Altaf Abdullah, Endo Yutaka, Guglielmi Alfredo, Aldrighetti Luca, Bauer Todd W, Marques Hugo P, Martel Guillaume, Alexandrescu Sorin, Weiss Mathew J, Kitago Minoru, Poultsides George, Maithel Shishir K, Pulitano Carlo, Shen Feng, Cauchy François, Koerkamp Bas G, Endo Itaru, Pawlik Timothy M
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.
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.
Surgery. 2025 Mar;179:108809. doi: 10.1016/j.surg.2024.06.059. Epub 2024 Sep 24.
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.
我们试图使用基于术前变量的人工智能（AI）模型，识别肝内胆管癌“无效”手术风险的患者。
从多机构数据库中识别出1990年至2020年间接受肝内胆管癌切除术的患者。无效被定义为术后12个月内死亡或复发。使用各种机器学习和深度学习技术来开发无效手术的预测模型。
总共纳入了827例肝内胆管癌患者。在378例（45.7%）接受无效手术的患者中，297例（78.6%）发生肝内胆管癌复发，81例（21.4%）在手术切除后12个月内死亡。一个由多层感知器和梯度提升分类器组成的集成模型使用10个术前因素，在训练队列和测试队列中的受试者操作特征曲线下面积分别为0.830（95%置信区间0.798 - 0.861）和0.781（95%置信区间0.707 - 0.853），显示出最高的准确性。该模型的敏感性和特异性分别为64.5%和80.0%，阳性预测值和阴性预测值分别为73.1%和72.7%。放射学肿瘤负荷评分、血清糖类抗原19 - 9和直接胆红素水平是最能预测无效手术的因素。基于人工智能的模型可在线获取，便于使用和临床应用（https://altaf-pawlik-icc-futilityofsurgery-calculator.streamlit.app/）。
人工智能集成模型在术前识别肝内胆管癌无效手术高风险患者方面显示出高准确性。基于人工智能的预测模型可为临床医生提供可靠的术前指导，并有助于避免不太可能给患者带来长期益处的无效手术。
