Pai, R. K. | Search bioRxiv

bioRxiv2024

Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune check-point inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction, which was by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEERs tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

bioinformatics↗

bioRxiv2021

Multi-scale brain simulation with integrated positron emission tomography yields hidden local field potential activity that augments machine-learning classification of Alzheimer's disease

INTRODUCTIONComputational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimers disease. METHODSWe enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid {beta} PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimers Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification. RESULTSThe combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the Alzheimers-typical spatial distribution. DISCUSSIONThe cause-and-effect implementation of local hyperexcitation caused by Amyloid {beta} can improve the machine-learning-driven classification of Alzheimers and demonstrates TVBs ability to decode information in empirical data employing connectivity-based brain simulation. RESEARCH IN CONTEXTO_LISYSTEMATIC REVIEW. Machine-learning has been proven to augment diagnostics of dementia in several ways. Imaging-based approaches enable early diagnostic predictions. However, individual projections of long-term outcome as well as differential diagnosis remain difficult, as the mechanisms behind the used classifying features often remain unclear. Mechanistic whole-brain models in synergy with powerful machine learning aim to close this gap. C_LIO_LIINTERPRETATION. Our work demonstrates that multi-scale brain simulations considering Amyloid {beta} distributions and cause-and-effect regulatory cascades reveal hidden electrophysiological processes that are not readily accessible through measurements in humans. We demonstrate that these simulation-inferred features hold the potential to improve diagnostic classification of Alzheimers disease. C_LIO_LIFUTURE DIRECTIONS. The simulation-based classification model needs to be tested for clinical usability in a larger cohort with an independent test set, either with another imaging database or a prospective study to assess its capability for long-term disease trajectories. C_LI

neuroscience↗