Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).

Purpose: Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements.

Materials And Methods: Our end-to-end framework (1) classifies MRI sequences using an ensemble classifier, (2) preprocesses the data in a reproducible manner, (3) delineates tumor tissue subtypes using convolutional neural networks, and (4) extracts diverse radiomic features.

Deep learning-based end-to-end scan-type classification, pre-processing, and segmentation of clinical neuro-oncology studies.

Modern neuro-oncology workflows are driven by large collections of high-dimensional MRI data obtained using varying acquisition protocols. The concomitant heterogeneity of this data makes extensive manual curation and pre-processing imperative prior to algorithmic use. The limited efforts invested towards automating this curation and processing are fragmented, do not encompass the entire workflow, or still require significant manual intervention.

Spatiotemporal analysis of the appearance of gamma-band Microstates in resting state MEG.

Spatiotemporal analysis of EEG signal has revealed a rich set of methods to quantify neuronal activity using spatially global topographic templates, called Microstates. These methods complement more traditional spectral analysis, which uses band limited source data to determine defining differences in band power and peak characteristics. The high sampling rate and increased resistance to high frequency noise of MEG data offers an opportunity to explore the utility of spatiotemporal analysis over a wider spectrum than in EEG.

Predicting a multi-parametric probability map of active tumor extent using random forests.

Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error.

Determination of neural state classification metrics from the power spectrum of human ECoG.

Brain electrical activity exhibits scale-free dynamics that follow power law scaling. Previous works have shown that broadband spectral power exhibits state-dependent scaling with a log frequency exponent that systematically varies with neural state. However, the frequency ranges which best characterize biological state are not consistent across brain location or subject.

Characterization of scale-free properties of human electrocorticography in awake and slow wave sleep States.

Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity.

A comparison of the retention of three endodontic dowel systems following different surface treatments.

Purpose: Practitioners have several options during the selection of a dowel for core restoration, including metal and glass fiber materials. Retention of the cemented dowel is critical for the success of this type of restoration. The purpose of this in vitro study was to evaluate the effect of two surface treatments on the retention of three types of dowels placed into prepared canals with a resin cement.