OS08.4.A Retrospective analysis of in vivo 1H-magnetic resonance spectroscopy based on a machine learning approach enables reliable prediction of IDH mutation in patients with glioma

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii11-ii11
Author(s):  
E Bumes ◽  
C Fellner ◽  
S Lenz ◽  
R Linker ◽  
S Weis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Validation Cohort ◽  
Resonance Spectroscopy ◽  
Learning Approach ◽  
Idh Mutation ◽  
1H Mrs ◽  
1H Magnetic Resonance Spectroscopy ◽  
Mutation Status ◽  
Machine Learning Approach

Abstract BACKGROUND Mutation of isocitrate dehydrogenase (IDH) is not only an important landmark in the development of low-grade gliomas, but also has prognostic significance and is a potential therapeutic target. There is a high need to determinate IDH mutation status at diagnosis and during the course of therapy in a non-invasive and reliable manner. We established a machine learning approach based on a support vector machine to detect IDH mutation status in in vivo standard 1H-magnetic resonance spectroscopy (1H-MRS) at 3T with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%), and a specificity of 75% (95% CI, 42.85–94.5%) in a prospective monocentric clinical trial. Here, the same method is applied in a retrospective cohort at 1.5T and tested for transferability. MATERIAL AND METHODS Validation cohort. The validation cohort comprised 100 patients with glioma for which standard in vivo 1H-MRS spectra had been acquired between 2002 and 2007. Standard single voxel spectroscopy had been measured at 1.5T using a PRESS sequence with a TR of 1500ms and a TE of 30ms. One sample had to be excluded due to non-malignant histology and for 15 samples the IDH mutation status was not available. Therefore, the validation cohort comprised 84 samples, of which 35 were bearing an IDH mutation in immunohistochemistry (sequencing for confirmation is outstanding). Machine learning. To transfer our method to an independent validation cohort our previously established machine learning approach was first trained on all samples of the 3T group. The trained algorithm was then applied to the data of the validation cohort. Here, among other factors the different field strengths, with which the spectra were acquired (3T vs. 1.5T) had to be considered. RESULTS 27 samples of the validation cohort had to be excluded due to poor spectra quality. Our approach correctly detected IDH mutation status in 47 of 62 patients (75.8%), although the technical conditions were significantly different from our published prospective cohort. 17 of 30 patients bearing an IDH mutation were correctly identified, while 30 of 32 wild type patients were determined successfully. CONCLUSION Our approach to detect IDH mutation status has promising application in an unselected retrospective cohort, demonstrating transferability across different technical conditions. Further investigations to improve our technique and an advanced neuropathological processing of the samples are planned.

scholarly journals Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3406
Author(s):  
Elisabeth Bumes ◽  
Fro-Philip Wirtz ◽  
Claudia Fellner ◽  
Jirka Grosse ◽  
Dirk Hellwig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Support Vector ◽  
Resonance Spectroscopy ◽  
Idh Mutation ◽  
1H Mrs ◽  
1H Magnetic Resonance Spectroscopy ◽  
Non Invasive

Isocitrate dehydrogenase (IDH)-1 mutation is an important prognostic factor and a potential therapeutic target in glioma. Immunohistological and molecular diagnosis of IDH mutation status is invasive. To avoid tumor biopsy, dedicated spectroscopic techniques have been proposed to detect D-2-hydroxyglutarate (2-HG), the main metabolite of IDH, directly in vivo. However, these methods are technically challenging and not broadly available. Therefore, we explored the use of machine learning for the non-invasive, inexpensive and fast diagnosis of IDH status in standard 1H-magnetic resonance spectroscopy (1H-MRS). To this end, 30 of 34 consecutive patients with known or suspected glioma WHO grade II-IV were subjected to metabolic positron emission tomography (PET) imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) for optimized voxel placement in 1H-MRS. Routine 1H-magnetic resonance (1H-MR) spectra of tumor and contralateral healthy brain regions were acquired on a 3 Tesla magnetic resonance (3T-MR) scanner, prior to surgical tumor resection and molecular analysis of IDH status. Since 2-HG spectral signals were too overlapped for reliable discrimination of IDH mutated (IDHmut) and IDH wild-type (IDHwt) glioma, we used a nested cross-validation approach, whereby we trained a linear support vector machine (SVM) on the complete spectral information of the 1H-MRS data to predict IDH status. Using this approach, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%) and a specificity of 75.0% (95% CI, 42.9–94.5%), respectively. The area under the curve (AUC) amounted to 0.83. Subsequent ex vivo 1H-nuclear magnetic resonance (1H-NMR) measurements performed on metabolite extracts of resected tumor material (eight specimens) revealed myo-inositol (M-ins) and glycine (Gly) to be the major discriminators of IDH status. We conclude that our approach allows a reliable, non-invasive, fast and cost-effective prediction of IDH status in a standard clinical setting.

scholarly journals Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

2019 ◽  
Author(s):  
Anton Levitan ◽  
Andrew N. Gale ◽  
Emma K. Dallon ◽  
Darby W. Kozan ◽  
Kyle W. Cunningham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Yeast Species ◽  
Transposon Mutagenesis ◽  
Learning Approach ◽  
Long Distance ◽  
Gene Essentiality ◽  
Genome Wide ◽  
Machine Learning Approach

ABSTRACTIn vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

scholarly journals Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252096
Author(s):  
Maria B. Rabaglino ◽  
Alan O’Doherty ◽  
Jan Bojsen-Møller Secher ◽  
Patrick Lonergan ◽  
Poul Hyttel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Focal Adhesion ◽  
Learning Approach ◽  
Omics Data ◽  
Machine Learning Approach ◽  
Cluster 2 ◽  
Omics Data Integration

Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact with the maternal endometrium. The main objective was to apply a multi-omics data integration approach to identify both temporally differentially expressed and differentially methylated genes (DEG and DMG), between IVP and MOET embryos, that could impact TE function. To start, four and five published transcriptomic and epigenomic datasets, respectively, were processed for data integration. Second, DEG from day 7 to days 13 and 16 and DMG from day 7 to day 17 were determined in the TE from IVP vs. MOET embryos. Third, genes that were both DE and DM were subjected to hierarchical clustering and functional enrichment analysis. Finally, findings were validated through a machine learning approach with two additional datasets from day 15 embryos. There were 1535 DEG and 6360 DMG, with 490 overlapped genes, whose expression profiles at days 13 and 16 resulted in three main clusters. Cluster 1 (188) and Cluster 2 (191) genes were down-regulated at day 13 or day 16, respectively, while Cluster 3 genes (111) were up-regulated at both days, in IVP embryos compared to MOET embryos. The top enriched terms were the KEGG pathway "focal adhesion" in Cluster 1 (FDR = 0.003), and the cellular component: "extracellular exosome" in Cluster 2 (FDR<0.0001), also enriched in Cluster 1 (FDR = 0.04). According to the machine learning approach, genes in Cluster 1 showed a similar expression pattern between IVP and less developed (short) MOET conceptuses; and between MOET and DKK1-treated (advanced) IVP conceptuses. In conclusion, these results suggest that early conceptuses derived from IVP embryos exhibit epigenomic and transcriptomic changes that later affect its elongation and focal adhesion, impairing post-transfer survival.

scholarly journals Low Amplitude Burst Detection of Catecholamines

2021 ◽  
Author(s):  
Amnah Eltahir ◽  
Jason White ◽  
Terry Lohrenz ◽  
P. Read Montague
Keyword(s):  
Machine Learning ◽  
Electrochemical Detection ◽  
Perception And Action ◽  
Learning Approach ◽  
Voltage Waveform ◽  
Machine Learning Approach ◽  
Dependent Signal ◽  
Low Amplitude ◽  
New Machine

Abstract Machine learning advances in electrochemical detection have recently produced subsecond and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to subsecond, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

scholarly journals Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration—A machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254690
Author(s):  
Saurav Z. K. Sajib ◽  
Munish Chauhan ◽  
Oh In Kwon ◽  
Rosalind J. Sadleir
Keyword(s):  
Machine Learning ◽  
Electrical Conductivity ◽  
Magnetic Resonance ◽  
Magnetic Flux ◽  
Flux Density ◽  
Diffusion Tensor ◽  
Magnetic Flux Density ◽  
Learning Approach ◽  
Machine Learning Approach

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations.

scholarly journals Low amplitude burst detection of catecholamines

2021 ◽  
Author(s):  
Amnah M Eltahir ◽  
Jason White ◽  
Terry M Lohrenz ◽  
Read Montague
Keyword(s):  
Machine Learning ◽  
Electrochemical Detection ◽  
Perception And Action ◽  
Learning Approach ◽  
Voltage Waveform ◽  
Machine Learning Approach ◽  
Dependent Signal ◽  
Low Amplitude ◽  
New Machine

Machine learning advances in electrochemical detection have recently produced sub- second and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to sub- second, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

Abstract WMP20: Validation of an Efficient Machine-learning Approach to Quantify CSF Volume Changes Using Multicenter CT Scans

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Yasheng Chen ◽  
Raj Dhar ◽  
Tobias Kulik ◽  
Kristy Yuan ◽  
Laura Heitsch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Random Forest ◽  
Cerebral Edema ◽  
Validation Cohort ◽  
Ct Scans ◽  
Learning Approach ◽  
Volume Changes ◽  
Head Ct ◽  
Machine Learning Approach

Introduction: CSF volumetric change within the first 24hrs after ischemic stroke (ΔCSF) may be an early imaging biomarker of cerebral edema. The ability to accurately and rapidly quantify ΔCSF in large multicenter stroke populations will facilitate large-scale studies to understand complex dynamics and genetic influences of cerebral edema. Hypothesis: An automated machine-learning approach to CSF segmentation utilizing random forest will be superior to standard Hounsfield unit thresholding for measuring ΔCSF using CT scans from different medical centers. Methods: Manual CSF delineation on both the baseline (within 6hrs) and 24hr follow-up head CT scans from 26 ischemic stroke patients acquired at center A were used as the training samples for random forest classifiers to segment CSF. The trained classifiers were then employed to segment baseline and 24-hr scans from 12 new patients from center B. Correlations of random forest detected CSF volumes to manual segmentation (including correlation coefficient and pvalue) were then compared to those from a thresholding approach, applying the optimal CT threshold derived from the training scans to the validation cohort from center B. Results: Random forest segmentation was very efficient (run time: 23 min/scan) and was able to accurately quantify CSF volumes and volumetric changes in the validation cohort. As shown in the table, random forest outperformed thresholding in the correlations between automated quantification and manual delineation. Conclusion: We have developed a robust, efficient and accurate automated approach to quantify both absolute and relative CSF volume changes from multicenter head CT images. This approach is ready to be employed in multicenter large data analysis of the genetic influence on edema formation following acute ischemic stroke.

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