Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort
Objective:. This study aimed to (1) develop a machine learning (ML) model that predicts the textbook outcome in liver surgery (TOLS) using preoperative variables and (2) validate the TOLS criteria by determining whether TOLS is associated with long-term survival after hepatectomy. Background:. Textb...
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Wolters Kluwer Health
2025-03-01
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| Series: | Annals of Surgery Open |
| Online Access: | http://journals.lww.com/10.1097/AS9.0000000000000539 |
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| author | Jane Wang, MD Amir Ashraf Ganjouei, MD, MPH Taizo Hibi, MD, PhD Nuria Lluis, MD, PhD Camilla Gomes, MD Fernanda Romero-Hernandez, MD Han Yin, BA Lucia Calthorpe, MD Yukiyasu Okamura, MD, PhD Yuta Abe, MD, PhD Shogo Tanaka, MD, PhD Minoru Tanabe, MD, PhD Zeniche Morise, MD, PhD Horacio Asbun, MD, PhD David Geller, MD Mohammed Abu Hilal, MD, PhD Mohamed Adam, MD Adnan Alseidi, MD, EdM International Hepatectomy Study Group Alison Baskin Annie Wong-On-Wing Annie Yang Devesh Sharma Taisuke Imamura Masanori Nakamura Yuya Miura Koki Hayashi Masatsugu Ishii Keita Shimata Kazuya Hirukawa Hiroki Ueda June S. Peng Lucas Thornblade Kenzo Hirose Kimberly Kirkwood Eric Nakakura Carlos Corvera Ajay Maker |
| author_facet | Jane Wang, MD Amir Ashraf Ganjouei, MD, MPH Taizo Hibi, MD, PhD Nuria Lluis, MD, PhD Camilla Gomes, MD Fernanda Romero-Hernandez, MD Han Yin, BA Lucia Calthorpe, MD Yukiyasu Okamura, MD, PhD Yuta Abe, MD, PhD Shogo Tanaka, MD, PhD Minoru Tanabe, MD, PhD Zeniche Morise, MD, PhD Horacio Asbun, MD, PhD David Geller, MD Mohammed Abu Hilal, MD, PhD Mohamed Adam, MD Adnan Alseidi, MD, EdM International Hepatectomy Study Group Alison Baskin Annie Wong-On-Wing Annie Yang Devesh Sharma Taisuke Imamura Masanori Nakamura Yuya Miura Koki Hayashi Masatsugu Ishii Keita Shimata Kazuya Hirukawa Hiroki Ueda June S. Peng Lucas Thornblade Kenzo Hirose Kimberly Kirkwood Eric Nakakura Carlos Corvera Ajay Maker |
| author_sort | Jane Wang, MD |
| collection | DOAJ |
| description | Objective:. This study aimed to (1) develop a machine learning (ML) model that predicts the textbook outcome in liver surgery (TOLS) using preoperative variables and (2) validate the TOLS criteria by determining whether TOLS is associated with long-term survival after hepatectomy.
Background:. Textbook outcome is a composite measure that combines several favorable outcomes into a single metric and represents the optimal postoperative course. Recently, an expert panel of surgeons proposed a Delphi consensus-based definition of TOLS.
Methods:. Adult patients who underwent hepatectomies were identified from a multicenter, international cohort (2010–2022). After data preprocessing and train-test splitting (80:20), 4 models for predicting TOLS were trained and tested. Following model optimization, the performance of the models was evaluated using receiver operating characteristic curves, and a web-based calculator was developed. In addition, a multivariable Cox proportional hazards analysis was conducted to determine the association between TOLS and overall survival (OS).
Results:. A total of 2059 patients were included, with 62.8% meeting the criteria for TOLS. The XGBoost model, which had the best performance with an area under the curve of 0.73, was chosen for the web-based calculator. The most predictive variables for having TOLS were a minimally invasive approach, fewer lesions, lower Charlson Comorbidity Index, lower preoperative creatinine levels, and smaller lesions. In the multivariable analysis, having TOLS was associated with improved OS (hazard ratio = 0.82, P = 0.015).
Conclusions:. Our ML model can predict TOLS with acceptable discrimination. We validated the TOLS criteria by demonstrating a significant association with improved OS, thus supporting their use in informing patient care. |
| format | Article |
| id | doaj-art-02ffb26e4d3b4b6c84ef3f9aa528a2ec |
| institution | DOAJ |
| issn | 2691-3593 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wolters Kluwer Health |
| record_format | Article |
| series | Annals of Surgery Open |
| spelling | doaj-art-02ffb26e4d3b4b6c84ef3f9aa528a2ec2025-08-20T02:42:08ZengWolters Kluwer HealthAnnals of Surgery Open2691-35932025-03-0161e53910.1097/AS9.0000000000000539202503000-00014Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International CohortJane Wang, MD0Amir Ashraf Ganjouei, MD, MPH1Taizo Hibi, MD, PhD2Nuria Lluis, MD, PhD3Camilla Gomes, MD4Fernanda Romero-Hernandez, MD5Han Yin, BA6Lucia Calthorpe, MD7Yukiyasu Okamura, MD, PhD8Yuta Abe, MD, PhD9Shogo Tanaka, MD, PhD10Minoru Tanabe, MD, PhD11Zeniche Morise, MD, PhD12Horacio Asbun, MD, PhD13David Geller, MD14Mohammed Abu Hilal, MD, PhD15Mohamed Adam, MD16Adnan Alseidi, MD, EdM17International Hepatectomy Study GroupAlison BaskinAnnie Wong-On-WingAnnie YangDevesh SharmaTaisuke ImamuraMasanori NakamuraYuya MiuraKoki HayashiMasatsugu IshiiKeita ShimataKazuya HirukawaHiroki UedaJune S. PengLucas ThornbladeKenzo HiroseKimberly KirkwoodEric NakakuraCarlos CorveraAjay MakerFrom the * Department of Surgery, University of California, San Francisco, San Francisco, CAFrom the * Department of Surgery, University of California, San Francisco, San Francisco, CA† Department of Pediatric Surgery and Transplantation, Kumamoto University Graduate School of Medical Sciences, Kumamoto, Japan‡ Hepato-Biliary and Pancreas Surgery, Miami Cancer Institute, Miami, FLFrom the * Department of Surgery, University of California, San Francisco, San Francisco, CAFrom the * Department of Surgery, University of California, San Francisco, San Francisco, CAFrom the * Department of Surgery, University of California, San Francisco, San Francisco, CAFrom the * Department of Surgery, University of California, San Francisco, San Francisco, CA§ Division of Hepato-Biliary-Pancreatic Surgery, Shizuoka Cancer Center, Shizuoka, Japan‖ Department of Surgery, Keio University School of Medicine, Tokyo, Japan¶ Department of Hepato-Biliary-Pancreatic Surgery, Osaka City University Graduate School of Medicine, Osaka, Japan# Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan** Department of Surgery, Okazaki Medical Center, Fujita Health University School of Medicine, Okazaki, Japan‡ Hepato-Biliary and Pancreas Surgery, Miami Cancer Institute, Miami, FL†† Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA‡‡ Department of General Surgery, Poliambulanza Foundation Hospital, Brescia, Italy.From the * Department of Surgery, University of California, San Francisco, San Francisco, CAFrom the * Department of Surgery, University of California, San Francisco, San Francisco, CAObjective:. This study aimed to (1) develop a machine learning (ML) model that predicts the textbook outcome in liver surgery (TOLS) using preoperative variables and (2) validate the TOLS criteria by determining whether TOLS is associated with long-term survival after hepatectomy. Background:. Textbook outcome is a composite measure that combines several favorable outcomes into a single metric and represents the optimal postoperative course. Recently, an expert panel of surgeons proposed a Delphi consensus-based definition of TOLS. Methods:. Adult patients who underwent hepatectomies were identified from a multicenter, international cohort (2010–2022). After data preprocessing and train-test splitting (80:20), 4 models for predicting TOLS were trained and tested. Following model optimization, the performance of the models was evaluated using receiver operating characteristic curves, and a web-based calculator was developed. In addition, a multivariable Cox proportional hazards analysis was conducted to determine the association between TOLS and overall survival (OS). Results:. A total of 2059 patients were included, with 62.8% meeting the criteria for TOLS. The XGBoost model, which had the best performance with an area under the curve of 0.73, was chosen for the web-based calculator. The most predictive variables for having TOLS were a minimally invasive approach, fewer lesions, lower Charlson Comorbidity Index, lower preoperative creatinine levels, and smaller lesions. In the multivariable analysis, having TOLS was associated with improved OS (hazard ratio = 0.82, P = 0.015). Conclusions:. Our ML model can predict TOLS with acceptable discrimination. We validated the TOLS criteria by demonstrating a significant association with improved OS, thus supporting their use in informing patient care.http://journals.lww.com/10.1097/AS9.0000000000000539 |
| spellingShingle | Jane Wang, MD Amir Ashraf Ganjouei, MD, MPH Taizo Hibi, MD, PhD Nuria Lluis, MD, PhD Camilla Gomes, MD Fernanda Romero-Hernandez, MD Han Yin, BA Lucia Calthorpe, MD Yukiyasu Okamura, MD, PhD Yuta Abe, MD, PhD Shogo Tanaka, MD, PhD Minoru Tanabe, MD, PhD Zeniche Morise, MD, PhD Horacio Asbun, MD, PhD David Geller, MD Mohammed Abu Hilal, MD, PhD Mohamed Adam, MD Adnan Alseidi, MD, EdM International Hepatectomy Study Group Alison Baskin Annie Wong-On-Wing Annie Yang Devesh Sharma Taisuke Imamura Masanori Nakamura Yuya Miura Koki Hayashi Masatsugu Ishii Keita Shimata Kazuya Hirukawa Hiroki Ueda June S. Peng Lucas Thornblade Kenzo Hirose Kimberly Kirkwood Eric Nakakura Carlos Corvera Ajay Maker Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort Annals of Surgery Open |
| title | Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort |
| title_full | Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort |
| title_fullStr | Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort |
| title_full_unstemmed | Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort |
| title_short | Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort |
| title_sort | development and validation of a machine learning prediction model for textbook outcome in liver surgery results from a multicenter international cohort |
| url | http://journals.lww.com/10.1097/AS9.0000000000000539 |
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