A risk prediction model for hepatocellular carcinoma in non‐alcoholic fatty liver disease without cirrhosis

Background & Aims Although non‐alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non‐cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non‐cirrhotic NAFLD. Methods A nationwi...

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Veröffentlicht in:Liver international 2024-03, Vol.44 (3), p.738-748
Hauptverfasser: Kim, Gi‐Ae, Park, Yewan, Oh, Shin Ju, Jung, Jooyi, Han, Seungbong, Chang, Hye‐Sook, Park, Sung Won, Kim, Tae Hyup, Park, Hye Won, Choe, Jaewon, Kim, Jaeil, Lee, Han Chu
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container_end_page 748
container_issue 3
container_start_page 738
container_title Liver international
container_volume 44
creator Kim, Gi‐Ae
Park, Yewan
Oh, Shin Ju
Jung, Jooyi
Han, Seungbong
Chang, Hye‐Sook
Park, Sung Won
Kim, Tae Hyup
Park, Hye Won
Choe, Jaewon
Kim, Jaeil
Lee, Han Chu
description Background & Aims Although non‐alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non‐cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non‐cirrhotic NAFLD. Methods A nationwide cohort of non‐cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K‐fold cross‐validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). Results An 11‐point HCC risk prediction model for non‐cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma‐glutamyl transferase level (c‐index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59–0.95] at 5 years and 0.84 (95% CI, 0.73–0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0–6), moderate (7, 8) and high (9–11; estimated incidence rate >0.2%/year) risk groups. Conclusions A novel HCC risk prediction model for non‐cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC.
doi_str_mv 10.1111/liv.15819
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We aimed to develop and validate an HCC risk prediction model for non‐cirrhotic NAFLD. Methods A nationwide cohort of non‐cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K‐fold cross‐validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). Results An 11‐point HCC risk prediction model for non‐cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma‐glutamyl transferase level (c‐index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59–0.95] at 5 years and 0.84 (95% CI, 0.73–0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0–6), moderate (7, 8) and high (9–11; estimated incidence rate &gt;0.2%/year) risk groups. Conclusions A novel HCC risk prediction model for non‐cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC.</description><identifier>ISSN: 1478-3223</identifier><identifier>EISSN: 1478-3231</identifier><identifier>DOI: 10.1111/liv.15819</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc</publisher><subject>Alanine ; Alanine transaminase ; Calibration ; Cirrhosis ; Diabetes mellitus ; Fatty liver ; Health care facilities ; Hepatocellular carcinoma ; Liver ; Liver cancer ; Liver diseases ; non‐alcoholic fatty liver disease ; Patients ; Prediction models ; Risk groups ; Statistical models</subject><ispartof>Liver international, 2024-03, Vol.44 (3), p.738-748</ispartof><rights>2023 John Wiley &amp; Sons A/S. Published by John Wiley &amp; Sons Ltd.</rights><rights>2024 John Wiley &amp; Sons A/S</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2579-8464cbcf239c1fe58759d1ba21a5b7331c5bfed647107a1251d35e2287fefa913</cites><orcidid>0000-0002-7631-4124</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fliv.15819$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fliv.15819$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Kim, Gi‐Ae</creatorcontrib><creatorcontrib>Park, Yewan</creatorcontrib><creatorcontrib>Oh, Shin Ju</creatorcontrib><creatorcontrib>Jung, Jooyi</creatorcontrib><creatorcontrib>Han, Seungbong</creatorcontrib><creatorcontrib>Chang, Hye‐Sook</creatorcontrib><creatorcontrib>Park, Sung Won</creatorcontrib><creatorcontrib>Kim, Tae Hyup</creatorcontrib><creatorcontrib>Park, Hye Won</creatorcontrib><creatorcontrib>Choe, Jaewon</creatorcontrib><creatorcontrib>Kim, Jaeil</creatorcontrib><creatorcontrib>Lee, Han Chu</creatorcontrib><title>A risk prediction model for hepatocellular carcinoma in non‐alcoholic fatty liver disease without cirrhosis</title><title>Liver international</title><description>Background &amp; Aims Although non‐alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non‐cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non‐cirrhotic NAFLD. Methods A nationwide cohort of non‐cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K‐fold cross‐validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). Results An 11‐point HCC risk prediction model for non‐cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma‐glutamyl transferase level (c‐index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59–0.95] at 5 years and 0.84 (95% CI, 0.73–0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0–6), moderate (7, 8) and high (9–11; estimated incidence rate &gt;0.2%/year) risk groups. Conclusions A novel HCC risk prediction model for non‐cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC.</description><subject>Alanine</subject><subject>Alanine transaminase</subject><subject>Calibration</subject><subject>Cirrhosis</subject><subject>Diabetes mellitus</subject><subject>Fatty liver</subject><subject>Health care facilities</subject><subject>Hepatocellular carcinoma</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Liver diseases</subject><subject>non‐alcoholic fatty liver disease</subject><subject>Patients</subject><subject>Prediction models</subject><subject>Risk groups</subject><subject>Statistical models</subject><issn>1478-3223</issn><issn>1478-3231</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1kL1OwzAUhS0EEqUw8AaWmBjS5tpxnYxVxU-lSizAajmOrbg4cbBTqm48As_IkxAIYuMu5w7fPUf3IHQJ6QyGmTv7NgOWQ3GEJpDxPKGEwvHfTugpOotxm6ZQFAwmqFniYOML7oKurOqtb3HjK-2w8QHXupO9V9q5nZMBKxmUbX0jsW1x69vP9w_plK-9swob2fcHPMTrgCsbtYwa721f-12PlQ2h9tHGc3RipIv64len6On25nF1n2we7tar5SZRhPEiybNFpkplCC0UGM1yzooKSklAspJTCoqVRleLjEPKJRAGFWWakJwbbWQBdIquRt8u-Nedjr3Y-l1oh0hBCkp4mqcLOlDXI6WCjzFoI7pgGxkOAlLx3aYY3hE_bQ7sfGT31unD_6DYrJ_Hiy-KDXkw</recordid><startdate>202403</startdate><enddate>202403</enddate><creator>Kim, Gi‐Ae</creator><creator>Park, Yewan</creator><creator>Oh, Shin Ju</creator><creator>Jung, Jooyi</creator><creator>Han, Seungbong</creator><creator>Chang, Hye‐Sook</creator><creator>Park, Sung Won</creator><creator>Kim, Tae Hyup</creator><creator>Park, Hye Won</creator><creator>Choe, Jaewon</creator><creator>Kim, Jaeil</creator><creator>Lee, Han Chu</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T5</scope><scope>7U9</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>RC3</scope><orcidid>https://orcid.org/0000-0002-7631-4124</orcidid></search><sort><creationdate>202403</creationdate><title>A risk prediction model for hepatocellular carcinoma in non‐alcoholic fatty liver disease without cirrhosis</title><author>Kim, Gi‐Ae ; Park, Yewan ; Oh, Shin Ju ; Jung, Jooyi ; Han, Seungbong ; Chang, Hye‐Sook ; Park, Sung Won ; Kim, Tae Hyup ; Park, Hye Won ; Choe, Jaewon ; Kim, Jaeil ; Lee, Han Chu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2579-8464cbcf239c1fe58759d1ba21a5b7331c5bfed647107a1251d35e2287fefa913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Alanine</topic><topic>Alanine transaminase</topic><topic>Calibration</topic><topic>Cirrhosis</topic><topic>Diabetes mellitus</topic><topic>Fatty liver</topic><topic>Health care facilities</topic><topic>Hepatocellular carcinoma</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Liver diseases</topic><topic>non‐alcoholic fatty liver disease</topic><topic>Patients</topic><topic>Prediction models</topic><topic>Risk groups</topic><topic>Statistical models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Gi‐Ae</creatorcontrib><creatorcontrib>Park, Yewan</creatorcontrib><creatorcontrib>Oh, Shin Ju</creatorcontrib><creatorcontrib>Jung, Jooyi</creatorcontrib><creatorcontrib>Han, Seungbong</creatorcontrib><creatorcontrib>Chang, Hye‐Sook</creatorcontrib><creatorcontrib>Park, Sung Won</creatorcontrib><creatorcontrib>Kim, Tae Hyup</creatorcontrib><creatorcontrib>Park, Hye Won</creatorcontrib><creatorcontrib>Choe, Jaewon</creatorcontrib><creatorcontrib>Kim, Jaeil</creatorcontrib><creatorcontrib>Lee, Han Chu</creatorcontrib><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><jtitle>Liver international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Gi‐Ae</au><au>Park, Yewan</au><au>Oh, Shin Ju</au><au>Jung, Jooyi</au><au>Han, Seungbong</au><au>Chang, Hye‐Sook</au><au>Park, Sung Won</au><au>Kim, Tae Hyup</au><au>Park, Hye Won</au><au>Choe, Jaewon</au><au>Kim, Jaeil</au><au>Lee, Han Chu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A risk prediction model for hepatocellular carcinoma in non‐alcoholic fatty liver disease without cirrhosis</atitle><jtitle>Liver international</jtitle><date>2024-03</date><risdate>2024</risdate><volume>44</volume><issue>3</issue><spage>738</spage><epage>748</epage><pages>738-748</pages><issn>1478-3223</issn><eissn>1478-3231</eissn><abstract>Background &amp; Aims Although non‐alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non‐cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non‐cirrhotic NAFLD. Methods A nationwide cohort of non‐cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K‐fold cross‐validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). Results An 11‐point HCC risk prediction model for non‐cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma‐glutamyl transferase level (c‐index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59–0.95] at 5 years and 0.84 (95% CI, 0.73–0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0–6), moderate (7, 8) and high (9–11; estimated incidence rate &gt;0.2%/year) risk groups. Conclusions A novel HCC risk prediction model for non‐cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC.</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1111/liv.15819</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-7631-4124</orcidid></addata></record>
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source Wiley Online Library Journals Frontfile Complete
subjects Alanine
Alanine transaminase
Calibration
Cirrhosis
Diabetes mellitus
Fatty liver
Health care facilities
Hepatocellular carcinoma
Liver
Liver cancer
Liver diseases
non‐alcoholic fatty liver disease
Patients
Prediction models
Risk groups
Statistical models
title A risk prediction model for hepatocellular carcinoma in non‐alcoholic fatty liver disease without cirrhosis
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