Machine Learning Assisted Intraoperative Assessment of Brain Tumor Margins Using HRMAS NMR Spectroscopy

AbstractComplete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 568), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a mean AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a mean AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguish tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.

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Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008184 ◽

2020 ◽

Vol 16 (11) ◽

pp. e1008184

Author(s):

Doruk Cakmakci ◽

Emin Onur Karakaslar ◽

Elisa Ruhland ◽

Marie-Pierre Chenard ◽

Francois Proust ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Magic Angle Spinning ◽

Magic Angle ◽

Full Spectrum ◽

Real Time Analysis ◽

Targeted Analysis ◽

Hrmas Nmr ◽

Angle Spinning ◽

And Control

Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 565), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a median AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a median AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguishing tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.

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Machine learning for a rapid discrimination of ginseng cultivation age using 1H-NMR spectra

Applied Biological Chemistry ◽

10.1186/s13765-020-00548-4 ◽

2020 ◽

Vol 63 (1) ◽

Author(s):

Wonho Lee ◽

Dahye Yoon ◽

Seohee Ma ◽

Dae Young Lee ◽

Jae Won Lee ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Nmr Spectroscopy ◽

Magic Angle Spinning ◽

Magic Angle ◽

Systematic Classification ◽

New Class ◽

Angle Spinning ◽

Rapid Discrimination ◽

Ginseng Cultivation

Abstract The scientific and systematic classification of cultivation age is important for preventing age falsification and ensuring the quality of ginseng. Therefore, we applied deep learning to classify the cultivation age of ginseng. Deep learning, which is based on an artificial neural network, is one of the new class of models for machine learning, and is state-of-the-art. It is a powerful tool and has been used to solve complex problems in many fields. In the present study, powdered samples of 4-, 5-, and 6-year-old ginseng were measured using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy. NMR data were analyzed with deep learning and partial least-squares discriminant analysis (PLS-DA) to improve accuracy. The accuracy of the PLS-DA was 87.1% and the accuracy of the deep learning model was 93.9%. NMR spectroscopy with deep learning can be a useful tool for discrimination of ginseng cultivation age.

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