Natural history and growth prediction model of pancreatic serous cystic neoplasms
Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection. Util...
Gespeichert in:
| Veröffentlicht in: | Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] 2024-05, Vol.24 (3), p.489-492 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 492 |
|---|---|
| container_issue | 3 |
| container_start_page | 489 |
| container_title | Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] |
| container_volume | 24 |
| creator | Chang, Jenny H. Perlmutter, Breanna C. Wehrle, Chase Naples, Robert Stackhouse, Kathryn McMichael, John Chao, Tu Naffouje, Samer Augustin, Toms Joyce, Daniel Simon, Robert Walsh, R Matthew |
| description | Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection.
Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package.
Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5–160 mm), with a mean follow-up of 72 months (range 3–266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p |
| doi_str_mv | 10.1016/j.pan.2024.02.016 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2938285483</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1424390324000607</els_id><sourcerecordid>2938285483</sourcerecordid><originalsourceid>FETCH-LOGICAL-c348t-e237d60451e552164b6d3b856d7e03e1e9542c371ad49fe9fce6c9604a75f8c63</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBCIlsIHcEE-cknwKy9xQhUvqQIhwdly7Q11lcTBTkD9e1y19Mhpd0czs7uD0CUlKSU0v1mnvepSRphICUsjcoSmVDCR8IrS40NP-ASdhbAmhDFKq1M04aUQnHE2RW8vahi9avDKhsH5DVadwZ_e_Qwr3HswVg_Wdbh1BhrsahwXag9qsBoH8G4MWG_CdurA9Y0KbThHJ7VqAlzs6wx9PNy_z5-Sxevj8_xukWguyiEBxguTE5FRyDJGc7HMDV-WWW4KIBwoVJlgmhdUGVHVUNUacl1FgSqyutQ5n6HrnW_v3dcIYZCtDRqaRsVTxiBZxUtWZqLkkUp3VO1dCB5q2XvbKr-RlMhtknIt42Nym6QkTEYkaq729uOyBXNQ_EUXCbc7AsQnvy14GbSFTsfMPOhBGmf_sf8F5aKEOg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2938285483</pqid></control><display><type>article</type><title>Natural history and growth prediction model of pancreatic serous cystic neoplasms</title><source>MEDLINE</source><source>Alma/SFX Local Collection</source><creator>Chang, Jenny H. ; Perlmutter, Breanna C. ; Wehrle, Chase ; Naples, Robert ; Stackhouse, Kathryn ; McMichael, John ; Chao, Tu ; Naffouje, Samer ; Augustin, Toms ; Joyce, Daniel ; Simon, Robert ; Walsh, R Matthew</creator><creatorcontrib>Chang, Jenny H. ; Perlmutter, Breanna C. ; Wehrle, Chase ; Naples, Robert ; Stackhouse, Kathryn ; McMichael, John ; Chao, Tu ; Naffouje, Samer ; Augustin, Toms ; Joyce, Daniel ; Simon, Robert ; Walsh, R Matthew</creatorcontrib><description>Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection.
Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package.
Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5–160 mm), with a mean follow-up of 72 months (range 3–266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals.
SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.</description><identifier>ISSN: 1424-3903</identifier><identifier>EISSN: 1424-3911</identifier><identifier>DOI: 10.1016/j.pan.2024.02.016</identifier><identifier>PMID: 38443232</identifier><language>eng</language><publisher>Switzerland: Elsevier B.V</publisher><subject>Cystadenoma, Serous - surgery ; Humans ; Neoplasms, Cystic, Mucinous, and Serous ; Pancreatic Cyst - surgery ; Pancreatic cysts ; Pancreatic Neoplasms - pathology ; Predictive growth nomogram ; Serous cystadenoma ; Serous cystic neoplasm ; Surgical management</subject><ispartof>Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], 2024-05, Vol.24 (3), p.489-492</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c348t-e237d60451e552164b6d3b856d7e03e1e9542c371ad49fe9fce6c9604a75f8c63</cites><orcidid>0000-0002-6546-3909 ; 0000-0001-7070-1345 ; 0000-0002-5146-8725 ; 0000-0003-4973-9597 ; 0000-0002-4592-0500 ; 0000-0003-2655-6333</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38443232$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chang, Jenny H.</creatorcontrib><creatorcontrib>Perlmutter, Breanna C.</creatorcontrib><creatorcontrib>Wehrle, Chase</creatorcontrib><creatorcontrib>Naples, Robert</creatorcontrib><creatorcontrib>Stackhouse, Kathryn</creatorcontrib><creatorcontrib>McMichael, John</creatorcontrib><creatorcontrib>Chao, Tu</creatorcontrib><creatorcontrib>Naffouje, Samer</creatorcontrib><creatorcontrib>Augustin, Toms</creatorcontrib><creatorcontrib>Joyce, Daniel</creatorcontrib><creatorcontrib>Simon, Robert</creatorcontrib><creatorcontrib>Walsh, R Matthew</creatorcontrib><title>Natural history and growth prediction model of pancreatic serous cystic neoplasms</title><title>Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]</title><addtitle>Pancreatology</addtitle><description>Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection.
Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package.
Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5–160 mm), with a mean follow-up of 72 months (range 3–266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals.
SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.</description><subject>Cystadenoma, Serous - surgery</subject><subject>Humans</subject><subject>Neoplasms, Cystic, Mucinous, and Serous</subject><subject>Pancreatic Cyst - surgery</subject><subject>Pancreatic cysts</subject><subject>Pancreatic Neoplasms - pathology</subject><subject>Predictive growth nomogram</subject><subject>Serous cystadenoma</subject><subject>Serous cystic neoplasm</subject><subject>Surgical management</subject><issn>1424-3903</issn><issn>1424-3911</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9UMtOwzAQtBCIlsIHcEE-cknwKy9xQhUvqQIhwdly7Q11lcTBTkD9e1y19Mhpd0czs7uD0CUlKSU0v1mnvepSRphICUsjcoSmVDCR8IrS40NP-ASdhbAmhDFKq1M04aUQnHE2RW8vahi9avDKhsH5DVadwZ_e_Qwr3HswVg_Wdbh1BhrsahwXag9qsBoH8G4MWG_CdurA9Y0KbThHJ7VqAlzs6wx9PNy_z5-Sxevj8_xukWguyiEBxguTE5FRyDJGc7HMDV-WWW4KIBwoVJlgmhdUGVHVUNUacl1FgSqyutQ5n6HrnW_v3dcIYZCtDRqaRsVTxiBZxUtWZqLkkUp3VO1dCB5q2XvbKr-RlMhtknIt42Nym6QkTEYkaq729uOyBXNQ_EUXCbc7AsQnvy14GbSFTsfMPOhBGmf_sf8F5aKEOg</recordid><startdate>202405</startdate><enddate>202405</enddate><creator>Chang, Jenny H.</creator><creator>Perlmutter, Breanna C.</creator><creator>Wehrle, Chase</creator><creator>Naples, Robert</creator><creator>Stackhouse, Kathryn</creator><creator>McMichael, John</creator><creator>Chao, Tu</creator><creator>Naffouje, Samer</creator><creator>Augustin, Toms</creator><creator>Joyce, Daniel</creator><creator>Simon, Robert</creator><creator>Walsh, R Matthew</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</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>7X8</scope><orcidid>https://orcid.org/0000-0002-6546-3909</orcidid><orcidid>https://orcid.org/0000-0001-7070-1345</orcidid><orcidid>https://orcid.org/0000-0002-5146-8725</orcidid><orcidid>https://orcid.org/0000-0003-4973-9597</orcidid><orcidid>https://orcid.org/0000-0002-4592-0500</orcidid><orcidid>https://orcid.org/0000-0003-2655-6333</orcidid></search><sort><creationdate>202405</creationdate><title>Natural history and growth prediction model of pancreatic serous cystic neoplasms</title><author>Chang, Jenny H. ; Perlmutter, Breanna C. ; Wehrle, Chase ; Naples, Robert ; Stackhouse, Kathryn ; McMichael, John ; Chao, Tu ; Naffouje, Samer ; Augustin, Toms ; Joyce, Daniel ; Simon, Robert ; Walsh, R Matthew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-e237d60451e552164b6d3b856d7e03e1e9542c371ad49fe9fce6c9604a75f8c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Cystadenoma, Serous - surgery</topic><topic>Humans</topic><topic>Neoplasms, Cystic, Mucinous, and Serous</topic><topic>Pancreatic Cyst - surgery</topic><topic>Pancreatic cysts</topic><topic>Pancreatic Neoplasms - pathology</topic><topic>Predictive growth nomogram</topic><topic>Serous cystadenoma</topic><topic>Serous cystic neoplasm</topic><topic>Surgical management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Jenny H.</creatorcontrib><creatorcontrib>Perlmutter, Breanna C.</creatorcontrib><creatorcontrib>Wehrle, Chase</creatorcontrib><creatorcontrib>Naples, Robert</creatorcontrib><creatorcontrib>Stackhouse, Kathryn</creatorcontrib><creatorcontrib>McMichael, John</creatorcontrib><creatorcontrib>Chao, Tu</creatorcontrib><creatorcontrib>Naffouje, Samer</creatorcontrib><creatorcontrib>Augustin, Toms</creatorcontrib><creatorcontrib>Joyce, Daniel</creatorcontrib><creatorcontrib>Simon, Robert</creatorcontrib><creatorcontrib>Walsh, R Matthew</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Jenny H.</au><au>Perlmutter, Breanna C.</au><au>Wehrle, Chase</au><au>Naples, Robert</au><au>Stackhouse, Kathryn</au><au>McMichael, John</au><au>Chao, Tu</au><au>Naffouje, Samer</au><au>Augustin, Toms</au><au>Joyce, Daniel</au><au>Simon, Robert</au><au>Walsh, R Matthew</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Natural history and growth prediction model of pancreatic serous cystic neoplasms</atitle><jtitle>Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]</jtitle><addtitle>Pancreatology</addtitle><date>2024-05</date><risdate>2024</risdate><volume>24</volume><issue>3</issue><spage>489</spage><epage>492</epage><pages>489-492</pages><issn>1424-3903</issn><eissn>1424-3911</eissn><abstract>Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection.
Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package.
Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5–160 mm), with a mean follow-up of 72 months (range 3–266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals.
SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.</abstract><cop>Switzerland</cop><pub>Elsevier B.V</pub><pmid>38443232</pmid><doi>10.1016/j.pan.2024.02.016</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-6546-3909</orcidid><orcidid>https://orcid.org/0000-0001-7070-1345</orcidid><orcidid>https://orcid.org/0000-0002-5146-8725</orcidid><orcidid>https://orcid.org/0000-0003-4973-9597</orcidid><orcidid>https://orcid.org/0000-0002-4592-0500</orcidid><orcidid>https://orcid.org/0000-0003-2655-6333</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1424-3903 |
| ispartof | Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], 2024-05, Vol.24 (3), p.489-492 |
| issn | 1424-3903 1424-3911 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2938285483 |
| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Cystadenoma, Serous - surgery Humans Neoplasms, Cystic, Mucinous, and Serous Pancreatic Cyst - surgery Pancreatic cysts Pancreatic Neoplasms - pathology Predictive growth nomogram Serous cystadenoma Serous cystic neoplasm Surgical management |
| title | Natural history and growth prediction model of pancreatic serous cystic neoplasms |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T22%3A08%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Natural%20history%20and%20growth%20prediction%20model%20of%20pancreatic%20serous%20cystic%20neoplasms&rft.jtitle=Pancreatology%20:%20official%20journal%20of%20the%20International%20Association%20of%20Pancreatology%20(IAP)%20...%20%5Bet%20al.%5D&rft.au=Chang,%20Jenny%20H.&rft.date=2024-05&rft.volume=24&rft.issue=3&rft.spage=489&rft.epage=492&rft.pages=489-492&rft.issn=1424-3903&rft.eissn=1424-3911&rft_id=info:doi/10.1016/j.pan.2024.02.016&rft_dat=%3Cproquest_cross%3E2938285483%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2938285483&rft_id=info:pmid/38443232&rft_els_id=S1424390324000607&rfr_iscdi=true |