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
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.