Development of lung cancer risk prediction models based on F-18 FDG PET images
Objective We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET). Methods We retrospec...
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| Veröffentlicht in: | Annals of nuclear medicine 2023-10, Vol.37 (10), p.572-582 |
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| description | Objective
We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).
Methods
We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.
Results
The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (
p
|
| doi_str_mv | 10.1007/s12149-023-01858-5 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2839246849</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2839246849</sourcerecordid><originalsourceid>FETCH-LOGICAL-c370t-b693e04ebc888179cf4a60ff0d1cacaf4970bb4b54b7af7fc000f0eefe814b873</originalsourceid><addsrcrecordid>eNp9kE1PGzEQhq2qqATaP9ADstQLF8N47V17j1VCAlJUOKRny_aOo6X7EexsJf59DUlB6qEna-Rn3pl5CPnK4YoDqOvECy5rBoVgwHWpWfmBzLiuJKukEB_JDGoumeJanZKzlB4BikwVn8ipULLUtRYz8mOBv7Ebdz0OezoG2k3Dlno7eIw0tukX3UVsWr9vx4H2Y4Ndos4mbGiul4xrulys6MPNhra93WL6TE6C7RJ-Ob7n5OfyZjO_Zev71d38-5p5oWDPXFULBInOa625qn2QtoIQoOHeehtkrcA56UrplA0qeAAIgBhQc-m0Eufk8pC7i-PThGlv-jZ57Do74DglU2hRF7LSss7ot3_Qx3GKQ94uU1UlucgbZKo4UD6OKUUMZhfzSfHZcDAvts3Btsm2zattU-ami2P05Hps3lr-6s2AOAApfw1bjO-z_xP7B4exiN8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2866413817</pqid></control><display><type>article</type><title>Development of lung cancer risk prediction models based on F-18 FDG PET images</title><source>SpringerNature Journals</source><creator>Choi, Kaeum ; Park, Jae Seok ; Kwon, Yong Shik ; Park, Sun Hyo ; Kim, Hyun Jung ; Noh, Hyunju ; Won, Kyoung Sook ; Song, Bong-Il ; Kim, Hae Won</creator><creatorcontrib>Choi, Kaeum ; Park, Jae Seok ; Kwon, Yong Shik ; Park, Sun Hyo ; Kim, Hyun Jung ; Noh, Hyunju ; Won, Kyoung Sook ; Song, Bong-Il ; Kim, Hae Won</creatorcontrib><description><![CDATA[Objective
We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).
Methods
We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.
Results
The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (
p
< 0.001 and
p
< 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07,
p
< 0.001; male: OR 2.08,
p
= 0.015; BMI: OR 0.93,
p
= 0.057; current or past smoker: OR 5.60,
p
< 0.001; lung SUVR: OR 1.13,
p
< 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06,
p
< 0.001; male: OR 1.87,
p
= 0.045; BMI: OR 0.93,
p
= 0.010; current or past smoker: OR 4.78,
p
< 0.001; lung MH index: OR 1.33,
p
< 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively].
Conclusions
The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.]]></description><identifier>ISSN: 0914-7187</identifier><identifier>EISSN: 1864-6433</identifier><identifier>DOI: 10.1007/s12149-023-01858-5</identifier><identifier>PMID: 37458983</identifier><language>eng</language><publisher>Singapore: Springer Nature Singapore</publisher><subject>Age ; Biomarkers ; Body mass index ; Body size ; Computed tomography ; Fluorine isotopes ; Heterogeneity ; Imaging ; Lung cancer ; Males ; Medicine ; Medicine & Public Health ; Metabolism ; Nuclear Medicine ; Original Article ; Parameters ; Positron emission ; Positron emission tomography ; Prediction models ; Radioisotopes ; Radiology ; Regression analysis ; Regression models ; Risk ; Sex ; Smoking ; Statistical analysis ; Training</subject><ispartof>Annals of nuclear medicine, 2023-10, Vol.37 (10), p.572-582</ispartof><rights>The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c370t-b693e04ebc888179cf4a60ff0d1cacaf4970bb4b54b7af7fc000f0eefe814b873</cites><orcidid>0000-0002-6707-3904</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12149-023-01858-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12149-023-01858-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27926,27927,41490,42559,51321</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37458983$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Choi, Kaeum</creatorcontrib><creatorcontrib>Park, Jae Seok</creatorcontrib><creatorcontrib>Kwon, Yong Shik</creatorcontrib><creatorcontrib>Park, Sun Hyo</creatorcontrib><creatorcontrib>Kim, Hyun Jung</creatorcontrib><creatorcontrib>Noh, Hyunju</creatorcontrib><creatorcontrib>Won, Kyoung Sook</creatorcontrib><creatorcontrib>Song, Bong-Il</creatorcontrib><creatorcontrib>Kim, Hae Won</creatorcontrib><title>Development of lung cancer risk prediction models based on F-18 FDG PET images</title><title>Annals of nuclear medicine</title><addtitle>Ann Nucl Med</addtitle><addtitle>Ann Nucl Med</addtitle><description><![CDATA[Objective
We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).
Methods
We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.
Results
The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (
p
< 0.001 and
p
< 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07,
p
< 0.001; male: OR 2.08,
p
= 0.015; BMI: OR 0.93,
p
= 0.057; current or past smoker: OR 5.60,
p
< 0.001; lung SUVR: OR 1.13,
p
< 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06,
p
< 0.001; male: OR 1.87,
p
= 0.045; BMI: OR 0.93,
p
= 0.010; current or past smoker: OR 4.78,
p
< 0.001; lung MH index: OR 1.33,
p
< 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively].
Conclusions
The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.]]></description><subject>Age</subject><subject>Biomarkers</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Computed tomography</subject><subject>Fluorine isotopes</subject><subject>Heterogeneity</subject><subject>Imaging</subject><subject>Lung cancer</subject><subject>Males</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metabolism</subject><subject>Nuclear Medicine</subject><subject>Original Article</subject><subject>Parameters</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>Prediction models</subject><subject>Radioisotopes</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Risk</subject><subject>Sex</subject><subject>Smoking</subject><subject>Statistical analysis</subject><subject>Training</subject><issn>0914-7187</issn><issn>1864-6433</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kE1PGzEQhq2qqATaP9ADstQLF8N47V17j1VCAlJUOKRny_aOo6X7EexsJf59DUlB6qEna-Rn3pl5CPnK4YoDqOvECy5rBoVgwHWpWfmBzLiuJKukEB_JDGoumeJanZKzlB4BikwVn8ipULLUtRYz8mOBv7Ebdz0OezoG2k3Dlno7eIw0tukX3UVsWr9vx4H2Y4Ndos4mbGiul4xrulys6MPNhra93WL6TE6C7RJ-Ob7n5OfyZjO_Zev71d38-5p5oWDPXFULBInOa625qn2QtoIQoOHeehtkrcA56UrplA0qeAAIgBhQc-m0Eufk8pC7i-PThGlv-jZ57Do74DglU2hRF7LSss7ot3_Qx3GKQ94uU1UlucgbZKo4UD6OKUUMZhfzSfHZcDAvts3Btsm2zattU-ami2P05Hps3lr-6s2AOAApfw1bjO-z_xP7B4exiN8</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Choi, Kaeum</creator><creator>Park, Jae Seok</creator><creator>Kwon, Yong Shik</creator><creator>Park, Sun Hyo</creator><creator>Kim, Hyun Jung</creator><creator>Noh, Hyunju</creator><creator>Won, Kyoung Sook</creator><creator>Song, Bong-Il</creator><creator>Kim, Hae Won</creator><general>Springer Nature Singapore</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7TK</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6707-3904</orcidid></search><sort><creationdate>20231001</creationdate><title>Development of lung cancer risk prediction models based on F-18 FDG PET images</title><author>Choi, Kaeum ; Park, Jae Seok ; Kwon, Yong Shik ; Park, Sun Hyo ; Kim, Hyun Jung ; Noh, Hyunju ; Won, Kyoung Sook ; Song, Bong-Il ; Kim, Hae Won</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c370t-b693e04ebc888179cf4a60ff0d1cacaf4970bb4b54b7af7fc000f0eefe814b873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Age</topic><topic>Biomarkers</topic><topic>Body mass index</topic><topic>Body size</topic><topic>Computed tomography</topic><topic>Fluorine isotopes</topic><topic>Heterogeneity</topic><topic>Imaging</topic><topic>Lung cancer</topic><topic>Males</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metabolism</topic><topic>Nuclear Medicine</topic><topic>Original Article</topic><topic>Parameters</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Prediction models</topic><topic>Radioisotopes</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Risk</topic><topic>Sex</topic><topic>Smoking</topic><topic>Statistical analysis</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Choi, Kaeum</creatorcontrib><creatorcontrib>Park, Jae Seok</creatorcontrib><creatorcontrib>Kwon, Yong Shik</creatorcontrib><creatorcontrib>Park, Sun Hyo</creatorcontrib><creatorcontrib>Kim, Hyun Jung</creatorcontrib><creatorcontrib>Noh, Hyunju</creatorcontrib><creatorcontrib>Won, Kyoung Sook</creatorcontrib><creatorcontrib>Song, Bong-Il</creatorcontrib><creatorcontrib>Kim, Hae Won</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Annals of nuclear medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Choi, Kaeum</au><au>Park, Jae Seok</au><au>Kwon, Yong Shik</au><au>Park, Sun Hyo</au><au>Kim, Hyun Jung</au><au>Noh, Hyunju</au><au>Won, Kyoung Sook</au><au>Song, Bong-Il</au><au>Kim, Hae Won</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of lung cancer risk prediction models based on F-18 FDG PET images</atitle><jtitle>Annals of nuclear medicine</jtitle><stitle>Ann Nucl Med</stitle><addtitle>Ann Nucl Med</addtitle><date>2023-10-01</date><risdate>2023</risdate><volume>37</volume><issue>10</issue><spage>572</spage><epage>582</epage><pages>572-582</pages><issn>0914-7187</issn><eissn>1864-6433</eissn><abstract><![CDATA[Objective
We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).
Methods
We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.
Results
The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (
p
< 0.001 and
p
< 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07,
p
< 0.001; male: OR 2.08,
p
= 0.015; BMI: OR 0.93,
p
= 0.057; current or past smoker: OR 5.60,
p
< 0.001; lung SUVR: OR 1.13,
p
< 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06,
p
< 0.001; male: OR 1.87,
p
= 0.045; BMI: OR 0.93,
p
= 0.010; current or past smoker: OR 4.78,
p
< 0.001; lung MH index: OR 1.33,
p
< 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively].
Conclusions
The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.]]></abstract><cop>Singapore</cop><pub>Springer Nature Singapore</pub><pmid>37458983</pmid><doi>10.1007/s12149-023-01858-5</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-6707-3904</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Biomarkers Body mass index Body size Computed tomography Fluorine isotopes Heterogeneity Imaging Lung cancer Males Medicine Medicine & Public Health Metabolism Nuclear Medicine Original Article Parameters Positron emission Positron emission tomography Prediction models Radioisotopes Radiology Regression analysis Regression models Risk Sex Smoking Statistical analysis Training |
| title | Development of lung cancer risk prediction models based on F-18 FDG PET images |
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