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Objective: To identify clinical and biological criteria predictive of significant traumatic injury in only kinetic-based polytrauma patients without clinical severity criteria. To propose a decisional algorithm to assist the emergency doctor in deciding whether or not to perform a WBCT in the above population., Methods: Retrospective bi-center study. 1270 patients with high velocity trauma without clinical severity criteria, for whom a WBCT was performed in 2017, were included. Patients with hemodynamic, respiratory or neurological severity criterion or those requiring pre-hospital resuscitation measures were excluded. Our primary endpoint was the identification of a significant lesion, i.e. any lesion that led to hospitalization > 24 h for monitoring or medico-surgical treatment. Data collected were age, sex, mechanism of injury, Glasgow Coma Scale score, number of symptomatic body regions, blood alcohol level, and neutrophil count., Results: Multivariate analysis found independent predictors of significant injury: fall > 5 m (OR: 14.36; CI: 2.3-283.4; p = 0.017), Glasgow score = 13 or 14 (OR: 4.40; CI:1.30-18.52; p = 0.027), presence of 2 symptomatic body regions (OR: 10.21; CI: 4.66-23.72; p = 0.05), positive blood alcohol level (OR: 2.81; CI: 1.13-7.33; p = 0.029) and neutrophilic leukocytosis (OR: 8.76; CI: 3.94-21.27; p = 0.01). A composite clinico-biological endpoint predictive of the absence of significant lesion was identified using a Classification and Regression Tree: number of symptomatic regions < 2, absence of Neutrophilic leukocytosis and negative blood alcohol concentration., Conclusion: A simple triage algorithm was created with the objective of identifying, in high velocity trauma without clinical severity criteria, those without significant traumatic injury., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: Therapeutic management of parotid gland tumours depends on their histological type. To aid its characterisation, we sought to develop automated decision-tree models based on multiparametric magnetic resonance imaging (MRI) parameters and to evaluate their added diagnostic value compared with morphological sequences., Methods: 206 MRIs from 206 patients with histologically proven parotid gland tumours were included from January 2009 to January 2018. Multiparametric MRI findings (including parameters derived from diffusion-weighted imaging [DWI] and dynamic contrast-enhanced [DCE]) were used to build predictive classification and regression tree (CART) models for each histological type. All MRIs were read twice: first, based on morphological sequence findings only, and second, with the addition of multiparametric sequences and CART findings. The diagnostic performance between these two readings was compared using ROC curves., Results: Compared to morphological sequences alone, the addition of multiparametric analysis significantly increased the diagnostic performance for all histological types (p < 0.001 to p = 0.011), except for lymphomas, where the increase was not significant (AUC 1.00 vs. 0.99, p = 0.066). ADCmean was the best parameter to identify pleomorphic adenomas, carcinomas and lymphomas with respective cut-offs of 1.292 × 10 -3  mm 2 /s, 1.181 × 10 -3  mm 2 /s and 0.611 × 10 -3  mm 2 /s, respectively. × 10 -3  mm 2 /s. The mean extracellular-extravascular space coefficient was the best parameter to Warthin tumours from the others, with a cut-off of 0.07., Conclusions: The addition of decision tree prediction models based on multiparametric sequences improves the non-invasive diagnostic performance of parotid gland tumours. ADC and extracellular-extravascular space coefficient are the two best parameters for decision making., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background and Purpose: Extent and dynamic of neurodegeneration in progressive multiple sclerosis (MS) might be reflected by global and regional brain perfusion, an outcome at the intercept between structure and function. Here, we provide a first insight into the evolution of brain perfusion and its association with disability in primary progressive MS (PPMS) over several years., Methods: Seventy-seven persons with PPMS were followed over up to 5 years. Visits included a 3-T magnetic resonance imaging with pulsed arterial spin labelling perfusion, the Timed 25-Foot Walk, 9-Hole Peg Test (NHPT), Symbol Digit Modalities Test (SDMT), and Expanded Disability Status Scale (EDSS). We extracted regional cerebral blood flow surrogates and compared them to 11 controls. Analyses focused on cortical and deep grey matter, the change over time, and associations with disability on the regional and global levels., Results: Baseline brain perfusion of patients and controls was comparable for the cortex (p = 0.716) and deep grey matter (p = 0.095). EDSS disability increased mildly (p = 0.023), whereas brain perfusion decreased during follow-up (p < 0.001) and with disease duration (p = 0.009). Lower global perfusion correlated with higher disability as indicated by EDSS, NHPT, and Timed 25-Foot Walk (p < 0.001). The motor task NHPT showed associations with 20 grey matter regions. In contrast, better SDMT performance correlated with lower perfusion (p < 0.001) in seven predominantly frontal regions, indicating a functional maladaptation., Conclusions: Decreasing perfusion indicates a putative association with MS disease mechanisms such as neurodegeneration, reduced metabolism, and loss of resilience. A low alteration rate limits its use in clinical practice, but regional association patterns might provide a snapshot of adaptive and maladaptive functional reorganization., (© 2022 European Academy of Neurology.)