Computer Engineering Department, Bilkent University, 06800 Ankara, Turkey.
School of Computer Science, McGill University, Montreal, QC, H3A 0E9, Canada.
Bioinformatics. 2023 Nov 1;39(11). doi: 10.1093/bioinformatics/btad684.
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 by 3.38% and the Area Under the Precision-Recall Curve 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 are released at https://zenodo.org/record/7228791.
术中对肿瘤特征进行在线评估非常重要，并有潜力建立术中外科医生反馈机制。有了这种反馈，外科医生可以决定在肿瘤切除方面更加自由或保守。虽然有基于代谢组学的肿瘤病理预测方法，但由于数据集规模较小，其模型复杂性预测性能受到限制。此外，反馈提供的关于肿瘤组织的信息在内容和准确性方面都可以得到改善。
在这项研究中，我们提出了一种基于代谢途径的深度学习模型（PiDeeL），该模型基于代谢物浓度进行生存分析和病理评估。我们表明，将途径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 可以提高肿瘤病理预测性能，在 ROC 曲线下面积（AUC）方面提高了 3.38%，在精度-召回曲线下面积（AUC）方面提高了 4.06%。同样，就时间依赖性一致性指数（c-index）而言，与最先进的方法相比，PiDeeL 具有更好的生存分析性能（提高了 4.3%）。此外，我们表明，对 PiDeeL 的输入代谢物特征和途径特定神经元进行重要性分析可深入了解肿瘤代谢。我们预计，该模型在手术室中的使用将帮助外科医生实时调整手术计划，并根据手术程序提供更好的预后估计。
代码发布在 https://github.com/ciceklab/PiDeeL。本研究中使用的数据发布在 https://zenodo.org/record/7228791。
