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

IF 3.2 2区医学 Q1 SURGERY

Surgery Pub Date : 2024-09-24 DOI:10.1016/j.surg.2024.06.059

Abdullah Altaf MD , Yutaka Endo MD, PhD , Alfredo Guglielmi MD , Luca Aldrighetti MD , Todd W. Bauer MD , Hugo P. Marques MD , Guillaume Martel MD , Sorin Alexandrescu MD , Mathew J. Weiss MD , Minoru Kitago MD , George Poultsides MD , Shishir K. Maithel MD , Carlo Pulitano MD , Feng Shen MD , François Cauchy MD , Bas G. Koerkamp MD , Itaru Endo MD , Timothy M. Pawlik MD, PhD, MPH, MTS, MBA, FACS, FSSO, FRACS (Hon)

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引用次数: 0

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.

查看原文本刊更多论文

肝内胆管癌的前期手术：利用人工智能预测徒劳。

目的我们试图利用基于术前变量的人工智能（AI）模型来识别有 "徒劳 "手术风险的肝内胆管癌患者：从一个多机构数据库中找出了1990年至2020年间接受切除术的肝内胆管癌患者。死亡率或术后 12 个月内复发均被定义为有期徒刑。利用各种机器学习和深度学习技术开发了无效手术预测模型：共纳入了827例肝内胆管癌患者。在378例（45.7%）无效手术患者中，297例（78.6%）出现肝内胆管癌复发，81例（21.4%）在手术切除后12个月内死亡。由多层感知器和梯度提升分类器组成的集合模型使用了10个术前因素，显示出最高的准确性，训练组和测试组的接收者操作特征曲线下面积分别为0.830（95%置信区间为0.798-0.861）和0.781（95%置信区间为0.707-0.853）。该模型的灵敏度和特异度分别为 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、血清碳水化合物抗原 19-9 和直接胆红素水平是预测无效手术最有力的因素。基于人工智能的模型可在线使用，便于临床应用（https://altaf-pawlik-icc-futilityofsurgery-calculator.streamlit.app/）。结论：结论：人工智能组合模型在术前识别肝内胆管癌高危无效手术患者方面具有很高的准确性。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，有助于避免对患者不可能带来长期益处的徒劳手术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Surgery 医学-外科

CiteScore

5.40

自引率

5.30%

发文量

687

审稿时长

64 days

期刊介绍： For 66 years, Surgery has published practical, authoritative information about procedures, clinical advances, and major trends shaping general surgery. Each issue features original scientific contributions and clinical reports. Peer-reviewed articles cover topics in oncology, trauma, gastrointestinal, vascular, and transplantation surgery. The journal also publishes papers from the meetings of its sponsoring societies, the Society of University Surgeons, the Central Surgical Association, and the American Association of Endocrine Surgeons.