Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach.

colorectal liver metastasis early recurrence extrahepatic recurrence machine learning online calculator predictive model

Journal

World journal of surgery
ISSN: 1432-2323
Titre abrégé: World J Surg
Pays: United States
ID NLM: 7704052

Informations de publication

Date de publication:
19 Oct 2024
Historique:
received: 27 06 2024
accepted: 06 10 2024
medline: 19 10 2024
pubmed: 19 10 2024
entrez: 19 10 2024
Statut: aheadofprint

Résumé

Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM). Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12 months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values. Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4 months (interquartile range [IQR] 29.9-46.7) versus 120.5 months (IQR 97.2-134.0), respectively (p < 0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/. An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.

Sections du résumé

BACKGROUND BACKGROUND
Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM).
METHODS METHODS
Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12 months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values.
RESULTS RESULTS
Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4 months (interquartile range [IQR] 29.9-46.7) versus 120.5 months (IQR 97.2-134.0), respectively (p < 0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/.
CONCLUSIONS CONCLUSIONS
An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.

Identifiants

pubmed: 39425666
doi: 10.1002/wjs.12376
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024 The Author(s). World Journal of Surgery published by John Wiley & Sons Ltd on behalf of International Society of Surgery/Société Internationale de Chirurgie (ISS/SIC).

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Auteurs

Jun Kawashima (J)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.
Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan.

Yutaka Endo (Y)

Department of Surgery, University of Rochester, Rochester, New York, USA.

Selamawit Woldesenbet (S)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Odysseas P Chatzipanagiotou (OP)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Diamantis I Tsilimigras (DI)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Giovanni Catalano (G)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.
Department of Surgery, University of Verona, Verona, Italy.

Muhammad Muntazir Mehdi Khan (MMM)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Zayed Rashid (Z)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Mujtaba Khalil (M)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Abdullah Altaf (A)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Muhammad Musaab Munir (MM)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Alfredo Guglielmi (A)

Department of Surgery, University of Verona, Verona, Italy.

Andrea Ruzzenente (A)

Department of Surgery, University of Verona, Verona, Italy.

Luca Aldrighetti (L)

Department of Surgery, Ospedale San Raffaele, Milan, Italy.

Sorin Alexandrescu (S)

Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania.

Minoru Kitago (M)

Department of Surgery, Keio University, Tokyo, Japan.

George Poultsides (G)

Department of Surgery, Stanford University, Stanford, California, USA.

Kazunari Sasaki (K)

Department of Surgery, Stanford University, Stanford, California, USA.

Federico Aucejo (F)

Department of General Surgery, Cleveland Clinic Foundation, Cleveland, Ohio, USA.

Itaru Endo (I)

Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan.

Timothy M Pawlik (TM)

Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, Ohio, USA.

Classifications MeSH