Prediction Model for Gastric Cancer Incidence in Korean Population
Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Bas...
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| Veröffentlicht in: | PloS one 2015-07, Vol.10 (7), p.e0132613-e0132613 |
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| creator | Eom, Bang Wool Joo, Jungnam Kim, Sohee Shin, Aesun Yang, Hye-Ryung Park, Junghyun Choi, Il Ju Kim, Young-Woo Kim, Jeongseon Nam, Byung-Ho |
| description | Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.
Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.
During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women).
In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance. |
| doi_str_mv | 10.1371/journal.pone.0132613 |
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Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.
During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women).
In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0132613</identifier><identifier>PMID: 26186332</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Alcohol use ; Alcoholic beverages ; Body mass ; Calibration ; Cancer ; Cancer diagnosis ; Cohort Studies ; Data processing ; Discrimination ; Epidemiology ; Exercise ; Female ; Gastric cancer ; Genetics ; Hazards ; Health insurance ; Health risk assessment ; Health risks ; Humans ; Incidence ; Male ; Medical screening ; Men ; Middle Aged ; Model accuracy ; Multivariate Analysis ; National health insurance ; Physical activity ; Prediction models ; Probability ; Proportional Hazards Models ; Reproducibility of Results ; Republic of Korea - epidemiology ; Risk analysis ; Risk Factors ; Risk groups ; Salts ; Smoking ; Socioeconomic factors ; Statistical models ; Statistics ; Stomach cancer ; Stomach Neoplasms - epidemiology</subject><ispartof>PloS one, 2015-07, Vol.10 (7), p.e0132613-e0132613</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Eom et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Eom et al 2015 Eom et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-26cf0b858c8e3831f942e7d8bad3c85a174491f39aaf2911815fb60afcf46e733</citedby><cites>FETCH-LOGICAL-c692t-26cf0b858c8e3831f942e7d8bad3c85a174491f39aaf2911815fb60afcf46e733</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/PMC4506054/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506054/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26186332$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Katoh, Masaru</contributor><creatorcontrib>Eom, Bang Wool</creatorcontrib><creatorcontrib>Joo, Jungnam</creatorcontrib><creatorcontrib>Kim, Sohee</creatorcontrib><creatorcontrib>Shin, Aesun</creatorcontrib><creatorcontrib>Yang, Hye-Ryung</creatorcontrib><creatorcontrib>Park, Junghyun</creatorcontrib><creatorcontrib>Choi, Il Ju</creatorcontrib><creatorcontrib>Kim, Young-Woo</creatorcontrib><creatorcontrib>Kim, Jeongseon</creatorcontrib><creatorcontrib>Nam, Byung-Ho</creatorcontrib><title>Prediction Model for Gastric Cancer Incidence in Korean Population</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.
Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.
During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women).
In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.</description><subject>Alcohol use</subject><subject>Alcoholic beverages</subject><subject>Body mass</subject><subject>Calibration</subject><subject>Cancer</subject><subject>Cancer diagnosis</subject><subject>Cohort Studies</subject><subject>Data processing</subject><subject>Discrimination</subject><subject>Epidemiology</subject><subject>Exercise</subject><subject>Female</subject><subject>Gastric cancer</subject><subject>Genetics</subject><subject>Hazards</subject><subject>Health insurance</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Humans</subject><subject>Incidence</subject><subject>Male</subject><subject>Medical screening</subject><subject>Men</subject><subject>Middle Aged</subject><subject>Model accuracy</subject><subject>Multivariate Analysis</subject><subject>National health insurance</subject><subject>Physical activity</subject><subject>Prediction models</subject><subject>Probability</subject><subject>Proportional Hazards Models</subject><subject>Reproducibility of Results</subject><subject>Republic of Korea - epidemiology</subject><subject>Risk analysis</subject><subject>Risk Factors</subject><subject>Risk groups</subject><subject>Salts</subject><subject>Smoking</subject><subject>Socioeconomic factors</subject><subject>Statistical models</subject><subject>Statistics</subject><subject>Stomach cancer</subject><subject>Stomach Neoplasms - epidemiology</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkktv1DAUhSMEog_4BwgiIVWwmMGvOM4GqYygjChqxWtr3Tj2jEeeeLATBP8ep5NWE9QFysKW_Z1zfW9Olj3DaI5pid9sfB9acPOdb_UcYUo4pg-yY1xRMuME0YcH-6PsJMYNQgUVnD_OjhIrOKXkOHt3HXRjVWd9m3_2jXa58SG_gNgFq_IFtEqHfNkq2-i0zW2bf_JBQ5tf-13vYNA9yR4ZcFE_HdfT7PuH998WH2eXVxfLxfnlTPGKdDPClUG1KIQSmgqKTcWILhtRQ0OVKACXjFXY0ArAkApjgQtTcwRGGcZ1Selp9mLvu3M-yrH9KDGvSoR5QXkilnui8bCRu2C3EP5ID1beHPiwkhA6q5yWtWCMNlWagdGMcAIISMlLVZcCakZJ8no7VuvrrW6UbrsAbmI6vWntWq78L8kKxFHBksGr0SD4n72OndzaqLRz0Grf37xbVJyUrEjoy3_Q-7sbqRWkBmxrfKqrBlN5zghJoUB4KDu_h0pfo7dWpawYm84ngtcTQWI6_btbQR-jXH798v_s1Y8pe3bArjW4bh2964fIxCnI9qAKPsagzd2QMZJD1G-nIYeoyzHqSfb88AfdiW6zTf8CBeH2ew</recordid><startdate>20150717</startdate><enddate>20150717</enddate><creator>Eom, Bang Wool</creator><creator>Joo, Jungnam</creator><creator>Kim, Sohee</creator><creator>Shin, Aesun</creator><creator>Yang, Hye-Ryung</creator><creator>Park, Junghyun</creator><creator>Choi, Il Ju</creator><creator>Kim, Young-Woo</creator><creator>Kim, Jeongseon</creator><creator>Nam, Byung-Ho</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20150717</creationdate><title>Prediction Model for Gastric Cancer Incidence in Korean Population</title><author>Eom, Bang Wool ; Joo, Jungnam ; Kim, Sohee ; Shin, Aesun ; Yang, Hye-Ryung ; Park, Junghyun ; Choi, Il Ju ; Kim, Young-Woo ; Kim, Jeongseon ; Nam, Byung-Ho</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-26cf0b858c8e3831f942e7d8bad3c85a174491f39aaf2911815fb60afcf46e733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Alcohol use</topic><topic>Alcoholic beverages</topic><topic>Body mass</topic><topic>Calibration</topic><topic>Cancer</topic><topic>Cancer diagnosis</topic><topic>Cohort Studies</topic><topic>Data processing</topic><topic>Discrimination</topic><topic>Epidemiology</topic><topic>Exercise</topic><topic>Female</topic><topic>Gastric cancer</topic><topic>Genetics</topic><topic>Hazards</topic><topic>Health insurance</topic><topic>Health risk assessment</topic><topic>Health risks</topic><topic>Humans</topic><topic>Incidence</topic><topic>Male</topic><topic>Medical screening</topic><topic>Men</topic><topic>Middle Aged</topic><topic>Model accuracy</topic><topic>Multivariate Analysis</topic><topic>National health insurance</topic><topic>Physical activity</topic><topic>Prediction models</topic><topic>Probability</topic><topic>Proportional Hazards Models</topic><topic>Reproducibility of Results</topic><topic>Republic of Korea - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eom, Bang Wool</au><au>Joo, Jungnam</au><au>Kim, Sohee</au><au>Shin, Aesun</au><au>Yang, Hye-Ryung</au><au>Park, Junghyun</au><au>Choi, Il Ju</au><au>Kim, Young-Woo</au><au>Kim, Jeongseon</au><au>Nam, Byung-Ho</au><au>Katoh, Masaru</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction Model for Gastric Cancer Incidence in Korean Population</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-07-17</date><risdate>2015</risdate><volume>10</volume><issue>7</issue><spage>e0132613</spage><epage>e0132613</epage><pages>e0132613-e0132613</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.
Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.
During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women).
In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26186332</pmid><doi>10.1371/journal.pone.0132613</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Alcohol use Alcoholic beverages Body mass Calibration Cancer Cancer diagnosis Cohort Studies Data processing Discrimination Epidemiology Exercise Female Gastric cancer Genetics Hazards Health insurance Health risk assessment Health risks Humans Incidence Male Medical screening Men Middle Aged Model accuracy Multivariate Analysis National health insurance Physical activity Prediction models Probability Proportional Hazards Models Reproducibility of Results Republic of Korea - epidemiology Risk analysis Risk Factors Risk groups Salts Smoking Socioeconomic factors Statistical models Statistics Stomach cancer Stomach Neoplasms - epidemiology |
| title | Prediction Model for Gastric Cancer Incidence in Korean Population |
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