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Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study
Background To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases. Methods A total of 96 patients with gastric cancer who were also diagnosed with l...
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| Published in: | Discover. Oncology 2024-08, Vol.15 (1), p.363-11 |
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| container_title | Discover. Oncology |
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| creator | Yu, Heng Jiang, Hang Lu, Xiaofeng Bai, Chunhua Song, Peng Sun, Feng Ai, Shichao Yin, Yi Hu, Qiongyuan Liu, Song Chen, Xin Du, Junfeng Shen, Xiaofei Guan, Wenxian |
| description | Background
To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases.
Methods
A total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts.
Results
Based on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P |
| doi_str_mv | 10.1007/s12672-024-01246-z |
| format | article |
| fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_43a779fd0e8a4b9798d8bc4cdc23cdda</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_43a779fd0e8a4b9798d8bc4cdc23cdda</doaj_id><sourcerecordid>3095321685</sourcerecordid><originalsourceid>FETCH-LOGICAL-d379t-e3bd812d7da89d96f557056beaacc1d5b325147635c06fcf8abf7e68f23898373</originalsourceid><addsrcrecordid>eNpdkttqFTEUhgdRbKl9AS8k4I03ozlMTt5IKR4KBW_0OmRy2M12JtkmmZbdF_C1ze5UbYVAkrX-9bEW6--6lwi-RRDydwVhxnEP8dBDhAfW3z7pjjEnsGcQoacP3kfdaSlbCCGmiBBIn3dHRKJWTIfj7tdZ1NO-hAKSBzmUH8BrU1MuwKcMpnDtMphd1aWdJgoR7HQNLtYCbkK9ApuWycEAo6NpUh0tMCm22GJqSPFA3WVnw_qbk3XTe3AG5mWqwTRMqyl1sfsX3TOvp-JO7--T7vunj9_Ov_SXXz9fnJ9d9pZwWXtHRisQttxqIa1knlIOKRud1sYgS0fShhw4I9RA5o0XevTcMeExEVIQTk66i5Vrk96qXQ6zznuVdFB3gZQ3SufW2uTUQDTn0lvohB5GyaWwYjSDsQYTY61urA8ra7eMs7OHcbKeHkEfZ2K4Upt0rVDbg4QQNcKbe0JOPxdXqppDMW6adHRpKYpASRknkh6kr_-TbtOS2_JWFcGICdpUrx629LeXPwtvArIKSkvFjcv_MAiqg7PU6izVnKXunKVuyW_cmcJD</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3095321685</pqid></control><display><type>article</type><title>Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Yu, Heng ; Jiang, Hang ; Lu, Xiaofeng ; Bai, Chunhua ; Song, Peng ; Sun, Feng ; Ai, Shichao ; Yin, Yi ; Hu, Qiongyuan ; Liu, Song ; Chen, Xin ; Du, Junfeng ; Shen, Xiaofei ; Guan, Wenxian</creator><creatorcontrib>Yu, Heng ; Jiang, Hang ; Lu, Xiaofeng ; Bai, Chunhua ; Song, Peng ; Sun, Feng ; Ai, Shichao ; Yin, Yi ; Hu, Qiongyuan ; Liu, Song ; Chen, Xin ; Du, Junfeng ; Shen, Xiaofei ; Guan, Wenxian</creatorcontrib><description>Background
To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases.
Methods
A total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts.
Results
Based on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P < 0.001) were independent risk factors for liver metastasis in patients with gastric cancer, and Nomogram was established based on this analysis (C-statistics = 0.911, 95%CI 0.880–0.958), and the C-statistics of the external validation cohorts achieved 0.926. ROC analysis and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic value than single variety.
Conclusions
By innovatively introducing a new tumor location classification method, systemic inflammatory response indicators such as NLR and PLR, and nutritional index OPNI, the risk factors of gastric cancer liver metastasis were determined and a predictive Nomogram model was established, which can provide clinical prediction for patients with gastric cancer liver metastasis.</description><identifier>ISSN: 2730-6011</identifier><identifier>EISSN: 2730-6011</identifier><identifier>DOI: 10.1007/s12672-024-01246-z</identifier><identifier>PMID: 39167254</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Antigens ; Cancer Research ; Cancer therapies ; Carbohydrates ; Chemotherapy ; Gastric cancer ; Internal Medicine ; Liver ; Liver metastases ; Medical prognosis ; Medicine ; Medicine & Public Health ; Metastasis ; Molecular Medicine ; Nomograms ; Oncology ; Prediction model ; Radiotherapy ; Risk factors ; Surgery ; Surgical Oncology ; Surgical outcomes</subject><ispartof>Discover. Oncology, 2024-08, Vol.15 (1), p.363-11</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/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) 2024 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-d379t-e3bd812d7da89d96f557056beaacc1d5b325147635c06fcf8abf7e68f23898373</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3095321685/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3095321685?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39167254$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yu, Heng</creatorcontrib><creatorcontrib>Jiang, Hang</creatorcontrib><creatorcontrib>Lu, Xiaofeng</creatorcontrib><creatorcontrib>Bai, Chunhua</creatorcontrib><creatorcontrib>Song, Peng</creatorcontrib><creatorcontrib>Sun, Feng</creatorcontrib><creatorcontrib>Ai, Shichao</creatorcontrib><creatorcontrib>Yin, Yi</creatorcontrib><creatorcontrib>Hu, Qiongyuan</creatorcontrib><creatorcontrib>Liu, Song</creatorcontrib><creatorcontrib>Chen, Xin</creatorcontrib><creatorcontrib>Du, Junfeng</creatorcontrib><creatorcontrib>Shen, Xiaofei</creatorcontrib><creatorcontrib>Guan, Wenxian</creatorcontrib><title>Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study</title><title>Discover. Oncology</title><addtitle>Discov Onc</addtitle><addtitle>Discov Oncol</addtitle><description>Background
To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases.
Methods
A total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts.
Results
Based on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P < 0.001) were independent risk factors for liver metastasis in patients with gastric cancer, and Nomogram was established based on this analysis (C-statistics = 0.911, 95%CI 0.880–0.958), and the C-statistics of the external validation cohorts achieved 0.926. ROC analysis and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic value than single variety.
Conclusions
By innovatively introducing a new tumor location classification method, systemic inflammatory response indicators such as NLR and PLR, and nutritional index OPNI, the risk factors of gastric cancer liver metastasis were determined and a predictive Nomogram model was established, which can provide clinical prediction for patients with gastric cancer liver metastasis.</description><subject>Antigens</subject><subject>Cancer Research</subject><subject>Cancer therapies</subject><subject>Carbohydrates</subject><subject>Chemotherapy</subject><subject>Gastric cancer</subject><subject>Internal Medicine</subject><subject>Liver</subject><subject>Liver metastases</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastasis</subject><subject>Molecular Medicine</subject><subject>Nomograms</subject><subject>Oncology</subject><subject>Prediction model</subject><subject>Radiotherapy</subject><subject>Risk factors</subject><subject>Surgery</subject><subject>Surgical Oncology</subject><subject>Surgical outcomes</subject><issn>2730-6011</issn><issn>2730-6011</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkttqFTEUhgdRbKl9AS8k4I03ozlMTt5IKR4KBW_0OmRy2M12JtkmmZbdF_C1ze5UbYVAkrX-9bEW6--6lwi-RRDydwVhxnEP8dBDhAfW3z7pjjEnsGcQoacP3kfdaSlbCCGmiBBIn3dHRKJWTIfj7tdZ1NO-hAKSBzmUH8BrU1MuwKcMpnDtMphd1aWdJgoR7HQNLtYCbkK9ApuWycEAo6NpUh0tMCm22GJqSPFA3WVnw_qbk3XTe3AG5mWqwTRMqyl1sfsX3TOvp-JO7--T7vunj9_Ov_SXXz9fnJ9d9pZwWXtHRisQttxqIa1knlIOKRud1sYgS0fShhw4I9RA5o0XevTcMeExEVIQTk66i5Vrk96qXQ6zznuVdFB3gZQ3SufW2uTUQDTn0lvohB5GyaWwYjSDsQYTY61urA8ra7eMs7OHcbKeHkEfZ2K4Upt0rVDbg4QQNcKbe0JOPxdXqppDMW6adHRpKYpASRknkh6kr_-TbtOS2_JWFcGICdpUrx629LeXPwtvArIKSkvFjcv_MAiqg7PU6izVnKXunKVuyW_cmcJD</recordid><startdate>20240821</startdate><enddate>20240821</enddate><creator>Yu, Heng</creator><creator>Jiang, Hang</creator><creator>Lu, Xiaofeng</creator><creator>Bai, Chunhua</creator><creator>Song, Peng</creator><creator>Sun, Feng</creator><creator>Ai, Shichao</creator><creator>Yin, Yi</creator><creator>Hu, Qiongyuan</creator><creator>Liu, Song</creator><creator>Chen, Xin</creator><creator>Du, Junfeng</creator><creator>Shen, Xiaofei</creator><creator>Guan, Wenxian</creator><general>Springer US</general><general>Springer Nature B.V</general><general>Springer</general><scope>C6C</scope><scope>NPM</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>NAPCQ</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></search><sort><creationdate>20240821</creationdate><title>Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study</title><author>Yu, Heng ; Jiang, Hang ; Lu, Xiaofeng ; Bai, Chunhua ; Song, Peng ; Sun, Feng ; Ai, Shichao ; Yin, Yi ; Hu, Qiongyuan ; Liu, Song ; Chen, Xin ; Du, Junfeng ; Shen, Xiaofei ; Guan, Wenxian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d379t-e3bd812d7da89d96f557056beaacc1d5b325147635c06fcf8abf7e68f23898373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Antigens</topic><topic>Cancer Research</topic><topic>Cancer therapies</topic><topic>Carbohydrates</topic><topic>Chemotherapy</topic><topic>Gastric cancer</topic><topic>Internal Medicine</topic><topic>Liver</topic><topic>Liver metastases</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastasis</topic><topic>Molecular Medicine</topic><topic>Nomograms</topic><topic>Oncology</topic><topic>Prediction model</topic><topic>Radiotherapy</topic><topic>Risk factors</topic><topic>Surgery</topic><topic>Surgical Oncology</topic><topic>Surgical outcomes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Heng</creatorcontrib><creatorcontrib>Jiang, Hang</creatorcontrib><creatorcontrib>Lu, Xiaofeng</creatorcontrib><creatorcontrib>Bai, Chunhua</creatorcontrib><creatorcontrib>Song, Peng</creatorcontrib><creatorcontrib>Sun, Feng</creatorcontrib><creatorcontrib>Ai, Shichao</creatorcontrib><creatorcontrib>Yin, Yi</creatorcontrib><creatorcontrib>Hu, Qiongyuan</creatorcontrib><creatorcontrib>Liu, Song</creatorcontrib><creatorcontrib>Chen, Xin</creatorcontrib><creatorcontrib>Du, Junfeng</creatorcontrib><creatorcontrib>Shen, Xiaofei</creatorcontrib><creatorcontrib>Guan, Wenxian</creatorcontrib><collection>Springer_OA刊</collection><collection>PubMed</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</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>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Nursing & Allied Health Premium</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>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>Discover. Oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Heng</au><au>Jiang, Hang</au><au>Lu, Xiaofeng</au><au>Bai, Chunhua</au><au>Song, Peng</au><au>Sun, Feng</au><au>Ai, Shichao</au><au>Yin, Yi</au><au>Hu, Qiongyuan</au><au>Liu, Song</au><au>Chen, Xin</au><au>Du, Junfeng</au><au>Shen, Xiaofei</au><au>Guan, Wenxian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study</atitle><jtitle>Discover. Oncology</jtitle><stitle>Discov Onc</stitle><addtitle>Discov Oncol</addtitle><date>2024-08-21</date><risdate>2024</risdate><volume>15</volume><issue>1</issue><spage>363</spage><epage>11</epage><pages>363-11</pages><issn>2730-6011</issn><eissn>2730-6011</eissn><abstract>Background
To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases.
Methods
A total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts.
Results
Based on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P < 0.001) were independent risk factors for liver metastasis in patients with gastric cancer, and Nomogram was established based on this analysis (C-statistics = 0.911, 95%CI 0.880–0.958), and the C-statistics of the external validation cohorts achieved 0.926. ROC analysis and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic value than single variety.
Conclusions
By innovatively introducing a new tumor location classification method, systemic inflammatory response indicators such as NLR and PLR, and nutritional index OPNI, the risk factors of gastric cancer liver metastasis were determined and a predictive Nomogram model was established, which can provide clinical prediction for patients with gastric cancer liver metastasis.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>39167254</pmid><doi>10.1007/s12672-024-01246-z</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Antigens Cancer Research Cancer therapies Carbohydrates Chemotherapy Gastric cancer Internal Medicine Liver Liver metastases Medical prognosis Medicine Medicine & Public Health Metastasis Molecular Medicine Nomograms Oncology Prediction model Radiotherapy Risk factors Surgery Surgical Oncology Surgical outcomes |
| title | Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study |
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