Dataset Information

Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning.

ABSTRACT: Fluorescence-guided surgery is a state-of-the-art approach for intraoperative imaging during neurosurgical removal of tumor tissue. While the visualization of high-grade gliomas is reliable, lower grade glioma often lack visible fluorescence signals. Here, we present a hybrid prototype combining visible light optical coherence microscopy (OCM) and high-resolution fluorescence imaging for assessment of brain tumor samples acquired by 5-aminolevulinic acid (5-ALA) fluorescence-guided surgery. OCM provides high-resolution information of the inherent tissue scattering and absorption properties of tissue. We here explore quantitative attenuation coefficients derived from volumetric OCM intensity data and quantitative high-resolution 5-ALA fluorescence as potential biomarkers for tissue malignancy including otherwise difficult-to-assess low-grade glioma. We validate our findings against the gold standard histology and use attenuation and fluorescence intensity measures to differentiate between tumor core, infiltrative zone and adjacent brain tissue. Using large field-of-view scans acquired by a near-infrared swept-source optical coherence tomography setup, we provide initial assessments of tumor heterogeneity. Finally, we use cross-sectional OCM images to train a convolutional neural network that discriminates tumor from non-tumor tissue with an accuracy of 97%. Collectively, the present hybrid approach offers potential to translate into an in vivo imaging setup for substantially improved intraoperative guidance of brain tumor surgeries.

SUBMITTER: Gesperger J

PROVIDER: S-EPMC7408054 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning.

Gesperger Johanna J Lichtenegger Antonia A Roetzer Thomas T Salas Matthias M Eugui Pablo P Harper Danielle J DJ Merkle Conrad W CW Augustin Marco M Kiesel Barbara B Mercea Petra A PA Widhalm Georg G Baumann Bernhard B Woehrer Adelheid A

Cancers 20200706 7

Fluorescence-guided surgery is a state-of-the-art approach for intraoperative imaging during neurosurgical removal of tumor tissue. While the visualization of high-grade gliomas is reliable, lower grade glioma often lack visible fluorescence signals. Here, we present a hybrid prototype combining visible light optical coherence microscopy (OCM) and high-resolution fluorescence imaging for assessment of brain tumor samples acquired by 5-aminolevulinic acid (5-ALA) fluorescence-guided surgery. OCM ...[more]

PMID: 32640583

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Project description:Abstract Aims Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation. Methods and results We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM centre of excellence. Using more than 18 000 combined instances of electrocardiograms and echocardiograms from 2728 patients, we developed LVH-fusion. On held-out test data, LVH-fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T-wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM. Conclusion These results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death. Graphical Abstract Graphical Abstract We present LVH-fusion, a multi-modal deep learning method to jointly model electrical (ECG) and ultrasound-based (ECHO) time series data of the heart. We demonstrate its potential with application to the diagnosis of left ventricular hypertrophy. Furthermore, we explored explainability techniques to investigate local and global features that positively/negatively impact predictions to provide actionable insight to model estimates.

Dataset Information

Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning.

Publications

Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning.

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