Prevention and Clinical Staging

Bipolar disorder and major depression are associated with significant disability, morbidity, and reduced life expectancy. People with mood disorders have shown higher ratios of unhealthy lifestyle choices, including poor diet quality and suboptimal nutrition. Diet and nutrition impact on brain /mental health, but cognitive outcomes have been less researched in psychiatric disorders. Neurocognitive dysfunction is a major driver of social dysfunction and a therapeutic target in mood disorders, although effective cognitive-enhancers are currently lacking. This narrative review aimed to assess the potential cognitive benefits of dietary and nutritional interventions in subjects diagnosed with mood disorders. Eight clinical trials with nutrients were identified, whereas none involved dietary interventions. Efficacy to improve select cognitive deficits has been reported, but results are either preliminary or inconsistent. Methodological recommendations for future cognition trials in the field are advanced. Current evidence and future views are discussed from the perspectives of precision medicine, clinical staging, nutritional psychiatry, and the brain-gut-microbiota axis.

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Clinical staging of accidental hypothermia: the Revised Swiss System

Resuscitation ◽

10.1016/j.resuscitation.2021.02.038 ◽

2021 ◽

Author(s):

Martin E. Musi ◽

Alison Sheets ◽

Ken Zafren ◽

Hermann Brugger ◽

Peter Paal ◽

...

Keyword(s):

Clinical Staging ◽

Accidental Hypothermia

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Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Cancers ◽

10.3390/cancers13081929 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1929

Author(s):

Jan C. Peeken ◽

Jan Neumann ◽

Rebecca Asadpour ◽

Yannik Leonhardt ◽

Joao R. Moreira ◽

...

Keyword(s):

Soft Tissue ◽

Soft Tissue Sarcomas ◽

Treatment Decision ◽

Staging System ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Clinical Staging ◽

Imaging Features ◽

Prognostic Assessment ◽

Selection Operator

Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

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