Segmentation of pre- and posttreatment diffuse glioma tissue subregions including resection cavities.

Background: Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities. Methods: A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years; 29%/36%/34% female/male/unknown; 396 pretreatment and 251 posttreatment, median 237 days post-surgery) from 7 publicly available repositories in The Cancer Imaging Archive were split into training (536) and test/generalization (111) samples. T1, T1-post-contrast, T2, and FLAIR images were used as inputs into a 3D nnU-Net to predict 3 tumor subregions and resection cavities. We evaluated the performance of networks trained on pretreatment training cases (Pre-Rx network), posttreatment training cases (Post-Rx network), and both pre- and posttreatment cases (Combined networks). Results: Segmentation performance was as good as or better than interrater reliability with median dice scores for main tumor subregions ranging from 0.82 to 0.94 and strong correlations between manually segmented and predicted total lesion volumes (0.94 < R 2 values < 0.98). The Combined network performed similarly to the Pre-Rx network on pretreatment cases and the Post-Rx network on posttreatment cases with fewer false positive resection cavities (7% vs 59%). Conclusions: Neural networks that accurately segment pre- and posttreatment diffuse gliomas have the potential to improve response assessment in clinical trials and reduce provider burden and errors in measurement.

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; P < .001) and PFS (HR = 1.04 per milliliter; P < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; P < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Keywords: Segmentation, Glioblastoma, Multishell Diffusion MRI Supplemental material is available for this article. © RSNA, 2024.

Therapeutic limb hypothermia for the treatment of traumatic acute limb ischemia.

Acute limb ischaemia (ALI) is an emergent clinical condition that strains pre-hospital resources and impacts healthcare costs and patient quality of life. Hypothermia has long been used in clinical and research settings to mitigate ischaemic damage in tissues, but prompt reperfusion is needed to prevent loss of limb or function from ALI. To address the unmet need for pre-hospital intervention of threatened limbs awaiting definitive specialty care, we have focused on controlled application of hypothermia. Over years of animal experiments, phantom limb creation, and materials selection, we conceptualised and created a portable limb-cooling device that can be used alone or combined with a traditional tourniquet or resuscitative endovascular balloon occlusion of the aorta. Here, we describe our process of building and testing the device, from computer simulation through animal-limb metabolic studies, to prototype testing.

Safety and Risk in Airway Management During Bariatric Air Medical Retrieval.

OBJECTIVE: Bariatric anatomy and physiology present added clinical challenges to the provision of safe critical care and patient transport. LifeFlight Retrieval Medicine provides air medical retrieval services in Queensland, Australia, and performs over 6,000 retrieval missions annually using rotary wing, fixed wing, and ground ambulance platforms. METHODS: Bariatric patient retrievals were identified from the LifeFlight Retrieval Medicine electronic patient database. These cases were interrogated to quantify and describe adverse events during patient transport. RESULTS: Over the study period from July 2019 to December 2021 11,096 patient retrievals were completed. Of these patients, 816 (7.3%) had a body weight ≥ 120 kg (range, 120-246 kg; median = 146 kg). Bariatric patients were more likely to be male (70%) and to require critical care interventions than nonbariatric patients (25.9% vs. 19.9%). There was an absolute 1.5% increase of high-interest events during patient retrieval, corresponding to a 1.9-fold increased relative risk. Five hundred eleven of 11,096 patients were intubated by the retrieval team, and 61 of these weighed ≥ 120 kg. Bariatric patients undergoing intubation were of similar age and sex, weighed significantly more, had nonsignificant trends toward poorer airway visualization by Cormack-Lehane laryngoscopic grade, and tended toward reduced first-attempt success compared with nonbariatric patients. Rates of airway adverse events (AAEs) were significantly increased for the bariatric group (30/61, 49.2%) compared with the nonbariatric group (135/450, 30.0%) (χ2 likelihood ratio, P = .004). Postintubation desaturation was the most common AAE and was the only criterion significantly increased when comparing bariatric (26%) versus nonbariatric (12%) patients (χ2 likelihood ratio, P = .005). Using patient weight as a continuous variable, nominal logistic regression revealed a significant effect of increasing weight on AAEs (χ2 = 12.9, P = .0003) with a threshold of 105 kg providing an optimal 88% sensitivity for predicting AAEs. The odds of AAEs were increased significantly for those weighing 105 to 119 kg versus those weighing < 105 kg (odds ratio [OR] = 3.4; 95% confidence interval [CI], 1.6-7.5) and for those weighing ≥ 120 kg versus those weighing < 105 kg (OR = 2.5; 95% CI, 1.4-4.3). There was no difference between those weighing ≥ 120 kg versus those weighing 105 to 119 kg (OR = 0.73; 95% CI, 0.3-1.8). CONCLUSION: Air medical retrieval of bariatric patients is safe despite an increased risk of adverse events. Strategies to optimize emergency anesthesia should be used to maximize safe intubation in bariatric patients.

Postoperative Instruction Retention, Written Versus Audiovisual Adjuncts: A Prospective Randomized Study.

Purpose: Adherence to postoperative protocols is an integral perioperative intervention that impacts surgical outcomes. The focus of this study was to identify the baseline postoperative instruction retention of our traditional written format and compare that with the retention when using an audiovisual adjunct. We hypothesize that the addition of audiovisual adjuncts would result in greater patient retention of their postoperative instructions. Methods: Sixty consecutive adult patients undergoing soft tissue procedures of the hand and wrist were enrolled prospectively at a single institution. Patients were randomized to receive postoperative instructions with either a written or an audiovisual adjunct format. Two days after surgery, a blinded investigator contacted the participants to administer a standardized phone questionnaire. Responses were recorded, and the data were analyzed by another blinded team member. Analysis was performed using χ 2 and Student t tests as appropriate. Results: Fifty patients were included in the final analysis. The written group scored an average retention of 80%, whereas the audiovisual group showed a retention score of 85%. Demographic analysis of men versus women, and patients <60 versus >60 years of age did not demonstrate significant score differences. The subgroup analysis of patients receiving local anesthesia alone demonstrated significantly higher rates of percent correct and perfect recall in the audiovisual compared with the written-only group (87.5 vs 80.5 and 44% vs 7%, respectively). Conclusions: For patients undergoing common soft tissue procedures of the hand, the addition of audiovisual supplementation to written instructions, especially in those undergoing wide awake, local anesthesia, no tourniquet procedures, is associated with higher rates of retention of a patient's postoperative instructions. The specific improvement in the local anesthesia cohort is especially relevant today due to an increased prevalence of wide awake, local anesthesia, no tourniquet style procedures, and the increasing reliance on patient engagement in postoperative care. Type of study/level of evidence: Randomized control trial; Diagnostic Level 2b.

Engineering Low Volume Resuscitants for the Prehospital Care of Severe Hemorrhagic Shock.

Globally, traumatic injury is a leading cause of suffering and death. The ability to curtail damage and ensure survival after major injury requires a time-sensitive response balancing organ perfusion, blood loss, and portability, underscoring the need for novel therapies for the prehospital environment. Currently, there are few options available for damage control resuscitation (DCR) of trauma victims. We hypothesize that synthetic polymers, which are tunable, portable, and stable under austere conditions, can be developed as effective injectable therapies for trauma medicine. In this work, we design injectable polymers for use as low volume resuscitants (LVRs). Using RAFT polymerization, we evaluate the effect of polymer size, architecture, and chemical composition upon both blood coagulation and resuscitation in a rat hemorrhagic shock model. Our therapy is evaluated against a clinically used colloid resuscitant, Hextend. We demonstrate that a radiant star poly(glycerol monomethacrylate) polymer did not interfere with coagulation while successfully correcting metabolic deficit and resuscitating animals from hemorrhagic shock to the desired mean arterial pressure range for DCR - correcting a 60 % total blood volume (TBV) loss when given at only 10 % TBV. This highly portable and non-coagulopathic resuscitant has profound potential for application in trauma medicine.

Effectiveness of an anti-inflammatory diet versus low-fat diet for knee osteoarthritis: the FEAST randomised controlled trial protocol.

INTRODUCTION: Chronic inflammation plays a key role in knee osteoarthritis pathophysiology and increases risk of comorbidities, yet most interventions do not typically target inflammation. Our study will investigate if an anti-inflammatory dietary programme is superior to a standard care low-fat dietary programme for improving knee pain, function and quality-of-life in people with knee osteoarthritis. METHODS AND ANALYSIS: The eFEct of an Anti-inflammatory diet for knee oSTeoarthritis study is a parallel-group, assessor-blinded, superiority randomised controlled trial. Following baseline assessment, 144 participants aged 45-85 years with symptomatic knee osteoarthritis will be randomly allocated to one of two treatment groups (1:1 ratio). Participants randomised to the anti-inflammatory dietary programme will receive six dietary consultations over 12 weeks (two in-person and four phone/videoconference) and additional educational and behaviour change resources. The consultations and resources emphasise nutrient-dense minimally processed anti-inflammatory foods and discourage proinflammatory processed foods. Participants randomised to the standard care low-fat dietary programme will receive three dietary consultations over 12 weeks (two in-person and one phone/videoconference) consisting of healthy eating advice and education based on the Australian Dietary Guidelines, reflecting usual care in Australia. Adherence will be assessed with 3-day food diaries. Outcomes are assessed at 12 weeks and 6 months. The primary outcome will be change from baseline to 12 weeks in the mean score on four Knee injury and Osteoarthritis Outcome Score (KOOS4) subscales: knee pain, symptoms, function in daily activities and knee-related quality of life. Secondary outcomes include change in individual KOOS subscale scores, patient-perceived improvement, health-related quality of life, body mass and composition using dual-energy X-ray absorptiometry, inflammatory (high-sensitivity C reactive protein, interleukins, tumour necrosis factor-α) and metabolic blood biomarkers (glucose, glycated haemoglobin (HbA1c), insulin, liver function, lipids), lower-limb function and physical activity. ETHICS AND DISSEMINATION: The study has received ethics approval from La Trobe University Human Ethics Committee. Results will be presented in peer-reviewed journals and at international conferences. TRIAL REGISTRATION NUMBER: ACTRN12622000440729.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry.

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

CRISPR/Cas9-Based Disease Modeling and Functional Correction of Interleukin 7 Receptor Alpha Severe Combined Immunodeficiency in T-Lymphocytes and Hematopoietic Stem Cells.

Interleukin 7 Receptor alpha Severe Combined Immunodeficiency (IL7R-SCID) is a life-threatening disorder caused by homozygous mutations in the IL7RA gene. Defective IL7R expression in humans hampers T cell precursors' proliferation and differentiation during lymphopoiesis resulting in the absence of T cells in newborns, who succumb to severe infections and death early after birth. Previous attempts to tackle IL7R-SCID by viral gene therapy have shown that unregulated IL7R expression predisposes to leukemia, suggesting the application of targeted gene editing to insert a correct copy of the IL7RA gene in its genomic locus and mediate its physiological expression as a more feasible therapeutic approach. To this aim, we have first developed a CRISPR/Cas9-based IL7R-SCID disease modeling system that recapitulates the disease phenotype in primary human T cells and hematopoietic stem and progenitor cells (HSPCs). Then, we have designed a knockin strategy that targets IL7RA exon 1 and introduces through homology-directed repair a corrective, promoterless IL7RA cDNA followed by a reporter cassette through AAV6 transduction. Targeted integration of the corrective cassette in primary T cells restored IL7R expression and rescued functional downstream IL7R signaling. When applied to HSPCs further induced to differentiate into T cells in an Artificial Thymic Organoid system, our gene editing strategy overcame the T cell developmental block observed in IL7R-SCID patients, while promoting full maturation of T cells with physiological and developmentally regulated IL7R expression. Finally, genotoxicity assessment of the CRISPR/Cas9 platform in HSPCs using biased and unbiased technologies confirmed the safety of the strategy, paving the way for a new, efficient, and safe therapeutic option for IL7R-SCID patients.