计算机科学
接收机工作特性
生物标志物
胶质瘤
脑瘤
边距(机器学习)
人工智能
磁共振成像
机器学习
化学
医学
放射科
癌症研究
病理
生物化学
作者
Doruk Cakmakci,Gun Kaynar,Caroline Bund,Martial Piotto,Francois Proust,Izzie Jacques Namer,A Ercument Cicek
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2022-05-05
卷期号:38 (12): 3238-3244
被引量:1
标识
DOI:10.1093/bioinformatics/btac309
摘要
Identification and removal of micro-scale residual tumor tissue during brain tumor surgery are key for survival in glioma patients. For this goal, High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy-based assessment of tumor margins during surgery has been an effective method. However, the time required for metabolite quantification and the need for human experts such as a pathologist to be present during surgery are major bottlenecks of this technique. While machine learning techniques that analyze the NMR spectrum in an untargeted manner (i.e. using the full raw signal) have been shown to effectively automate this feedback mechanism, high dimensional and noisy structure of the NMR signal limits the attained performance.In this study, we show that identifying informative regions in the HRMAS NMR spectrum and using them for tumor margin assessment improves the prediction power. We use the spectra normalized with the ERETIC (electronic reference to access in vivo concentrations) method which uses an external reference signal to calibrate the HRMAS NMR spectrum. We train models to predict quantities of metabolites from annotated regions of this spectrum. Using these predictions for tumor margin assessment provides performance improvements up to 4.6% the Area Under the ROC Curve (AUC-ROC) and 2.8% the Area Under the Precision-Recall Curve (AUC-PR). We validate the importance of various tumor biomarkers and identify a novel region between 7.97 ppm and 8.09 ppm as a new candidate for a glioma biomarker.The code is released at https://github.com/ciceklab/targeted_brain_tumor_margin_assessment. The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.5781769.Supplementary data are available at Bioinformatics online.
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