f901ca5f2a5ec1188c9c9879aaa7f664 cancers-16-02463-v2.pdf 7dc05dedc08f43d72c59d39b4e7d302eac3f3172 cancers-16-02463-v2.pdf 0de727d611cd7bab547643047197e4ffd11bb185606adc253d3a57cadae4690a cancers-16-02463-v2.pdf Title: International External Validation of Risk Prediction Model of 90-Day Mortality after Gastrectomy for Cancer Using Machine Learning Subject: 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. Keywords: gastric cancer; gastrectomy; mortality; prediction; machine learning; validation Author: Mariagiulia Dal Cero, Joan Gibert, Luis Grande, Marta Gimeno, Javier Osorio, Maria Bencivenga, Uberto Fumagalli Romario, Riccardo Rosati, Paolo Morgagni, Suzanne Gisbertz, Wojciech P. Polkowski, Lucio Lara Santos, Piotr Kołodziejczyk, Wojciech Kielan, Rossella Reddavid, Johanna W. van Sandick, Gian Luca Baiocchi, Ines Gockel, Andrew Davies, Bas P. L. Wijnhoven, Daniel Reim, Paulo Costa, William H. Allum, Guillaume Piessen, John V. Reynolds, Stefan P. Mönig, Paul M. Schneider, Elisenda Garsot, Emma Eizaguirre, Mònica Miró, Sandra Castro, Coro Miranda, Xavier Monzonis-Hernández, Manuel Pera and on behalf of the Spanish EURECCA Esophagogastric Cancer Group and the European GASTRODATA Study Group Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Mon Jul 8 16:51:30 2024 CEST ModDate: Mon Jul 8 16:56:03 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1010653 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HDGIXW+VnURWPalladioL-Bold Type 1 Custom yes yes no 10 0 CIQLLF+VnURWPalladioL Type 1 Custom yes yes no 15 0 DXVKOS+URWPalladioL-Bold Type 1 Custom yes yes no 19 0 FVYMFN+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 BMXRPL+URWPalladioL-Ital Type 1 Custom yes yes no 28 0 LCJEVJ+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 47 0 QLBIEB+URWPalladioL-BoldItal Type 1 Custom yes yes no 74 0 GOIDCM+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 96 0 GOIDCN+PalatinoLinotype CID TrueType Identity-H yes yes yes 99 0 GOIDCO+PalatinoLinotype TrueType WinAnsi yes yes no 105 0 GOIDDA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 108 0 GOIDMN+PalatinoLinotype TrueType MacRoman yes yes no 111 0 GOIDCM+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 131 0 GOIDCN+PalatinoLinotype CID TrueType Identity-H yes yes yes 134 0 GOIDCO+PalatinoLinotype TrueType WinAnsi yes yes no 140 0 GOIDDA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 143 0 GOIDMN+PalatinoLinotype TrueType MacRoman yes yes no 146 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-05-13 02:51:24 CEST RepresentationInformation: cancers-16-02463-v2.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-05-12 15:55:46 CEST Size: 1010653 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 317 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Source of Data Destination: subsection.2.1 Children: Item: Title: Study Development Cohort Destination: subsubsection.2.1.1 Item: Title: Validation Cohort Destination: subsubsection.2.1.2 Item: Title: Ethics Destination: subsubsection.2.1.3 Item: Title: Eligibility and Primary Outcome Destination: subsubsection.2.1.4 Item: Title: Predictor Characteristics and Statistical Analysis Destination: subsubsection.2.1.5 Item: Title: External Validation of the Predictive Model Destination: subsubsection.2.1.6 Item: Title: Results Destination: section.3 Children: Item: Title: Model Performance: Discrimination Destination: subsection.3.1 Item: Title: Variable Importance Destination: subsection.3.2 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: Appendix A Destination: section.A. Item: Title: References Destination: section.B. Info: Title: International External Validation of Risk Prediction Model of 90-Day Mortality after Gastrectomy for Cancer Using Machine Learning Author: Mariagiulia Dal Cero, Joan Gibert, Luis Grande, Marta Gimeno, Javier Osorio, Maria Bencivenga, Uberto Fumagalli Romario, Riccardo Rosati, Paolo Morgagni, Suzanne Gisbertz, Wojciech P. Polkowski, Lucio Lara Santos, Piotr Kołodziejczyk, Wojciech Kielan, Rossella Reddavid, Johanna W. van Sandick, Gian Luca Baiocchi, Ines Gockel, Andrew Davies, Bas P. L. Wijnhoven, Daniel Reim, Paulo Costa, William H. Allum, Guillaume Piessen, John V. Reynolds, Stefan P. Mönig, Paul M. Schneider, Elisenda Garsot, Emma Eizaguirre, Mònica Miró, Sandra Castro, Coro Miranda, Xavier Monzonis-Hernández, Manuel Pera and on behalf of the Spanish EURECCA Esophagogastric Cancer Group and the European GASTRODATA Study Group Subject: 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. 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