Brain tumour genetic network signatures of survival.

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.

Brain tumour segmentation with incomplete imaging data.

Progress in neuro-oncology is increasingly recognized to be obstructed by the marked heterogeneity-genetic, pathological, and clinical-of brain tumours. If the treatment susceptibilities and outcomes of individual patients differ widely, determined by the interactions of many multimodal characteristics, then large-scale, fully-inclusive, richly phenotyped data-including imaging-will be needed to predict them at the individual level. Such data can realistically be acquired only in the routine clinical stream, where its quality is inevitably degraded by the constraints of real-world clinical care. Although contemporary machine learning could theoretically provide a solution to this task, especially in the domain of imaging, its ability to cope with realistic, incomplete, low-quality data is yet to be determined. In the largest and most comprehensive study of its kind, applying state-of-the-art brain tumour segmentation models to large scale, multi-site MRI data of 1251 individuals, here we quantify the comparative fidelity of automated segmentation models drawn from MR data replicating the various levels of completeness observed in real life. We demonstrate that models trained on incomplete data can segment lesions very well, often equivalently to those trained on the full completement of images, exhibiting Dice coefficients of 0.907 (single sequence) to 0.945 (complete set) for whole tumours and 0.701 (single sequence) to 0.891 (complete set) for component tissue types. This finding opens the door both to the application of segmentation models to large-scale historical data, for the purpose of building treatment and outcome predictive models, and their application to real-world clinical care. We further ascertain that segmentation models can accurately detect enhancing tumour in the absence of contrast-enhancing imaging, quantifying the burden of enhancing tumour with an R 2 > 0.97, varying negligibly with lesion morphology. Such models can quantify enhancing tumour without the administration of intravenous contrast, inviting a revision of the notion of tumour enhancement if the same information can be extracted without contrast-enhanced imaging. Our analysis includes validation on a heterogeneous, real-world 50 patient sample of brain tumour imaging acquired over the last 15 years at our tertiary centre, demonstrating maintained accuracy even on non-isotropic MRI acquisitions, or even on complex post-operative imaging with tumour recurrence. This work substantially extends the translational opportunity for quantitative analysis to clinical situations where the full complement of sequences is not available and potentially enables the characterization of contrast-enhanced regions where contrast administration is infeasible or undesirable.

Differentiating central nervous system infection from disease infiltration in hematological malignancy.

Hematological malignancies place individuals at risk of CNS involvement from their hematological disease and opportunistic intracranial infection secondary to disease-/treatment-associated immunosuppression. Differentiating CNS infection from hematological disease infiltration in these patients is valuable but often challenging. We sought to determine if statistical models might aid discrimination between these processes. Neuroradiology, clinical and laboratory data for patients with hematological malignancy at our institution between 2007 and 2017 were retrieved. MRI were deep-phenotyped across anatomical distribution, presence of pathological enhancement, diffusion restriction and hemorrhage and statistically modelled with Bayesian-directed probability networks and multivariate logistic regression. 109 patients were studied. Irrespective of a diagnosis of CNS infection or hematological disease, the commonest anatomical distributions of abnormality were multifocal-parenchymal (34.9%), focal-parenchymal (29.4%) and leptomeningeal (11.9%). Pathological enhancement was the most frequently observed abnormality (46.8%), followed by hemorrhage (22.9%) and restricted diffusion (19.3%). Logistic regression could differentiate CNS infection from hematological disease infiltration with an AUC of 0.85 where, with OR > 1 favoring CNS infection and < 1 favoring CNS hematological disease, significantly predictive imaging features were hemorrhage (OR 24.61, p = 0.02), pathological enhancement (OR 0.17, p = 0.04) and an extra-axial location (OR 0.06, p = 0.05). In conclusion, CNS infection and hematological disease are heterogeneous entities with overlapping radiological appearances but a multivariate interaction of MR imaging features may assist in distinguishing them.

Artificial Intelligence-Assisted Gastroenterology- Promises and Pitfalls.

Technological advances in artificial intelligence (AI) represent an enticing opportunity to benefit gastroenterological practice. Moreover, AI, through machine or deep learning, permits the ability to develop predictive models from large datasets. Possibilities of predictive model development in machine learning are numerous dependent on the clinical question. For example, binary classifiers aim to stratify allocation to a categorical outcome, such as the presence or absence of a gastrointestinal disease. In addition, continuous variable fitting techniques can be used to predict quantity of a therapeutic response, thus offering a tool to predict which therapeutic intervention may be most beneficial to the given patient. Namely, this permits an important opportunity for personalization of medicine, including a movement from guideline-specific treatment algorithms to patient-specific ones, providing both clinician and patient the capacity for data-driven decision making. Furthermore, such analyses could predict the development of GI disease prior to the manifestation of symptoms, raising the possibility of prevention or pre-treatment. In addition, computer vision additionally provides an exciting opportunity in endoscopy to automatically detect lesions. In this review, we overview the recent developments in healthcare-based AI and machine learning and describe promises and pitfalls for its application to gastroenterology.

Linaclotide increases cecal pH, accelerates colonic transit, and increases colonic motility in irritable bowel syndrome with constipation.

BACKGROUND: Linaclotide is efficacious in the management of irritable bowel syndrome with constipation (IBS-C), yet relatively little is known regarding its effect on human gastrointestinal physiology. The primary aim of the study was to examine the effect of linaclotide on change in pH across the ileocecal junction (ICJ), a proposed measure of cecal fermentation, and its relationship to symptoms and quality of life (QoL) in IBS-C. METHODS: A total of 13 participants with Rome III IBS-C underwent a standardized wireless motility capsule (WMC). Stool consistency was measured using the Bristol stool form scale (BSFS) and frequency with spontaneous bowel movements (SBM). Gastrointestinal symptoms and QoL were assessed using validated questionnaires. The WMC and questionnaires were repeated after 28 days of linaclotide 290 g po od. KEY RESULTS: Linaclotide reduced the change in pH across the ICJ (-2.4 ± 0.2 vs -2.1 ± 0.4, P = 0.01) as a function of a relative alkalinization of the cecum (5.2 ± 0.2 vs 5.5 ± 0.3, P = 0.02). Linaclotide accelerated colonic transit time (2650 minutes (2171-4038) vs. 1757 (112-3011), P = 0.02), increased colonic log motility index (15 ± 1.8 vs. 16.5 ± 1.8, P = 0.004) but had no effect of gastric emptying or small bowel transit. Change in pH across the ICJ correlated with improvement in symptom intensity, unpleasantness, and visceral sensitivity index (r = 0.62, P = 0.03, r = 0.63, P = 0.02, r = 0.62, P = 0.02) and with increases in BSFS type and SBM (r = 0.9, P < 0.0001, r = 0.6, P = 0.02). CONCLUSIONS & INFERENCES: Linaclotide's effects are confined to the colon where it increases cecal pH, potentially representing a reduction in cecal fermentation and accelerates colonic motility.

Morphology of subcortical brain nuclei is associated with autonomic function in healthy humans.

The autonomic nervous system (ANS) is a brain body interface which serves to maintain homeostasis by influencing a plethora of physiological processes, including metabolism, cardiorespiratory regulation and nociception. Accumulating evidence suggests that ANS function is disturbed in numerous prevalent clinical disorders, including irritable bowel syndrome and fibromyalgia. While the brain is a central hub for regulating autonomic function, the association between resting autonomic activity and subcortical morphology has not been comprehensively studied and thus was our aim. In 27 healthy subjects [14 male and 13 female; mean age 30 years (range 22-53 years)], we quantified resting ANS function using validated indices of cardiac sympathetic index (CSI) and parasympathetic cardiac vagal tone (CVT). High resolution structural magnetic resonance imaging scans were acquired, and differences in subcortical nuclei shape, that is, 'deformation', contingent on resting ANS activity were investigated. CSI positively correlated with outward deformation of the brainstem, right nucleus accumbens, right amygdala and bilateral pallidum (all thresholded to corrected P < 0.05). In contrast, parasympathetic CVT negatively correlated with inward deformation of the right amygdala and pallidum (all thresholded to corrected P < 0.05). Left and right putamen volume positively correlated with CVT (r = 0.62, P = 0.0047 and r = 0.59, P = 0.008, respectively), as did the brainstem (r = 0.46, P = 0.049). These data provide novel evidence that resting autonomic state is associated with differences in the shape and volume of subcortical nuclei. Thus, subcortical morphological brain differences in various disorders may partly be attributable to perturbation in autonomic function. Further work is warranted to investigate these findings in clinical populations. Hum Brain Mapp 39:381-392, 2018. © 2017 Wiley Periodicals, Inc.

Molecular neurobiology of addiction: what's all the (Δ)FosB about?

The transcription factor ΔFosB is upregulated in numerous brain regions following repeated drug exposure. This induction is likely to, at least in part, be responsible for the mechanisms underlying addiction, a disorder in which the regulation of gene expression is thought to be essential. In this review, we describe and discuss the proposed role of ΔFosB as well as the implications of recent findings. The expression of ΔFosB displays variability dependent on the administered substance, showing region-specificity for different drug stimuli. This transcription factor is understood to act via interaction with Jun family proteins and the formation of activator protein-1 (AP-1) complexes. Once AP-1 complexes are formed, a multitude of molecular pathways are initiated, causing genetic, molecular and structural alterations. Many of these molecular changes identified are now directly linked to the physiological and behavioral changes observed following chronic drug exposure. In addition, ΔFosB induction is being considered as a biomarker for the evaluation of potential therapeutic interventions for addiction.