Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.

AI-Based Glioma Grading for a Trustworthy Diagnosis: An Analytical Pipeline for Improved Reliability.

Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The grading of these tumors from image information is a far from trivial task for radiologists and one in which they could be assisted by machine-learning-based decision support.

PREDICT identifies precipitating events associated with the clinical course of acutely decompensated cirrhosis.

Background & Aims: Acute decompensation (AD) of cirrhosis may present without acute-on-chronic liver failure (ACLF) (AD-No ACLF), or with ACLF (AD-ACLF), defined by organ failure(s). Herein, we aimed to analyze and characterize the precipitants leading to both of these AD phenotypes.

Methods: The multicenter, prospective, observational PREDICT study (NCT03056612) included 1,273 non-electively hospitalized patients with AD (No ACLF = 1,071; ACLF = 202).

The PREDICT study uncovers three clinical courses of acutely decompensated cirrhosis that have distinct pathophysiology.

Background & Aims: Acute decompensation (AD) of cirrhosis is defined as the acute development of ascites, gastrointestinal hemorrhage, hepatic encephalopathy, infection or any combination thereof, requiring hospitalization. The presence of organ failure(s) in patients with AD defines acute-on-chronic liver failure (ACLF). The PREDICT study is a European, prospective, observational study, designed to characterize the clinical course of AD and to identify predictors of ACLF.

Rebleeding and mortality risk are increased by ACLF but reduced by pre-emptive TIPS.

Background & Aims: The relationship between acute-on-chronic liver failure (ACLF) and acute variceal bleeding (AVB) is poorly understood. Specifically, the prevalence and prognosis of ACLF in the context of AVB is unclear, while the role of transjugular intrahepatic portosystemic shunt (TIPS) in the management in patients with ACLF has not been described to date.

Methods: A multicenter, international, observational study was conducted in 2,138 patients from 34 centers between 2011 and 2015.

HDL-related biomarkers are robust predictors of survival in patients with chronic liver failure.

Background & Aims: High-density lipoprotein cholesterol (HDL-C) levels are reduced in patients with chronic liver disease and inversely correlate with disease severity. During acute conditions such as sepsis, HDL-C levels decrease rapidly and HDL particles undergo profound changes in their composition and function. We aimed to determine whether indices of HDL quantity and quality associate with progression and survival in patients with advanced liver disease.

Addressing Profiles of Systemic Inflammation Across the Different Clinical Phenotypes of Acutely Decompensated Cirrhosis.

Patients with acutely decompensated cirrhosis (AD) may or may not develop acute-on-chronic liver failure (ACLF). ACLF is characterized by high-grade systemic inflammation, organ failures (OF) and high short-term mortality. Although patients with AD cirrhosis exhibit distinct clinical phenotypes at baseline, they have low short-term mortality, unless ACLF develops during follow-up.