Upfront surgery for intrahepatic cholangiocarcinoma: Prediction of futility using artificial intelligence.


Journal

Surgery
ISSN: 1532-7361
Titre abrégé: Surgery
Pays: United States
ID NLM: 0417347

Informations de publication

Date de publication:
24 Sep 2024
Historique:
received: 26 03 2024
revised: 15 06 2024
accepted: 18 06 2024
medline: 26 9 2024
pubmed: 26 9 2024
entrez: 25 9 2024
Statut: aheadofprint

Résumé

We sought to identify patients at risk of "futile" surgery for intrahepatic cholangiocarcinoma using an artificial intelligence (AI)-based model based on preoperative variables. Intrahepatic cholangiocarcinoma patients who underwent resection between 1990 and 2020 were identified from a multi-institutional database. Futility was defined either as mortality or recurrence within 12 months of surgery. Various machine learning and deep learning techniques were used to develop prediction models for futile surgery. Overall, 827 intrahepatic cholangiocarcinoma patients were included. Among 378 patients (45.7%) who had futile surgery, 297 patients (78.6%) developed intrahepatic cholangiocarcinoma recurrence and 81 patients (21.4%) died within 12 months of surgical resection. An ensemble model consisting of multilayer perceptron and gradient boosting classifiers that used 10 preoperative factors demonstrated the highest accuracy, with areas under receiver operating characteristic curves of 0.830 (95% confidence interval 0.798-0.861) and 0.781 (95% confidence interval 0.707-0.853) in the training and testing cohorts, respectively. The model displayed sensitivity and specificity of 64.5% and 80.0%, respectively, with positive and negative predictive values of 73.1% and 72.7%, respectively. Radiologic tumor burden score, serum carbohydrate antigen 19-9, and direct bilirubin levels were the factors most strongly predictive of futile surgery. The artificial intelligence-based model was made available online for ease of use and clinical applicability (https://altaf-pawlik-icc-futilityofsurgery-calculator.streamlit.app/). The artificial intelligence ensemble model demonstrated high accuracy to identify patients preoperatively at high risk of undergoing futile surgery for intrahepatic cholangiocarcinoma. Artificial intelligence-based prediction models can provide clinicians with reliable preoperative guidance and aid in avoiding futile surgical procedures that are unlikely to provide patients long-term benefits.

Identifiants

pubmed: 39322483
pii: S0039-6060(24)00670-6
doi: 10.1016/j.surg.2024.06.059
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflict of Interest/Disclosure The authors declare no conflict of interest.

Auteurs

Abdullah Altaf (A)

Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH. Electronic address: https://twitter.com/AbdullahAltaf97.

Yutaka Endo (Y)

Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH. Electronic address: https://twitter.com/YutakaEndoSurg.

Alfredo Guglielmi (A)

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

Luca Aldrighetti (L)

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

Todd W Bauer (TW)

Department of Surgery, University of Virginia School of Medicine, Charlottesville, VA.

Hugo P Marques (HP)

Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal.

Guillaume Martel (G)

Department of Surgery, University of Ottawa, Ottawa, ON, Canada.

Sorin Alexandrescu (S)

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

Mathew J Weiss (MJ)

Department of Surgery, Johns Hopkins Medicine, Baltimore, MD.

Minoru Kitago (M)

Department of Surgery, Keio University, Tokyo, Japan.

George Poultsides (G)

Department of Surgery, Stanford University School of Medicine, Stanford, CA.

Shishir K Maithel (SK)

Department of Surgery, Emory University School of Medicine, Atlanta, GA.

Carlo Pulitano (C)

Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia.

Feng Shen (F)

Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.

François Cauchy (F)

Department of Surgery, AP-HP, Beaujon Hospital, Clichy, France.

Bas G Koerkamp (BG)

Department of Surgery, Erasmus University Medical Centre, Rotterdam, Netherlands.

Itaru Endo (I)

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

Timothy M Pawlik (TM)

Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH. Electronic address: Tim.Pawlik@osumc.edu.

Classifications MeSH