Publicação
International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning
| Resumo: | Background: Radical gastrectomy remains the main treatment for gastric cancer, despite its high mortality. A clinical predictive model of 90-day mortality (90DM) risk after gastric cancer surgery based on the Spanish EURECCA registry database was developed using a matching learning algorithm. We performed an external validation of this model based on data from an international multicenter cohort of patients. Methods: A cohort of patients from the European GASTRODATA database was selected. Demographic, clinical, and treatment variables in the original and validation cohorts were compared. The performance of the model was evaluated using the area under the curve (AUC) for a random forest model. Results: The validation cohort included 2546 patients from 24 European hospitals. The advanced clinical T- and N-category, neoadjuvant therapy, open procedures, total gastrectomy rates, and mean volume of the centers were significantly higher in the validation cohort. The 90DM rate was also higher in the validation cohort (5.6%) vs. the original cohort (3.7%). The AUC in the validation model was 0.716. Conclusion: The externally validated model for predicting the 90DM risk in gastric cancer patients undergoing gastrectomy with curative intent continues to be as useful as the original model in clinical practice. |
|---|---|
| Autores principais: | Dal Cero, Mariagiulia |
| Outros Autores: | Gibert, Joan; Grande, Luis; Gimeno, Marta; Osorio, Javier; Bencivenga, Maria; Fumagalli Romario, Uberto; Rosati, Riccardo; Morgagni, Paolo; Gisbertz, Suzanne; Polkowski, Wojciech P.; Lara Santos, Lucio; Kołodziejczyk, Piotr; Kielan, Wojciech; Reddavid, Rossella; van Sandick, Johanna W.; Baiocchi, Gian Luca; Gockel, Ines; Davies, Andrew; Wijnhoven, Bas P. L.; Reim, Daniel; Costa, Paulo M.; Allum, William H.; Piessen, Guillaume; Reynolds, John V.; Mönig, Stefan P.; Schneider, Paul M.; Garsot, Elisenda; Eizaguirre, Emma; Miró, Mònica; Castro, Sandra; Miranda, Coro; Monzonis-Hernández, Xavier; Pera, Manuel |
| Assunto: | Gastrectomy Gastric cancer Machine learning Mortality Prediction Validation |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| _version_ | 1866809781266677760 |
|---|---|
| author | Dal Cero, Mariagiulia |
| author2 | Gibert, Joan Grande, Luis Gimeno, Marta Osorio, Javier Bencivenga, Maria Fumagalli Romario, Uberto Rosati, Riccardo Morgagni, Paolo Gisbertz, Suzanne Polkowski, Wojciech P. Lara Santos, Lucio Kołodziejczyk, Piotr Kielan, Wojciech Reddavid, Rossella van Sandick, Johanna W. Baiocchi, Gian Luca Gockel, Ines Davies, Andrew Wijnhoven, Bas P. L. Reim, Daniel Costa, Paulo M. Allum, William H. Piessen, Guillaume Reynolds, John V. Mönig, Stefan P. Schneider, Paul M. Garsot, Elisenda Eizaguirre, Emma Miró, Mònica Castro, Sandra Miranda, Coro Monzonis-Hernández, Xavier Pera, Manuel |
| author2_role | author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| author_facet | Dal Cero, Mariagiulia Gibert, Joan Grande, Luis Gimeno, Marta Osorio, Javier Bencivenga, Maria Fumagalli Romario, Uberto Rosati, Riccardo Morgagni, Paolo Gisbertz, Suzanne Polkowski, Wojciech P. Lara Santos, Lucio Kołodziejczyk, Piotr Kielan, Wojciech Reddavid, Rossella van Sandick, Johanna W. Baiocchi, Gian Luca Gockel, Ines Davies, Andrew Wijnhoven, Bas P. L. Reim, Daniel Costa, Paulo M. Allum, William H. Piessen, Guillaume Reynolds, John V. Mönig, Stefan P. Schneider, Paul M. Garsot, Elisenda Eizaguirre, Emma Miró, Mònica Castro, Sandra Miranda, Coro Monzonis-Hernández, Xavier Pera, Manuel |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Dal Cero, Mariagiulia\"},{\"Person.name\":\"Gibert, Joan\"},{\"Person.name\":\"Grande, Luis\"},{\"Person.name\":\"Gimeno, Marta\"},{\"Person.name\":\"Osorio, Javier\"},{\"Person.name\":\"Bencivenga, Maria\"},{\"Person.name\":\"Fumagalli Romario, Uberto\"},{\"Person.name\":\"Rosati, Riccardo\"},{\"Person.name\":\"Morgagni, Paolo\"},{\"Person.name\":\"Gisbertz, Suzanne\"},{\"Person.name\":\"Polkowski, Wojciech P.\"},{\"Person.name\":\"Lara Santos, Lucio\"},{\"Person.name\":\"Kołodziejczyk, Piotr\"},{\"Person.name\":\"Kielan, Wojciech\"},{\"Person.name\":\"Reddavid, Rossella\"},{\"Person.name\":\"van Sandick, Johanna W.\"},{\"Person.name\":\"Baiocchi, Gian Luca\"},{\"Person.name\":\"Gockel, Ines\"},{\"Person.name\":\"Davies, Andrew\"},{\"Person.name\":\"Wijnhoven, Bas P. L.\"},{\"Person.name\":\"Reim, Daniel\"},{\"Person.name\":\"Costa, Paulo M.\",\"Person.identifier.orcid\":\"0000-0002-7550-8285\"},{\"Person.name\":\"Allum, William H.\"},{\"Person.name\":\"Piessen, Guillaume\"},{\"Person.name\":\"Reynolds, John V.\"},{\"Person.name\":\"Mönig, Stefan P.\"},{\"Person.name\":\"Schneider, Paul M.\"},{\"Person.name\":\"Garsot, Elisenda\"},{\"Person.name\":\"Eizaguirre, Emma\"},{\"Person.name\":\"Miró, Mònica\"},{\"Person.name\":\"Castro, Sandra\"},{\"Person.name\":\"Miranda, Coro\"},{\"Person.name\":\"Monzonis-Hernández, Xavier\"},{\"Person.name\":\"Pera, Manuel\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Dal Cero, Mariagiulia Gibert, Joan Grande, Luis Gimeno, Marta Osorio, Javier Bencivenga, Maria Fumagalli Romario, Uberto Rosati, Riccardo Morgagni, Paolo Gisbertz, Suzanne Polkowski, Wojciech P. Lara Santos, Lucio Kołodziejczyk, Piotr Kielan, Wojciech Reddavid, Rossella van Sandick, Johanna W. Baiocchi, Gian Luca Gockel, Ines Davies, Andrew Wijnhoven, Bas P. L. Reim, Daniel Costa, Paulo M. Allum, William H. Piessen, Guillaume Reynolds, John V. Mönig, Stefan P. Schneider, Paul M. Garsot, Elisenda Eizaguirre, Emma Miró, Mònica Castro, Sandra Miranda, Coro Monzonis-Hernández, Xavier Pera, Manuel |
| datacite.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-11-19T14:39:26Z |
| datacite.date.embargoed.fl_str_mv | 2024-11-19T14:39:26Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Gastrectomy Gastric cancer Machine learning Mortality Prediction Validation |
| datacite.titles.title.fl_str_mv | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Dal Cero, Mariagiulia Gibert, Joan Grande, Luis Gimeno, Marta Osorio, Javier Bencivenga, Maria Fumagalli Romario, Uberto Rosati, Riccardo Morgagni, Paolo Gisbertz, Suzanne Polkowski, Wojciech P. Lara Santos, Lucio Kołodziejczyk, Piotr Kielan, Wojciech Reddavid, Rossella van Sandick, Johanna W. Baiocchi, Gian Luca Gockel, Ines Davies, Andrew Wijnhoven, Bas P. L. Reim, Daniel Costa, Paulo M. Allum, William H. Piessen, Guillaume Reynolds, John V. Mönig, Stefan P. Schneider, Paul M. Garsot, Elisenda Eizaguirre, Emma Miró, Mònica Castro, Sandra Miranda, Coro Monzonis-Hernández, Xavier Pera, Manuel |
| dc.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-11-19T14:39:26Z |
| dc.date.embargoed.fl_str_mv | 2024-11-19T14:39:26Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10400.5/95436 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Gastrectomy Gastric cancer Machine learning Mortality Prediction Validation |
| dc.title.fl_str_mv | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Background: Radical gastrectomy remains the main treatment for gastric cancer, despite its high mortality. A clinical predictive model of 90-day mortality (90DM) risk after gastric cancer surgery based on the Spanish EURECCA registry database was developed using a matching learning algorithm. We performed an external validation of this model based on data from an international multicenter cohort of patients. Methods: A cohort of patients from the European GASTRODATA database was selected. Demographic, clinical, and treatment variables in the original and validation cohorts were compared. The performance of the model was evaluated using the area under the curve (AUC) for a random forest model. Results: The validation cohort included 2546 patients from 24 European hospitals. The advanced clinical T- and N-category, neoadjuvant therapy, open procedures, total gastrectomy rates, and mean volume of the centers were significantly higher in the validation cohort. The 90DM rate was also higher in the validation cohort (5.6%) vs. the original cohort (3.7%). The AUC in the validation model was 0.716. Conclusion: The externally validated model for predicting the 90DM risk in gastric cancer patients undergoing gastrectomy with curative intent continues to be as useful as the original model in clinical practice. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/0289a768-559c-46ce-9fa8-964a5c77844c/download |
| id | ul_7b7ad36c8419fc136fd3973df18b23b5 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10400.5/95436 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10400.5/95436 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Dal Cero, Mariagiulia Gibert, Joan Grande, Luis Gimeno, Marta Osorio, Javier Bencivenga, Maria Fumagalli Romario, Uberto Rosati, Riccardo Morgagni, Paolo Gisbertz, Suzanne Polkowski, Wojciech P. Lara Santos, Lucio Kołodziejczyk, Piotr Kielan, Wojciech Reddavid, Rossella van Sandick, Johanna W. Baiocchi, Gian Luca Gockel, Ines Davies, Andrew Wijnhoven, Bas P. L. Reim, Daniel Costa, Paulo M. Costa, Paulo M. https://www.ciencia-id.pt/0415-4404-DDBE 0415-4404-DDBE http://orcid.org/0000-0002-7550-8285 0000-0002-7550-8285 Allum, William H. Piessen, Guillaume Reynolds, John V. Mönig, Stefan P. Schneider, Paul M. Garsot, Elisenda Eizaguirre, Emma Miró, Mònica Castro, Sandra Miranda, Coro Monzonis-Hernández, Xavier Pera, Manuel |
| publishDate | 2024 |
| publisher.none.fl_str_mv | MDPI |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engMDPIpt_PTBackground: Radical gastrectomy remains the main treatment for gastric cancer, despite its high mortality. A clinical predictive model of 90-day mortality (90DM) risk after gastric cancer surgery based on the Spanish EURECCA registry database was developed using a matching learning algorithm. We performed an external validation of this model based on data from an international multicenter cohort of patients. Methods: A cohort of patients from the European GASTRODATA database was selected. Demographic, clinical, and treatment variables in the original and validation cohorts were compared. The performance of the model was evaluated using the area under the curve (AUC) for a random forest model. Results: The validation cohort included 2546 patients from 24 European hospitals. The advanced clinical T- and N-category, neoadjuvant therapy, open procedures, total gastrectomy rates, and mean volume of the centers were significantly higher in the validation cohort. The 90DM rate was also higher in the validation cohort (5.6%) vs. the original cohort (3.7%). The AUC in the validation model was 0.716. Conclusion: The externally validated model for predicting the 90DM risk in gastric cancer patients undergoing gastrectomy with curative intent continues to be as useful as the original model in clinical practice.application/pdfpt_PTInternational external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learningDal Cero, MariagiuliaGibert, JoanGrande, LuisGimeno, MartaOsorio, JavierBencivenga, MariaFumagalli Romario, UbertoRosati, RiccardoMorgagni, PaoloGisbertz, SuzannePolkowski, Wojciech P.Lara Santos, LucioKołodziejczyk, PiotrKielan, WojciechReddavid, Rossellavan Sandick, Johanna W.Baiocchi, Gian LucaGockel, InesDavies, AndrewWijnhoven, Bas P. L.Reim, DanielPersonalCosta, Paulo M.DSpacehttp://dspace.org/items/7f62714b-b0f1-4d22-a3a5-de04bc736ccaDSpacehttp://dspace.org/items/7f62714b-b0f1-4d22-a3a5-de04bc736ccaCostaPaulo MatosCiência IDhttps://www.ciencia-id.pt0415-4404-DDBEORCIDhttp://orcid.org0000-0002-7550-8285Researcher IDhttps://www.researcherid.comABD-1573-2021Scopus Author IDhttps://www.scopus.com55977165500Scopus Author IDhttps://www.scopus.com57211860958Allum, William H.Piessen, GuillaumeReynolds, John V.Mönig, Stefan P.Schneider, Paul M.Garsot, ElisendaEizaguirre, EmmaMiró, MònicaCastro, SandraMiranda, CoroMonzonis-Hernández, XavierPera, ManuelHostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptDOIIsPartOf10.3390/cancers161324632024-11-19T14:39:26Z20242024-01-01T00:00:00ZHandlehttp://hdl.handle.net/10400.5/95436http://purl.org/coar/access_right/c_abf2open accessGastrectomyGastric cancerMachine learningMortalityPredictionValidation1010653 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2024http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/0289a768-559c-46ce-9fa8-964a5c77844c/downloadCancers1613 |
| spellingShingle | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning Dal Cero, Mariagiulia Gastrectomy Gastric cancer Machine learning Mortality Prediction Validation |
| status | SINGLETON |
| subject.fl_str_mv | Gastrectomy Gastric cancer Machine learning Mortality Prediction Validation |
| title | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| title_full | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| title_fullStr | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| title_full_unstemmed | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| title_short | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| title_sort | International external validation of risk prediction model of 90-day mortality after gastrectomy for cancer using machine learning |
| topic | Gastrectomy Gastric cancer Machine learning Mortality Prediction Validation |
| topic_facet | Gastrectomy Gastric cancer Machine learning Mortality Prediction Validation |
| url | http://hdl.handle.net/10400.5/95436 |
| visible | 1 |