Prediction of outcomes in patients with local recurrent nasopharyngeal carcinoma: development and validation of a four-factor prognostic model integrating baseline characteristics and [18F]FDG PET/CT parameters
Objectives To investigate the prognostic value of [ 18 F]FDG PET/CT parameters in local recurrent nasopharyngeal carcinoma (lrNPC) and establish a prognostic tool for lrNPC patients based on these [ 18 F]FDG PET/CT parameters. Methods A total of 358 lrNPC patients seen from 2010 to 2019 at Sun Yat-s...
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| Veröffentlicht in: | European radiology 2023-04, Vol.33 (4), p.2840-2849 |
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| creator | Dongxiang, Wen Liting, Liu Yujing, Liang Meijuan, Luo Shanshan, Guo Longbin, Xiong Yanzhou, Chen Meiling, Chen Kang, Ning Haiqiang, Mai Linquan, Tang Qiuyan, Chen |
| description | Objectives
To investigate the prognostic value of [
18
F]FDG PET/CT parameters in local recurrent nasopharyngeal carcinoma (lrNPC) and establish a prognostic tool for lrNPC patients based on these [
18
F]FDG PET/CT parameters.
Methods
A total of 358 lrNPC patients seen from 2010 to 2019 at Sun Yat-sen University Cancer Center with complete baseline characteristics and [
18
F]FDG PET/CT data were retrospectively analyzed. Maximal standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI) for recurrent nasopharynx tumors were included. Cox regression analysis was performed to select candidate variables. Subsequently, a nomogram for predicting overall survival (OS) for lrNPC patients was developed and internally validated.
Results
Multivariate Cox analysis results suggested that age ≥ 47 years (hazard ratio (HR), 1.62 (1.18-2.24);
p
= 0.003)，with smoking history (HR, 1.41 (1.01–1.98);
p
= 0.046), recurrent T stage {[rT3 vs rT1/2: HR, 1.81 (1.04–3.12);
p
= 0.037]; [rT4 vs rT1/2: HR, 2.46 (1.32–4.60);
p
= 0.005]}, and TLG {[37.1–184.3 vs ≤ 37.1: HR, 2.26 (1.49–3.42);
p
< 0.001]; [>184.3 vs ≤ 37.1: HR, 4.31 (2.50–7.43);
p
< 0.001]) were independent predictors of OS. A 4-factor nomogram was generated to stratify patients into 3 risk groups. This novel model showed good discrimination with a high C-index (0.752, 95%CI: 0.714–0.790). In addition, the calibration curves showed good agreement between the predicted probabilities and actual observations and decision curve analysis (DCA) suggested that the nomogram was useful for clinical decision-making.
Conclusions
Our study confirmed that [
18
F]FDG PET/CT parameters were valuable in predicting OS and PFS for lrNPC patients. The 4-factor prognostic model combing baseline patient characteristics with [
18
F]FDG PET/CT parameters for lrNPC patients had good discrimination, agreement, and clinical application potential.
Key Points
•
[
18
F]FDG
PET/CT
parameters were valuable in predicting OS and PFS for lrNPC patients.
• The novel 4-factor nomogram for lrNPC patients had good discrimination, agreement, and potential for clinical application. |
| doi_str_mv | 10.1007/s00330-022-09232-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10017585</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2787049751</sourcerecordid><originalsourceid>FETCH-LOGICAL-c475t-c3a6ffce19a44cdc16ea3c03a844ff70edbe877123b1b8c23b4816c5d4ef9bd93</originalsourceid><addsrcrecordid>eNp9ksFu1DAQhiMEoqXwAhyQJS5cQu3YWSdcULV0C1IlelhOCFkTZ7LrKrGDnSziNXkiZrttKRw42fJ88_8znsmyl4K_FZzr08S5lDznRZHzupBFLh5lx0LtL7xSjx_cj7JnKV1zzmuh9NPsSC5UUSyUPs5-XUVsnZ1c8Cx0LMyTDQMm5jwbYXLop8R-uGnL-mChZxHtHCO9Mg8pjFuIP_0GKWAhWufDAO9YizvswzjsKfAt20HvWrhzANaFOeYd2ClENsaw8SFNzrIhtNiT74SbSLTfsAYS9s4js-RDPEa3J9ON6ldRrb6tPlywq_P16XJN1UYYkJj0PHvSQZ_wxe15kn1Zna-XH_PLzxeflmeXuVW6nHIrYdF1FkUNStnWigWCtFxCpVTXaY5tg5XWopCNaCpLh6rEwpatwq5u2lqeZO8PuuPcDNha6jdCb8boBvoWE8CZvyPebc0m7AwNT-iyKknhza1CDN9nTJMZXLLY9-AxzMkUWtZa8bJWhL7-B72mb_TUH1GV5qrWpSCqOFA2hpQidvfVCL631eawM4Z2xtzsjNknvXrYx33K3ZIQIA9AohCNO_7x_o_sbzeh03I</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2787049751</pqid></control><display><type>article</type><title>Prediction of outcomes in patients with local recurrent nasopharyngeal carcinoma: development and validation of a four-factor prognostic model integrating baseline characteristics and [18F]FDG PET/CT parameters</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Dongxiang, Wen ; Liting, Liu ; Yujing, Liang ; Meijuan, Luo ; Shanshan, Guo ; Longbin, Xiong ; Yanzhou, Chen ; Meiling, Chen ; Kang, Ning ; Haiqiang, Mai ; Linquan, Tang ; Qiuyan, Chen</creator><creatorcontrib>Dongxiang, Wen ; Liting, Liu ; Yujing, Liang ; Meijuan, Luo ; Shanshan, Guo ; Longbin, Xiong ; Yanzhou, Chen ; Meiling, Chen ; Kang, Ning ; Haiqiang, Mai ; Linquan, Tang ; Qiuyan, Chen</creatorcontrib><description>Objectives
To investigate the prognostic value of [
18
F]FDG PET/CT parameters in local recurrent nasopharyngeal carcinoma (lrNPC) and establish a prognostic tool for lrNPC patients based on these [
18
F]FDG PET/CT parameters.
Methods
A total of 358 lrNPC patients seen from 2010 to 2019 at Sun Yat-sen University Cancer Center with complete baseline characteristics and [
18
F]FDG PET/CT data were retrospectively analyzed. Maximal standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI) for recurrent nasopharynx tumors were included. Cox regression analysis was performed to select candidate variables. Subsequently, a nomogram for predicting overall survival (OS) for lrNPC patients was developed and internally validated.
Results
Multivariate Cox analysis results suggested that age ≥ 47 years (hazard ratio (HR), 1.62 (1.18-2.24);
p
= 0.003)，with smoking history (HR, 1.41 (1.01–1.98);
p
= 0.046), recurrent T stage {[rT3 vs rT1/2: HR, 1.81 (1.04–3.12);
p
= 0.037]; [rT4 vs rT1/2: HR, 2.46 (1.32–4.60);
p
= 0.005]}, and TLG {[37.1–184.3 vs ≤ 37.1: HR, 2.26 (1.49–3.42);
p
< 0.001]; [>184.3 vs ≤ 37.1: HR, 4.31 (2.50–7.43);
p
< 0.001]) were independent predictors of OS. A 4-factor nomogram was generated to stratify patients into 3 risk groups. This novel model showed good discrimination with a high C-index (0.752, 95%CI: 0.714–0.790). In addition, the calibration curves showed good agreement between the predicted probabilities and actual observations and decision curve analysis (DCA) suggested that the nomogram was useful for clinical decision-making.
Conclusions
Our study confirmed that [
18
F]FDG PET/CT parameters were valuable in predicting OS and PFS for lrNPC patients. The 4-factor prognostic model combing baseline patient characteristics with [
18
F]FDG PET/CT parameters for lrNPC patients had good discrimination, agreement, and clinical application potential.
Key Points
•
[
18
F]FDG
PET/CT
parameters were valuable in predicting OS and PFS for lrNPC patients.
• The novel 4-factor nomogram for lrNPC patients had good discrimination, agreement, and potential for clinical application.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-022-09232-1</identifier><identifier>PMID: 36422647</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Calibration ; Cancer ; Cancer therapies ; Decision analysis ; Decision making ; Diagnostic Radiology ; Epstein-Barr virus ; Fluorine isotopes ; Fluorodeoxyglucose F18 - metabolism ; Glycolysis ; Head and Neck ; Heterogeneity ; Humans ; Hypertension ; Imaging ; Internal Medicine ; Interventional Radiology ; Laboratories ; Mathematical models ; Medical prognosis ; Medicine ; Medicine & Public Health ; Metabolism ; Metastasis ; Middle Aged ; Nasopharyngeal Carcinoma ; Nasopharyngeal Neoplasms - diagnostic imaging ; Nasopharynx ; Neoplasm Recurrence, Local - pathology ; Neuroradiology ; Nomograms ; Parameters ; Patients ; Positron emission tomography ; Positron Emission Tomography Computed Tomography - methods ; Prognosis ; Radiation therapy ; Radiology ; Radiopharmaceuticals ; Regression analysis ; Retrospective Studies ; Risk groups ; Throat cancer ; Tomography ; Tumor Burden ; Tumors ; Ultrasound</subject><ispartof>European radiology, 2023-04, Vol.33 (4), p.2840-2849</ispartof><rights>The Author(s) 2022</rights><rights>2022. The Author(s).</rights><rights>The Author(s) 2022. This work is published 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c475t-c3a6ffce19a44cdc16ea3c03a844ff70edbe877123b1b8c23b4816c5d4ef9bd93</citedby><cites>FETCH-LOGICAL-c475t-c3a6ffce19a44cdc16ea3c03a844ff70edbe877123b1b8c23b4816c5d4ef9bd93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-022-09232-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-022-09232-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36422647$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dongxiang, Wen</creatorcontrib><creatorcontrib>Liting, Liu</creatorcontrib><creatorcontrib>Yujing, Liang</creatorcontrib><creatorcontrib>Meijuan, Luo</creatorcontrib><creatorcontrib>Shanshan, Guo</creatorcontrib><creatorcontrib>Longbin, Xiong</creatorcontrib><creatorcontrib>Yanzhou, Chen</creatorcontrib><creatorcontrib>Meiling, Chen</creatorcontrib><creatorcontrib>Kang, Ning</creatorcontrib><creatorcontrib>Haiqiang, Mai</creatorcontrib><creatorcontrib>Linquan, Tang</creatorcontrib><creatorcontrib>Qiuyan, Chen</creatorcontrib><title>Prediction of outcomes in patients with local recurrent nasopharyngeal carcinoma: development and validation of a four-factor prognostic model integrating baseline characteristics and [18F]FDG PET/CT parameters</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To investigate the prognostic value of [
18
F]FDG PET/CT parameters in local recurrent nasopharyngeal carcinoma (lrNPC) and establish a prognostic tool for lrNPC patients based on these [
18
F]FDG PET/CT parameters.
Methods
A total of 358 lrNPC patients seen from 2010 to 2019 at Sun Yat-sen University Cancer Center with complete baseline characteristics and [
18
F]FDG PET/CT data were retrospectively analyzed. Maximal standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI) for recurrent nasopharynx tumors were included. Cox regression analysis was performed to select candidate variables. Subsequently, a nomogram for predicting overall survival (OS) for lrNPC patients was developed and internally validated.
Results
Multivariate Cox analysis results suggested that age ≥ 47 years (hazard ratio (HR), 1.62 (1.18-2.24);
p
= 0.003)，with smoking history (HR, 1.41 (1.01–1.98);
p
= 0.046), recurrent T stage {[rT3 vs rT1/2: HR, 1.81 (1.04–3.12);
p
= 0.037]; [rT4 vs rT1/2: HR, 2.46 (1.32–4.60);
p
= 0.005]}, and TLG {[37.1–184.3 vs ≤ 37.1: HR, 2.26 (1.49–3.42);
p
< 0.001]; [>184.3 vs ≤ 37.1: HR, 4.31 (2.50–7.43);
p
< 0.001]) were independent predictors of OS. A 4-factor nomogram was generated to stratify patients into 3 risk groups. This novel model showed good discrimination with a high C-index (0.752, 95%CI: 0.714–0.790). In addition, the calibration curves showed good agreement between the predicted probabilities and actual observations and decision curve analysis (DCA) suggested that the nomogram was useful for clinical decision-making.
Conclusions
Our study confirmed that [
18
F]FDG PET/CT parameters were valuable in predicting OS and PFS for lrNPC patients. The 4-factor prognostic model combing baseline patient characteristics with [
18
F]FDG PET/CT parameters for lrNPC patients had good discrimination, agreement, and clinical application potential.
Key Points
•
[
18
F]FDG
PET/CT
parameters were valuable in predicting OS and PFS for lrNPC patients.
• The novel 4-factor nomogram for lrNPC patients had good discrimination, agreement, and potential for clinical application.</description><subject>Calibration</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Diagnostic Radiology</subject><subject>Epstein-Barr virus</subject><subject>Fluorine isotopes</subject><subject>Fluorodeoxyglucose F18 - metabolism</subject><subject>Glycolysis</subject><subject>Head and Neck</subject><subject>Heterogeneity</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Laboratories</subject><subject>Mathematical models</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metabolism</subject><subject>Metastasis</subject><subject>Middle Aged</subject><subject>Nasopharyngeal Carcinoma</subject><subject>Nasopharyngeal Neoplasms - diagnostic imaging</subject><subject>Nasopharynx</subject><subject>Neoplasm Recurrence, Local - pathology</subject><subject>Neuroradiology</subject><subject>Nomograms</subject><subject>Parameters</subject><subject>Patients</subject><subject>Positron emission tomography</subject><subject>Positron Emission Tomography Computed Tomography - methods</subject><subject>Prognosis</subject><subject>Radiation therapy</subject><subject>Radiology</subject><subject>Radiopharmaceuticals</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Risk groups</subject><subject>Throat cancer</subject><subject>Tomography</subject><subject>Tumor Burden</subject><subject>Tumors</subject><subject>Ultrasound</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9ksFu1DAQhiMEoqXwAhyQJS5cQu3YWSdcULV0C1IlelhOCFkTZ7LrKrGDnSziNXkiZrttKRw42fJ88_8znsmyl4K_FZzr08S5lDznRZHzupBFLh5lx0LtL7xSjx_cj7JnKV1zzmuh9NPsSC5UUSyUPs5-XUVsnZ1c8Cx0LMyTDQMm5jwbYXLop8R-uGnL-mChZxHtHCO9Mg8pjFuIP_0GKWAhWufDAO9YizvswzjsKfAt20HvWrhzANaFOeYd2ClENsaw8SFNzrIhtNiT74SbSLTfsAYS9s4js-RDPEa3J9ON6ldRrb6tPlywq_P16XJN1UYYkJj0PHvSQZ_wxe15kn1Zna-XH_PLzxeflmeXuVW6nHIrYdF1FkUNStnWigWCtFxCpVTXaY5tg5XWopCNaCpLh6rEwpatwq5u2lqeZO8PuuPcDNha6jdCb8boBvoWE8CZvyPebc0m7AwNT-iyKknhza1CDN9nTJMZXLLY9-AxzMkUWtZa8bJWhL7-B72mb_TUH1GV5qrWpSCqOFA2hpQidvfVCL631eawM4Z2xtzsjNknvXrYx33K3ZIQIA9AohCNO_7x_o_sbzeh03I</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Dongxiang, Wen</creator><creator>Liting, Liu</creator><creator>Yujing, Liang</creator><creator>Meijuan, Luo</creator><creator>Shanshan, Guo</creator><creator>Longbin, Xiong</creator><creator>Yanzhou, Chen</creator><creator>Meiling, Chen</creator><creator>Kang, Ning</creator><creator>Haiqiang, Mai</creator><creator>Linquan, Tang</creator><creator>Qiuyan, Chen</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>C6C</scope><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>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230401</creationdate><title>Prediction of outcomes in patients with local recurrent nasopharyngeal carcinoma: development and validation of a four-factor prognostic model integrating baseline characteristics and [18F]FDG PET/CT parameters</title><author>Dongxiang, Wen ; Liting, Liu ; Yujing, Liang ; Meijuan, Luo ; Shanshan, Guo ; Longbin, Xiong ; Yanzhou, Chen ; Meiling, Chen ; Kang, Ning ; Haiqiang, Mai ; Linquan, Tang ; Qiuyan, Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c475t-c3a6ffce19a44cdc16ea3c03a844ff70edbe877123b1b8c23b4816c5d4ef9bd93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Calibration</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Diagnostic Radiology</topic><topic>Epstein-Barr virus</topic><topic>Fluorine isotopes</topic><topic>Fluorodeoxyglucose F18 - metabolism</topic><topic>Glycolysis</topic><topic>Head and Neck</topic><topic>Heterogeneity</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Laboratories</topic><topic>Mathematical models</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metabolism</topic><topic>Metastasis</topic><topic>Middle Aged</topic><topic>Nasopharyngeal Carcinoma</topic><topic>Nasopharyngeal Neoplasms - diagnostic imaging</topic><topic>Nasopharynx</topic><topic>Neoplasm Recurrence, Local - pathology</topic><topic>Neuroradiology</topic><topic>Nomograms</topic><topic>Parameters</topic><topic>Patients</topic><topic>Positron emission tomography</topic><topic>Positron Emission Tomography Computed Tomography - methods</topic><topic>Prognosis</topic><topic>Radiation therapy</topic><topic>Radiology</topic><topic>Radiopharmaceuticals</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Risk groups</topic><topic>Throat cancer</topic><topic>Tomography</topic><topic>Tumor Burden</topic><topic>Tumors</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dongxiang, Wen</creatorcontrib><creatorcontrib>Liting, Liu</creatorcontrib><creatorcontrib>Yujing, Liang</creatorcontrib><creatorcontrib>Meijuan, Luo</creatorcontrib><creatorcontrib>Shanshan, Guo</creatorcontrib><creatorcontrib>Longbin, Xiong</creatorcontrib><creatorcontrib>Yanzhou, Chen</creatorcontrib><creatorcontrib>Meiling, Chen</creatorcontrib><creatorcontrib>Kang, Ning</creatorcontrib><creatorcontrib>Haiqiang, Mai</creatorcontrib><creatorcontrib>Linquan, Tang</creatorcontrib><creatorcontrib>Qiuyan, Chen</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</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 UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dongxiang, Wen</au><au>Liting, Liu</au><au>Yujing, Liang</au><au>Meijuan, Luo</au><au>Shanshan, Guo</au><au>Longbin, Xiong</au><au>Yanzhou, Chen</au><au>Meiling, Chen</au><au>Kang, Ning</au><au>Haiqiang, Mai</au><au>Linquan, Tang</au><au>Qiuyan, Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of outcomes in patients with local recurrent nasopharyngeal carcinoma: development and validation of a four-factor prognostic model integrating baseline characteristics and [18F]FDG PET/CT parameters</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2023-04-01</date><risdate>2023</risdate><volume>33</volume><issue>4</issue><spage>2840</spage><epage>2849</epage><pages>2840-2849</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To investigate the prognostic value of [
18
F]FDG PET/CT parameters in local recurrent nasopharyngeal carcinoma (lrNPC) and establish a prognostic tool for lrNPC patients based on these [
18
F]FDG PET/CT parameters.
Methods
A total of 358 lrNPC patients seen from 2010 to 2019 at Sun Yat-sen University Cancer Center with complete baseline characteristics and [
18
F]FDG PET/CT data were retrospectively analyzed. Maximal standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI) for recurrent nasopharynx tumors were included. Cox regression analysis was performed to select candidate variables. Subsequently, a nomogram for predicting overall survival (OS) for lrNPC patients was developed and internally validated.
Results
Multivariate Cox analysis results suggested that age ≥ 47 years (hazard ratio (HR), 1.62 (1.18-2.24);
p
= 0.003)，with smoking history (HR, 1.41 (1.01–1.98);
p
= 0.046), recurrent T stage {[rT3 vs rT1/2: HR, 1.81 (1.04–3.12);
p
= 0.037]; [rT4 vs rT1/2: HR, 2.46 (1.32–4.60);
p
= 0.005]}, and TLG {[37.1–184.3 vs ≤ 37.1: HR, 2.26 (1.49–3.42);
p
< 0.001]; [>184.3 vs ≤ 37.1: HR, 4.31 (2.50–7.43);
p
< 0.001]) were independent predictors of OS. A 4-factor nomogram was generated to stratify patients into 3 risk groups. This novel model showed good discrimination with a high C-index (0.752, 95%CI: 0.714–0.790). In addition, the calibration curves showed good agreement between the predicted probabilities and actual observations and decision curve analysis (DCA) suggested that the nomogram was useful for clinical decision-making.
Conclusions
Our study confirmed that [
18
F]FDG PET/CT parameters were valuable in predicting OS and PFS for lrNPC patients. The 4-factor prognostic model combing baseline patient characteristics with [
18
F]FDG PET/CT parameters for lrNPC patients had good discrimination, agreement, and clinical application potential.
Key Points
•
[
18
F]FDG
PET/CT
parameters were valuable in predicting OS and PFS for lrNPC patients.
• The novel 4-factor nomogram for lrNPC patients had good discrimination, agreement, and potential for clinical application.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>36422647</pmid><doi>10.1007/s00330-022-09232-1</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1432-1084 |
| ispartof | European radiology, 2023-04, Vol.33 (4), p.2840-2849 |
| issn | 1432-1084 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10017585 |
| source | MEDLINE; SpringerNature Journals |
| subjects | Calibration Cancer Cancer therapies Decision analysis Decision making Diagnostic Radiology Epstein-Barr virus Fluorine isotopes Fluorodeoxyglucose F18 - metabolism Glycolysis Head and Neck Heterogeneity Humans Hypertension Imaging Internal Medicine Interventional Radiology Laboratories Mathematical models Medical prognosis Medicine Medicine & Public Health Metabolism Metastasis Middle Aged Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms - diagnostic imaging Nasopharynx Neoplasm Recurrence, Local - pathology Neuroradiology Nomograms Parameters Patients Positron emission tomography Positron Emission Tomography Computed Tomography - methods Prognosis Radiation therapy Radiology Radiopharmaceuticals Regression analysis Retrospective Studies Risk groups Throat cancer Tomography Tumor Burden Tumors Ultrasound |
| title | Prediction of outcomes in patients with local recurrent nasopharyngeal carcinoma: development and validation of a four-factor prognostic model integrating baseline characteristics and [18F]FDG PET/CT parameters |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T10%3A47%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20of%20outcomes%20in%20patients%20with%20local%20recurrent%20nasopharyngeal%20carcinoma:%20development%20and%20validation%20of%20a%20four-factor%20prognostic%20model%20integrating%20baseline%20characteristics%20and%20%5B18F%5DFDG%20PET/CT%20parameters&rft.jtitle=European%20radiology&rft.au=Dongxiang,%20Wen&rft.date=2023-04-01&rft.volume=33&rft.issue=4&rft.spage=2840&rft.epage=2849&rft.pages=2840-2849&rft.issn=1432-1084&rft.eissn=1432-1084&rft_id=info:doi/10.1007/s00330-022-09232-1&rft_dat=%3Cproquest_pubme%3E2787049751%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2787049751&rft_id=info:pmid/36422647&rfr_iscdi=true |