手术期间肿瘤特征的在线评估很重要，并且有可能建立术中外科医生反馈机制。有了这样的反馈，外科医生就可以决定在肿瘤切除方面采取更加自由或保守的态度。虽然有一些方法可以进行基于代谢组学的肿瘤病理学预测，但它们的模型复杂性预测性能受到小数据集大小的限制。此外，肿瘤组织反馈所传达的信息在内容和准确性方面都可以得到改善。在本研究中，我们提出了一种基于代谢途径的深度学习模型（PiDeeL），用于根据代谢物浓度进行生存分析和病理评估。我们表明，将通路信息纳入模型架构可大大降低参数复杂性，并实现更好的生存分析和病理分类性能。通过这些设计决策，我们表明 PiDeeL 将 ROC 曲线下面积 (AUC-ROC) 和精确召回曲线下面积 (AUC-ROC) 方面的肿瘤病理学预测性能提高了 3.38% AUC-PR) 增加 4.06%。同样，在时间依赖性一致性指数（c-index）方面，与最先进的技术相比，PiDeeL 实现了更好的生存分析性能（提高了 4.3%）。此外，我们表明对输入代谢物特征以及 PiDeeL 通路特异性神经元进行的重要性分析可以提供对肿瘤代谢的见解。我们预计，在手术室中使用该模型将有助于外科医生即时调整手术计划，并根据手术程序做出更好的预后估计。代码发布于 https://github.com/ciceklab/PiDeeL 。本研究中使用的数据发布于 https://zenodo.org/record/7228791。补充数据可在生物信息学在线获取。© 作者 2023。由牛津大学出版社出版。

Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or conservative regarding the resection of the tumor. While there are methods to perform metabolomics-based tumor pathology prediction, their model complexity predictive performance is limited by the small dataset sizes. Furthermore, the information conveyed by the feedback provided on the tumor tissue could be improved both in terms of content and accuracy. In this study, we propose a metabolic pathway-informed deep learning model (PiDeeL) to perform survival analysis and pathology assessment based on metabolite concentrations. We show that incorporating pathway information into the model architecture substantially reduces parameter complexity and achieves better survival analysis and pathological classification performance. With these design decisions, we show that PiDeeL improves tumor pathology prediction performance of the state-of-the-art in terms of the Area Under the ROC Curve (AUC-ROC) by 3.38% and the Area Under the Precision-Recall Curve (AUC-PR) by 4.06%. Similarly, with respect to the time-dependent concordance index (c-index), PiDeeL achieves better survival analysis performance (improvement of 4.3%) when compared to the state-of-the-art. Moreover, we show that importance analyses performed on input metabolite features as well as pathway-specific neurons of PiDeeL provide insights into tumor metabolism. We foresee that the use of this model in the surgery room will help surgeons adjust the surgery plan on the fly and will result in better prognosis estimates tailored to surgical procedures.The code is released at https://github.com/ciceklab/PiDeeL. The data used in this study is released at https://zenodo.org/record/7228791.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.