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

Abdullah Altaf; Yutaka Endo; Alfredo Guglielmi; Luca Aldrighetti; Todd W Bauer; Hugo P Marques; Guillaume Martel; Sorin Alexandrescu; Mathew J Weiss; Minoru Kitago; George Poultsides; Shishir K Maithel; Carlo Pulitano; Feng Shen; François Cauchy; Bas G Koerkamp; Itaru Endo; Timothy M Pawlik

doi:10.1016/j.surg.2024.06.059

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

Surgery. 2024 Sep 24:S0039-6060(24)00670-6. doi: 10.1016/j.surg.2024.06.059. Online ahead of print.

Authors

Abdullah Altaf¹, Yutaka Endo², Alfredo Guglielmi³, Luca Aldrighetti⁴, Todd W Bauer⁵, Hugo P Marques⁶, Guillaume Martel⁷, Sorin Alexandrescu⁸, Mathew J Weiss⁹, Minoru Kitago¹⁰, George Poultsides¹¹, Shishir K Maithel¹², Carlo Pulitano¹³, Feng Shen¹⁴, François Cauchy¹⁵, Bas G Koerkamp¹⁶, Itaru Endo¹⁷, Timothy M Pawlik¹⁸

Affiliations

¹ 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.
² 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.
³ Department of Surgery, University of Verona, Verona, Italy.
⁴ Department of Surgery, San Raffaele Hospital, Milan, Italy.
⁵ Department of Surgery, University of Virginia School of Medicine, Charlottesville, VA.
⁶ Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal.
⁷ Department of Surgery, University of Ottawa, Ottawa, ON, Canada.
⁸ Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania.
⁹ Department of Surgery, Johns Hopkins Medicine, Baltimore, MD.
¹⁰ Department of Surgery, Keio University, Tokyo, Japan.
¹¹ Department of Surgery, Stanford University School of Medicine, Stanford, CA.
¹² Department of Surgery, Emory University School of Medicine, Atlanta, GA.
¹³ Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia.
¹⁴ Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China.
¹⁵ Department of Surgery, AP-HP, Beaujon Hospital, Clichy, France.
¹⁶ Department of Surgery, Erasmus University Medical Centre, Rotterdam, Netherlands.
¹⁷ Department of Surgery, Yokohama City University School of Medicine, Yokohama, Japan.
¹⁸ Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH. Electronic address: [email protected].

PMID: 39322483
DOI: 10.1016/j.surg.2024.06.059

Abstract

Objective: We sought to identify patients at risk of "futile" surgery for intrahepatic cholangiocarcinoma using an artificial intelligence (AI)-based model based on preoperative variables.

Methods: 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.

Results: 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/).

Conclusion: 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.