AutoFRS: an externally validated, annotation-free approach to computational preoperative complication risk stratification in pancreatic surgery - an experimental study.

IF 12.5 2区 医学 Q1 SURGERY
Fiona R Kolbinger, Nithya Bhasker, Felix Schön, Daniel Cser, Alex Zwanenburg, Steffen Löck, Sebastian Hempel, André Schulze, Nadiia Skorobohach, Hanna M Schmeiser, Rosa Klotz, Ralf-Thorsten Hoffmann, Pascal Probst, Beat Müller, Sebastian Bodenstedt, Martin Wagner, Jürgen Weitz, Jens-Peter Kühn, Marius Distler, Stefanie Speidel
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引用次数: 0

Abstract

Background: The risk of postoperative pancreatic fistula (POPF), one of the most dreaded complications after pancreatic surgery, can be predicted from preoperative imaging and tabular clinical routine data. However, existing studies suffer from limited clinical applicability due to a need for manual data annotation and a lack of external validation. We propose AutoFRS (automated fistula risk score software), an externally validated end-to-end prediction tool for POPF risk stratification based on multimodal preoperative data.

Materials and methods: We trained AutoFRS on preoperative contrast-enhanced CT imaging and clinical data from 108 patients undergoing pancreatic head resection and validated it on an external cohort of 61 patients. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and balanced accuracy. In addition, model performance was compared to the updated alternative fistula risk score (ua-FRS), the current clinical gold standard method for intraoperative POPF risk stratification.

Results: AutoFRS achieved an AUC of 0.81 and a balanced accuracy of 0.72 in internal validation and an AUC of 0.79 and a balanced accuracy of 0.70 in external validation. In a patient subset with documented intraoperative POPF risk factors, AutoFRS (AUC: 0.84 ± 0.05) performed on par with the uaFRS (AUC: 0.85 ± 0.06). The AutoFRS web application facilitates annotation-free prediction of POPF from preoperative imaging and clinical data based on the AutoFRS prediction model.

Conclusion: POPF can be predicted from multimodal clinical routine data without human data annotation, automating the risk prediction process. We provide additional evidence of the clinical feasibility of preoperative POPF risk stratification and introduce a software pipeline for future prospective evaluation.

AutoFRS:一种外部验证的、无注释的胰腺手术术前并发症风险分层计算方法-一项实验研究。
背景:胰腺手术后最可怕的并发症之一胰瘘(POPF)的风险可以通过术前影像学和表格临床常规数据来预测。然而,由于需要手工标注数据和缺乏外部验证,现有研究的临床适用性有限。我们提出AutoFRS(自动瘘风险评分软件),这是一种外部验证的端到端预测工具,用于基于多模态术前数据的POPF风险分层。材料和方法:我们对108例胰头切除术患者的术前CT增强成像和临床数据进行了AutoFRS训练,并在61例患者的外部队列中进行了验证。使用受试者工作特征曲线下面积(AUC)和平衡精度评估预测性能。此外,将模型性能与更新的替代瘘风险评分(ua-FRS)进行比较,后者是目前临床上术中POPF风险分层的金标准方法。结果:AutoFRS内部验证的AUC为0.81,平衡精度为0.72;外部验证的AUC为0.79,平衡精度为0.70。在有记录的术中POPF危险因素的患者亚组中,AutoFRS (AUC: 0.84±0.05)的表现与uaFRS (AUC: 0.85±0.06)相当。AutoFRS web应用程序基于AutoFRS预测模型,可以根据术前成像和临床数据进行无注释的POPF预测。结论:多模态临床常规数据无需人工标注即可预测POPF,使风险预测过程自动化。我们为术前POPF风险分层的临床可行性提供了额外的证据,并为未来的前瞻性评估介绍了一个软件管道。
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
CiteScore
17.70
自引率
3.30%
发文量
0
审稿时长
6-12 weeks
期刊介绍: The International Journal of Surgery (IJS) has a broad scope, encompassing all surgical specialties. Its primary objective is to facilitate the exchange of crucial ideas and lines of thought between and across these specialties.By doing so, the journal aims to counter the growing trend of increasing sub-specialization, which can result in "tunnel-vision" and the isolation of significant surgical advancements within specific specialties.
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