食管癌手术吻合口漏的预测:整合成像和临床数据的多模态机器学习模型

IF 3.8 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Michail E. Klontzas , Motonari Ri , Emmanouil Koltsakis , Erik Stenqvist , Georgios Kalarakis , Erik Boström , Aristotelis Kechagias , Dimitrios Schizas , Ioannis Rouvelas , Antonios Tzortzakakis
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引用次数: 0

摘要

理由和目标:手术结合化疗/放疗是治疗局部晚期食管癌的标准方法。即使在引入微创技术后,食管切除术仍会带来显著的发病率和死亡率。食管切除术最常见、最令人担忧的并发症之一就是吻合口漏(AL)。我们的工作旨在开发一种多模态机器学习模型,该模型结合了 CT 导出数据和临床数据,用于预测食管癌食管切除术后的 AL:共纳入471例前瞻性患者(2010年1月至2022年12月)。术前计算机断层扫描(CT)用于评估糜烂干狭窄和血管钙化。将人口统计学、疾病分期、手术细节、术后 CRP 和分期等临床变量与 CT 数据相结合,建立 AL 预测模型。数据被分成 80%:20% 用于训练和测试,并通过 10 倍交叉验证和早期停止建立了 XGBoost 模型。计算了ROC曲线和各自的曲线下面积(AUC)、灵敏度、特异性、PPV、NPV和F1分数:共有 117 名患者(24.8%)出现术后 AL。XGboost 模型的 AUC 为 79.2%(95%CI 69%-89.4%),特异性为 77.46%,灵敏度为 65.22%,PPV 为 48.39%,NPV 为 87.3%,F1 分数为 56%。Shapley Additive exPlanation 分析显示了各个变量对模型结果的影响。决策曲线分析表明,该模型尤其适用于阈值概率在 15% 到 48% 之间的情况:与临床相关的多模态模型可以预测 AL,这对临床上 AL 概率较低的病例尤其有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Anastomotic Leakage in Esophageal Cancer Surgery: A Multimodal Machine Learning Model Integrating Imaging and Clinical Data

Rationale and Objectives

Surgery in combination with chemo/radiotherapy is the standard treatment for locally advanced esophageal cancer. Even after the introduction of minimally invasive techniques, esophagectomy carries significant morbidity and mortality. One of the most common and feared complications of esophagectomy is anastomotic leakage (AL). Our work aimed to develop a multimodal machine-learning model combining CT-derived and clinical data for predicting AL following esophagectomy for esophageal cancer.

Material and Methods

A total of 471 patients were prospectively included (Jan 2010–Dec 2022). Preoperative computed tomography (CT) was used to evaluate celia trunk stenosis and vessel calcification. Clinical variables, including demographics, disease stage, operation details, postoperative CRP, and stage, were combined with CT data to build a model for AL prediction. Data was split into 80%:20% for training and testing, and an XGBoost model was developed with 10-fold cross-validation and early stopping. ROC curves and respective areas under the curve (AUC), sensitivity, specificity, PPV, NPV, and F1-scores were calculated.

Results

A total of 117 patients (24.8%) exhibited post-operative AL. The XGboost model achieved an AUC of 79.2% (95%CI 69%–89.4%) with a specificity of 77.46%, a sensitivity of 65.22%, PPV of 48.39%, NPV of 87.3%, and F1-score of 56%. Shapley Additive exPlanation analysis showed the effect of individual variables on the result of the model. Decision curve analysis showed that the model was particularly beneficial for threshold probabilities between 15% and 48%.

Conclusion

A clinically relevant multimodal model can predict AL, which is especially valuable in cases with low clinical probability of AL.
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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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