Semi-supervised analysis of human brain tumours from partially labeled MRS information, using manifold learning models

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

Thought Chart: tracking the thought with manifold learning during emotion regulation

The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks. EEG-based temporal dynamic functional connectomes are created based on 20 psychiatrically healthy participants' EEG recordings during resting state and an emotion regulation task. Graph dissimilarity space embedding was applied to all the dynamic EEG connectomes. In order to visualize the learned manifold in a lower dimensional space, local neighborhood information is reconstructed via k-nearest neighbor-based nonlinear dimensionality reduction (NDR) and epsilon distance-based NDR. We showed that two neighborhood constructing approaches of NDR embed the manifold in a two-dimensional space, which we named Thought Chart. In Thought Chart, different task conditions represent distinct trajectories. Properties such as the distribution or average length in the 2-D space may serve as useful parameters to explore the underlying cognitive load and emotion processing during the complex task. In sum, this framework is a novel data-driven approach to the learning and visualization of underlying neurophysiological dynamics of complex functional brain data.

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

Accurate grading of brain gliomas by soft independent modeling of class analogy based on non-negative matrix factorization of proton magnetic resonance spectra

Hydrogen magnetic resonance spectroscopy ((1) H-MRS) is a non-invasive technique which provides a 'frequency-signal intensity' spectrum of biochemical compounds of tissues in the body. Although this method is currently used in human brain studies, accurate classification of in-vivo (1) H-MRS is a challenging task in the diagnosis of brain tumors. Problems such as overlapping metabolite peaks, incomplete information on background component and low signal-to-noise ratio disturb classification results of this spectroscopic method. This study presents an alternative approach to the soft independent modeling of class analogy (SIMCA) technique, using non-negative matrix factorization (NMF) for dimensionality reduction. In the adopted strategy, the performance of SIMCA was improved by application of a robust algorithm for classification in the presence of noisy measurements. Total of 219 spectra from two databases were taken by water-suppressed short echo-time (1) H-MRS, acquired from different subjects with different stages of glial brain tumors (Grade II (26 cases), grade III (24 cases), grade IV (41 cases), as well as 25 healthy cases). The SIMCA was performed using two approaches: (i) principal component analysis (PCA) and (ii) non-negative matrix factorization (NMF), as a modified approach. Square prediction error was considered to assess the class membership of the external validation set. Finally, several figures of merit such as the correct classification rate (CCR), sensitivity and specificity were calculated. Results of SIMCA based on NMF showed significant improvement in percentage of correctly classified samples, 91.4% versus 83.5% for PCA-based model in an independent test set.

Early-Stage White Matter Lesions Detected by Multispectral MRI Segmentation Predict Progressive Cognitive Decline

White matter lesions (WML) are the main brain imaging surrogate of cerebral small-vessel disease. A new MRI tissue segmentation method, based on a discriminative clustering approach without explicit model-based added prior, detects partial WML volumes, likely representing very early-stage changes in normal-appearing brain tissue. This study investigated how the different stages of WML, from a “pre-visible” stage to fully developed lesions, predict future cognitive decline. MRI scans of 78 subjects, aged 65–84 years, from the Leukoaraiosis and Disability (LADIS) study were analyzed using a self-supervised multispectral segmentation algorithm to identify tissue types and partial WML volumes. Each lesion voxel was classified as having a small (33%), intermediate (66%), or high (100%) proportion of lesion tissue. The subjects were evaluated with detailed clinical and neuropsychological assessments at baseline and at three annual follow-up visits. We found that voxels with small partial WML predicted lower executive function compound scores at baseline, and steeper decline of executive scores in follow-up, independently of the demographics and the conventionally estimated hyperintensity volume on fluid-attenuated inversion recovery images. The intermediate and fully developed lesions were related to impairments in multiple cognitive domains including executive functions, processing speed, memory, and global cognitive function. In conclusion, early-stage partial WML, still too faint to be clearly detectable on conventional MRI, already predict executive dysfunction and progressive cognitive decline regardless of the conventionally evaluated WML load. These findings advance early recognition of small vessel disease and incipient vascular cognitive impairment.

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors: semi-supervised classification of class C GPCRs

G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The tertiary structure of the transmembrane domain, a gate to the study of protein functionality, is unknown for almost all members of class C GPCRs, which are the target of the current study. As a result, their investigation must often rely on alignments of their amino acid sequences. Sequence alignment entails the risk of missing relevant information. Various approaches have attempted to circumvent this risk through alignment-free transformations of the sequences on the basis of different amino acid physicochemical properties. In this paper, we use several of these alignment-free methods, as well as a basic amino acid composition representation, to transform the available sequences. Novel semi-supervised statistical machine learning methods are then used to discriminate the different class C GPCRs types from the transformed data. This approach is relevant due to the existence of orphan proteins to which type labels should be assigned in a process of deorphanization or reverse pharmacology. The reported experiments show that the proposed techniques provide accurate classification even in settings of extreme class-label scarcity and that fair accuracy can be achieved even with very simple transformation strategies that ignore the sequence ordering.

DETECTION OF DRIVING FATIGUE BY USING NONCONTACT EMG AND ECG SIGNALS MEASUREMENT SYSTEM

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov–Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

SELF-SUPERVISED MRI TISSUE SEGMENTATION BY DISCRIMINATIVE CLUSTERING

The study of brain lesions can benefit from a clear identification of transitions between healthy and pathological tissues, through the analysis of brain imaging data. Current signal processing methods, able to address these issues, often rely on strong prior information. In this article, a new method for tissue segmentation is proposed. It is based on a discriminative strategy, in a self-supervised machine learning approach. This method avoids the use of prior information, which makes it very versatile, and able to cope with different tissue types. It also returns tissue probabilities for each voxel, crucial for a good characterization of the evolution of brain lesions. Simulated as well as real benchmark data were used to validate the accuracy of the method and compare it against other segmentation algorithms.

Adaptive filtering and random variables coefficient for analyzing functional magnetic resonance imaging data

Functional magnetic resonance imaging (fMRI) is used to study brain functional connectivity (FC) after filtering the physiological noise (PN). Herein, we employ: adaptive filtering for removing nonstationary PN; random variables (RV) coefficient for FC analysis. Comparisons with standard techniques were performed by quantifying PN filtering and FC in neural vs. non-neural regions. As a result, adaptive filtering plus RV coefficient showed a greater suppression of PN and higher connectivity in neural regions, representing a novel effective approach to analyze fMRI data.

Strategies for annotation and curation of translational databases: the eTUMOUR project

The eTUMOUR (eT) multi-centre project gathered in vivo and ex vivo magnetic resonance (MR) data, as well as transcriptomic and clinical information from brain tumour patients, with the purpose of improving the diagnostic and prognostic evaluation of future patients. In order to carry this out, among other work, a database—the eTDB—was developed. In addition to complex permission rules and software and management quality control (QC), it was necessary to develop anonymization, processing and data visualization tools for the data uploaded. It was also necessary to develop sophisticated curation strategies that involved on one hand, dedicated fields for QC-generated meta-data and specialized queries and global permissions for senior curators and on the other, to establish a set of metrics to quantify its contents. The indispensable dataset (ID), completeness and pairedness indices were set. The database contains 1317 cases created as a result of the eT project and 304 from a previous project, INTERPRET. The number of cases fulfilling the ID was 656. Completeness and pairedness were heterogeneous, depending on the data type involved.

ONLINE SEMI-SUPERVISED GROWING NEURAL GAS

In this paper we introduce online semi-supervised growing neural gas (OSSGNG), a novel online semi-supervised classification approach based on growing neural gas (GNG). Existing semi-supervised classification approaches based on GNG require that the training data is explicitly stored as the labeling is performed a posteriori after the training phase. As main contribution, we present an approach that relies on online labeling and prediction functions to process labeled and unlabeled data uniformly and in an online fashion, without the need to store any of the training examples explicitly. We show that using on-the-fly labeling strategies does not significantly deteriorate the performance of classifiers based on GNG, while circumventing the need to explicitly store training examples. Armed with this result, we then present a semi-supervised extension of GNG (OSSGNG) that relies on the above mentioned online labeling functions to label unlabeled examples and incorporate them into the model on-the-fly. As an important result, we show that OSSGNG performs as good as previous semi-supervised extensions of GNG which rely on offline labeling strategies. We also show that OSSGNG compares favorably to other state-of-the-art semi-supervised learning approaches on standard benchmarking datasets.

AI TOOLS IN DECISION MAKING SUPPORT SYSTEMS: A REVIEW

AI tools have advanced sufficiently such that they are integrated into decision making support systems for real applications and are impacting decision making in substantive ways. This paper reviews decision making theories and AI tools and the intelligent decision systems that result from the integration of these concepts.

THE S2-ENSEMBLE FUSION ALGORITHM

This paper presents a novel model for performing classification and visualization of high-dimensional data by means of combining two enhancing techniques. The first is a semi-supervised learning, an extension of the supervised learning used to incorporate unlabeled information to the learning process. The second is an ensemble learning to replicate the analysis performed, followed by a fusion mechanism that yields as a combined result of previously performed analysis in order to improve the result of a single model. The proposed learning schema, termed S 2-Ensemble, is applied to several unsupervised learning algorithms within the family of topology maps, such as the Self-Organizing Maps and the Neural Gas. This study also includes a thorough research of the characteristics of these novel schemes, by means quality measures, which allow a complete analysis of the resultant classifiers from the viewpoint of various perspectives over the different ways that these classifiers are used. The study conducts empirical evaluations and comparisons on various real-world datasets from the UCI repository, which exhibit different characteristics, so to enable an extensive selection of situations where the presented new algorithms can be applied.