, Qiu</creatorcontrib><creatorcontrib>Liu, Xianwang</creatorcontrib><creatorcontrib>Xue, Caiqiang</creatorcontrib><creatorcontrib>Lu, Ting</creatorcontrib><creatorcontrib>Zhang, Wenjuan</creatorcontrib><creatorcontrib>Zhou, Junlin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Cancer imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Shenglin</au><au>Yuan, Long</au><au>Yue, Mengying</au><au>Xu, Yuan</au><au>Liu, Suwei</au><au>Wang, Feng</au><au>Liu, Xiaoqin</au><au>Wang, Fengyan</au><au>Deng, Juan</au><au>Sun, Qiu</au><au>Liu, Xianwang</au><au>Xue, Caiqiang</au><au>Lu, Ting</au><au>Zhang, Wenjuan</au><au>Zhou, Junlin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab</atitle><jtitle>Cancer imaging</jtitle><addtitle>Cancer Imaging</addtitle><date>2023-03-24</date><risdate>2023</risdate><volume>23</volume><issue>1</issue><spage>30</spage><epage>30</epage><pages>30-30</pages><artnum>30</artnum><issn>1470-7330</issn><issn>1740-5025</issn><eissn>1470-7330</eissn><abstract>Early evaluation of the efficacy of first-line chemotherapy combined with bevacizumab in patients with colorectal cancer liver metastasis (CRLM) remains challenging. This study used 2-month post-chemotherapy spectral computed tomography (CT) to predict the overall survival (OS) and response of CRLM patients with bevacizumab-containing therapy.
This retrospective analysis was performed in 104 patients with pathologically confirmed CRLM between April 2017 and October 2021. Patients were treated with 5-fluorouracil, leucovorin, oxaliplatin or irinotecan with bevacizumab. Portal venous phase spectral CT was performed on the target liver lesion within 2 months of commencing chemotherapy to demonstrate the iodine concentration (IoD) of the target liver lesion. The patients were classified as responders (R +) or non-responders (R -) according to the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 at 6 months. Multivariate analysis was performed to determine the relationships of the spectral CT parameters, tumor markers, morphology of target lesions with OS and response. The differences in portal venous phase spectral CT parameters between the R + and R - groups were analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of spectral CT parameters.
Of the 104 patients (mean age ± standard deviation: 57.73 years ± 12.56; 60 men) evaluated, 28 (26.9%) were classified as R + . Cox multivariate analysis identified the iodine concentration (hazard ratio [HR]: 1.238; 95% confidence interval [95% CI]: 1.089-1.408; P < 0.001), baseline tumor longest diameter (BLD) (HR: 1.022; 95% CI: 1.005-1.038, P = 0.010), higher baseline CEA (HR: 1.670; 95% CI: 1.016-2.745, P = 0.043), K-RAS mutation (HR: 2.027; 95% CI: 1.192-3.449; P = 0.009), and metachronous liver metastasis (HR: 1.877; 95% CI: 1.179-2.988; P = 0.008) as independent risk factors for patient OS. Logistic multivariate analysis identified the IoD (Odds Ratio [OR]: 2.243; 95% CI: 1.405-4.098; P = 0.002) and clinical N stage of the primary tumor (OR: 4.998; 95% CI: 1.210-25.345; P = 0.035) as independent predictor of R + . Using IoD cutoff values of 4.75 (100ug/cm
) the area under the ROC curve was 0.916, sensitivity and specificity were 80.3% and 96.4%, respectively.
Spectral CT IoD can predict the OS and response of patients with CRLM after 2 months of treatment with bevacizumab-containing therapy.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>36964617</pmid><doi>10.1186/s40644-023-00547-w</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-8109-6347</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1470-7330 |
ispartof | Cancer imaging, 2023-03, Vol.23 (1), p.30-30, Article 30 |
issn | 1470-7330 1740-5025 1470-7330 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_4da1945126634477be524cd3c82731c8 |
source | Publicly Available Content Database; PubMed Central |
subjects | Abdomen Analysis Antineoplastic Combined Chemotherapy Protocols - therapeutic use Bevacizumab Bevacizumab - therapeutic use Biomarkers Cancer Cancer therapies Chemotherapy Clinical medicine Colorectal cancer Colorectal Neoplasms - pathology Computed tomography CT imaging Efficacy evaluation Evaluation Fluorouracil FOLFOXIRI Growth factors Hospitals Humans Iodine Leucovorin Liver Liver metastasis Liver Neoplasms - diagnostic imaging Liver Neoplasms - drug therapy Liver Neoplasms - secondary Male Medical imaging Medical prognosis Medical research Medicine, Experimental Metastasis Morphology Multivariate analysis Neoplasm Metastasis - drug therapy Parameters Patient outcomes Patients Prognosis Retrospective Studies Risk factors Spectral CT Tomography Tomography, X-Ray Computed - methods Tumors |
title | Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T02%3A05%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Early%20evaluation%20of%20liver%20metastasis%20using%20spectral%20CT%20to%20predict%20outcome%20in%20patients%20with%20colorectal%20cancer%20treated%20with%20FOLFOXIRI%20and%20bevacizumab&rft.jtitle=Cancer%20imaging&rft.au=Li,%20Shenglin&rft.date=2023-03-24&rft.volume=23&rft.issue=1&rft.spage=30&rft.epage=30&rft.pages=30-30&rft.artnum=30&rft.issn=1470-7330&rft.eissn=1470-7330&rft_id=info:doi/10.1186/s40644-023-00547-w&rft_dat=%3Cgale_doaj_%3EA742978096%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c595t-5a29180a054553fd633a53fc86cbc11156eff71080d9b96dba3b25d0fc44e2e63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2803055031&rft_id=info:pmid/36964617&rft_galeid=A742978096&rfr_iscdi=true |