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Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study
Hepato-pulmonary metastasis of colorectal cancer (CRC) is a rare disease with poor prognosis. This study aims to establish a highly efficient nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer hepato-pulmonary metastasis (CRCHPM). We...
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Published in: | BMC cancer 2022-06, Vol.22 (1), p.643-13, Article 643 |
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description | Hepato-pulmonary metastasis of colorectal cancer (CRC) is a rare disease with poor prognosis. This study aims to establish a highly efficient nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer hepato-pulmonary metastasis (CRCHPM).
We retrospectively analyzed the data of patients with CRCHPM from SEER database and Wuhan Union Hospital Cancer Center (WUHCC). A total of 1250 CRCHPM patients were randomly assigned to the training, internal validation, and external validation cohorts from 2010 to 2016.Univariate and multivariate cox analysis were performed to identify independent clinicopathological predictors of OS and CSS, and a nomogram was constructed to predict OS and CSS in CRCHPM patients.
A nomogram of OS was constructed based on seven independent predictors of age, degree of differentiation, T stage, chemotherapy, number of lsampled lymph nodes, number of positive lymph nodes, and tumor size. Nomogram showed favorable sensitivity in predicting OS at 1, 3 and 5 years, with area under the receiver operating characteristic curve (AUROC) values of 0.802, 0.759 and 0.752 in the training cohort;0.814, 0.769 and 0.716 in the internal validation cohort;0.778, 0.756 and 0.753 in the external validation cohort, respectively. A nomogram of CSS was constructed based on three independent predictors of T stage, chemotherapy, and tumor size. The AUROC values of 1, 3 and 5 years were 0.709,0.588,0.686 in the training cohort; 0.751, 0.648,0.666 in the internal validation cohort;0.781,0.588,0.645 in the external validation cohort, respectively. Calibration curves, Concordance index (C-index), and decision curve analysis (DCA) results revealed that using our model to predict OS and CSS is more efficient than other single clinicopathological characteristics.
A nomogram of OS and CSS based on clinicopathological characteristics can be conveniently used to predict the prognosis of CRCHPM patients. |
doi_str_mv | 10.1186/s12885-022-09738-3 |
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We retrospectively analyzed the data of patients with CRCHPM from SEER database and Wuhan Union Hospital Cancer Center (WUHCC). A total of 1250 CRCHPM patients were randomly assigned to the training, internal validation, and external validation cohorts from 2010 to 2016.Univariate and multivariate cox analysis were performed to identify independent clinicopathological predictors of OS and CSS, and a nomogram was constructed to predict OS and CSS in CRCHPM patients.
A nomogram of OS was constructed based on seven independent predictors of age, degree of differentiation, T stage, chemotherapy, number of lsampled lymph nodes, number of positive lymph nodes, and tumor size. Nomogram showed favorable sensitivity in predicting OS at 1, 3 and 5 years, with area under the receiver operating characteristic curve (AUROC) values of 0.802, 0.759 and 0.752 in the training cohort;0.814, 0.769 and 0.716 in the internal validation cohort;0.778, 0.756 and 0.753 in the external validation cohort, respectively. A nomogram of CSS was constructed based on three independent predictors of T stage, chemotherapy, and tumor size. The AUROC values of 1, 3 and 5 years were 0.709,0.588,0.686 in the training cohort; 0.751, 0.648,0.666 in the internal validation cohort;0.781,0.588,0.645 in the external validation cohort, respectively. Calibration curves, Concordance index (C-index), and decision curve analysis (DCA) results revealed that using our model to predict OS and CSS is more efficient than other single clinicopathological characteristics.
A nomogram of OS and CSS based on clinicopathological characteristics can be conveniently used to predict the prognosis of CRCHPM patients.</description><identifier>ISSN: 1471-2407</identifier><identifier>EISSN: 1471-2407</identifier><identifier>DOI: 10.1186/s12885-022-09738-3</identifier><identifier>PMID: 35690752</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Calibration ; Cancer specific survival ; Care and treatment ; Chemotherapy ; Colorectal cancer ; Colorectal carcinoma ; Colorectal Neoplasms ; Complications and side effects ; Evaluation ; Health aspects ; Hepato-pulmonary metastasis ; Humans ; Liver ; Liver cancer ; Lung cancer ; Lung Neoplasms - therapy ; Lymph nodes ; Lymphatic system ; Medical prognosis ; Metastases ; Metastasis ; Neoplasm Staging ; Nomogram ; Nomograms ; Nomography (Mathematics) ; Overall survival ; Patient outcomes ; Patients ; Prognosis ; Radiation therapy ; Rare diseases ; Regression analysis ; Retrospective Studies ; SEER Program ; Statistical analysis ; Tumors ; Variables</subject><ispartof>BMC cancer, 2022-06, Vol.22 (1), p.643-13, Article 643</ispartof><rights>2022. The Author(s).</rights><rights>COPYRIGHT 2022 BioMed Central Ltd.</rights><rights>2022. 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) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c558t-b093a05af93fbb5b0b5c47fa845f3478ff51243ed80131a1c49653679af1c16f3</citedby><cites>FETCH-LOGICAL-c558t-b093a05af93fbb5b0b5c47fa845f3478ff51243ed80131a1c49653679af1c16f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188712/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2678211077?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25732,27903,27904,36991,36992,44569,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35690752$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Deng, Shenghe</creatorcontrib><creatorcontrib>Jiang, Zhenxing</creatorcontrib><creatorcontrib>Cao, Yinghao</creatorcontrib><creatorcontrib>Gu, Junnan</creatorcontrib><creatorcontrib>Mao, Fuwei</creatorcontrib><creatorcontrib>Xue, Yifan</creatorcontrib><creatorcontrib>Qin, Le</creatorcontrib><creatorcontrib>Liu, Ke</creatorcontrib><creatorcontrib>Wang, Jiliang</creatorcontrib><creatorcontrib>Wu, Ke</creatorcontrib><creatorcontrib>Cai, Kailin</creatorcontrib><title>Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study</title><title>BMC cancer</title><addtitle>BMC Cancer</addtitle><description>Hepato-pulmonary metastasis of colorectal cancer (CRC) is a rare disease with poor prognosis. This study aims to establish a highly efficient nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer hepato-pulmonary metastasis (CRCHPM).
We retrospectively analyzed the data of patients with CRCHPM from SEER database and Wuhan Union Hospital Cancer Center (WUHCC). A total of 1250 CRCHPM patients were randomly assigned to the training, internal validation, and external validation cohorts from 2010 to 2016.Univariate and multivariate cox analysis were performed to identify independent clinicopathological predictors of OS and CSS, and a nomogram was constructed to predict OS and CSS in CRCHPM patients.
A nomogram of OS was constructed based on seven independent predictors of age, degree of differentiation, T stage, chemotherapy, number of lsampled lymph nodes, number of positive lymph nodes, and tumor size. Nomogram showed favorable sensitivity in predicting OS at 1, 3 and 5 years, with area under the receiver operating characteristic curve (AUROC) values of 0.802, 0.759 and 0.752 in the training cohort;0.814, 0.769 and 0.716 in the internal validation cohort;0.778, 0.756 and 0.753 in the external validation cohort, respectively. A nomogram of CSS was constructed based on three independent predictors of T stage, chemotherapy, and tumor size. The AUROC values of 1, 3 and 5 years were 0.709,0.588,0.686 in the training cohort; 0.751, 0.648,0.666 in the internal validation cohort;0.781,0.588,0.645 in the external validation cohort, respectively. Calibration curves, Concordance index (C-index), and decision curve analysis (DCA) results revealed that using our model to predict OS and CSS is more efficient than other single clinicopathological characteristics.
A nomogram of OS and CSS based on clinicopathological characteristics can be conveniently used to predict the prognosis of CRCHPM patients.</description><subject>Calibration</subject><subject>Cancer specific survival</subject><subject>Care and treatment</subject><subject>Chemotherapy</subject><subject>Colorectal cancer</subject><subject>Colorectal carcinoma</subject><subject>Colorectal Neoplasms</subject><subject>Complications and side effects</subject><subject>Evaluation</subject><subject>Health aspects</subject><subject>Hepato-pulmonary metastasis</subject><subject>Humans</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - therapy</subject><subject>Lymph nodes</subject><subject>Lymphatic system</subject><subject>Medical prognosis</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Neoplasm Staging</subject><subject>Nomogram</subject><subject>Nomograms</subject><subject>Nomography (Mathematics)</subject><subject>Overall survival</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Radiation therapy</subject><subject>Rare diseases</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>SEER Program</subject><subject>Statistical analysis</subject><subject>Tumors</subject><subject>Variables</subject><issn>1471-2407</issn><issn>1471-2407</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkl2L1DAUhoso7rr6B7yQgCB60TUfTZN6ISzr18CC4Md1OE2TmQxt003S0fkX_mQzO-s6I5JAwsnzvkkOb1E8JficEFm_joRKyUtMaYkbwWTJ7hWnpBKkpBUW9w_2J8WjGNcYEyGxfFicMF43WHB6Wvx6Zzam99NgxoRg7NAGetdBcn5E3iJAU_DL0cfkNIraBzcuUdzGZAZkfUBTJrMyoh8urZD2vQ9GJ-iRhlGbgFYmE76c5n7wI4QtGkyCmKeLb7J5MCn4OGWJ2xgU09xtHxcPLPTRPLldz4rvH95_u_xUXn3-uLi8uCo15zKVLW4YYA62YbZteYtbrithQVbcskpIazmhFTOdxIQRILpqas5q0YAlmtSWnRWLvW_nYa2m4Ib8POXBqZuCD0sFIf-6NwrazrYSVzXtqooAllATRo1sa06rivLs9XbvNc3tYDqdOxKgPzI9PhndSi39RjVESkFoNnh5axD89WxiUoOL2vQ9jMbPUdFa8BqzRsiMPv8HXfs5jLlVO0pSQrAQf6kl5A-40fp8r96ZqguBa1k3lDSZOv8PlUdnBqf9aKzL9SPBqyNBZpL5mZYwx6gWX78csy8O2JWBPq2i7-ddtOIxSPegzlmIwdi7xhGsdkFX-6CrHHR1E3TFsujZYcvvJH-SzX4DMjP5ag</recordid><startdate>20220611</startdate><enddate>20220611</enddate><creator>Deng, Shenghe</creator><creator>Jiang, Zhenxing</creator><creator>Cao, Yinghao</creator><creator>Gu, Junnan</creator><creator>Mao, Fuwei</creator><creator>Xue, Yifan</creator><creator>Qin, Le</creator><creator>Liu, Ke</creator><creator>Wang, Jiliang</creator><creator>Wu, Ke</creator><creator>Cai, Kailin</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>ISR</scope><scope>3V.</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</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>20220611</creationdate><title>Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study</title><author>Deng, Shenghe ; Jiang, Zhenxing ; Cao, Yinghao ; Gu, Junnan ; Mao, Fuwei ; Xue, Yifan ; Qin, Le ; Liu, Ke ; Wang, Jiliang ; Wu, Ke ; Cai, Kailin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c558t-b093a05af93fbb5b0b5c47fa845f3478ff51243ed80131a1c49653679af1c16f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Calibration</topic><topic>Cancer specific survival</topic><topic>Care and treatment</topic><topic>Chemotherapy</topic><topic>Colorectal cancer</topic><topic>Colorectal carcinoma</topic><topic>Colorectal Neoplasms</topic><topic>Complications and side effects</topic><topic>Evaluation</topic><topic>Health aspects</topic><topic>Hepato-pulmonary metastasis</topic><topic>Humans</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - therapy</topic><topic>Lymph nodes</topic><topic>Lymphatic system</topic><topic>Medical prognosis</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Neoplasm Staging</topic><topic>Nomogram</topic><topic>Nomograms</topic><topic>Nomography (Mathematics)</topic><topic>Overall survival</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Prognosis</topic><topic>Radiation therapy</topic><topic>Rare diseases</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>SEER Program</topic><topic>Statistical analysis</topic><topic>Tumors</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Deng, Shenghe</creatorcontrib><creatorcontrib>Jiang, Zhenxing</creatorcontrib><creatorcontrib>Cao, Yinghao</creatorcontrib><creatorcontrib>Gu, Junnan</creatorcontrib><creatorcontrib>Mao, Fuwei</creatorcontrib><creatorcontrib>Xue, Yifan</creatorcontrib><creatorcontrib>Qin, Le</creatorcontrib><creatorcontrib>Liu, Ke</creatorcontrib><creatorcontrib>Wang, Jiliang</creatorcontrib><creatorcontrib>Wu, Ke</creatorcontrib><creatorcontrib>Cai, Kailin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Databases</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>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</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>Directory of Open Access Journals</collection><jtitle>BMC cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Deng, Shenghe</au><au>Jiang, Zhenxing</au><au>Cao, Yinghao</au><au>Gu, Junnan</au><au>Mao, Fuwei</au><au>Xue, Yifan</au><au>Qin, Le</au><au>Liu, Ke</au><au>Wang, Jiliang</au><au>Wu, Ke</au><au>Cai, Kailin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study</atitle><jtitle>BMC cancer</jtitle><addtitle>BMC Cancer</addtitle><date>2022-06-11</date><risdate>2022</risdate><volume>22</volume><issue>1</issue><spage>643</spage><epage>13</epage><pages>643-13</pages><artnum>643</artnum><issn>1471-2407</issn><eissn>1471-2407</eissn><abstract>Hepato-pulmonary metastasis of colorectal cancer (CRC) is a rare disease with poor prognosis. This study aims to establish a highly efficient nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer hepato-pulmonary metastasis (CRCHPM).
We retrospectively analyzed the data of patients with CRCHPM from SEER database and Wuhan Union Hospital Cancer Center (WUHCC). A total of 1250 CRCHPM patients were randomly assigned to the training, internal validation, and external validation cohorts from 2010 to 2016.Univariate and multivariate cox analysis were performed to identify independent clinicopathological predictors of OS and CSS, and a nomogram was constructed to predict OS and CSS in CRCHPM patients.
A nomogram of OS was constructed based on seven independent predictors of age, degree of differentiation, T stage, chemotherapy, number of lsampled lymph nodes, number of positive lymph nodes, and tumor size. Nomogram showed favorable sensitivity in predicting OS at 1, 3 and 5 years, with area under the receiver operating characteristic curve (AUROC) values of 0.802, 0.759 and 0.752 in the training cohort;0.814, 0.769 and 0.716 in the internal validation cohort;0.778, 0.756 and 0.753 in the external validation cohort, respectively. A nomogram of CSS was constructed based on three independent predictors of T stage, chemotherapy, and tumor size. The AUROC values of 1, 3 and 5 years were 0.709,0.588,0.686 in the training cohort; 0.751, 0.648,0.666 in the internal validation cohort;0.781,0.588,0.645 in the external validation cohort, respectively. Calibration curves, Concordance index (C-index), and decision curve analysis (DCA) results revealed that using our model to predict OS and CSS is more efficient than other single clinicopathological characteristics.
A nomogram of OS and CSS based on clinicopathological characteristics can be conveniently used to predict the prognosis of CRCHPM patients.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>35690752</pmid><doi>10.1186/s12885-022-09738-3</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Calibration Cancer specific survival Care and treatment Chemotherapy Colorectal cancer Colorectal carcinoma Colorectal Neoplasms Complications and side effects Evaluation Health aspects Hepato-pulmonary metastasis Humans Liver Liver cancer Lung cancer Lung Neoplasms - therapy Lymph nodes Lymphatic system Medical prognosis Metastases Metastasis Neoplasm Staging Nomogram Nomograms Nomography (Mathematics) Overall survival Patient outcomes Patients Prognosis Radiation therapy Rare diseases Regression analysis Retrospective Studies SEER Program Statistical analysis Tumors Variables |
title | Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study |
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