Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments.

BACKGROUND: Current radiological assessments of 18fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment. METHODS: Our aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422). RESULTS: Our AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31-2.67) vs 1.38 (95% CI: 0.98-1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37-3.40) vs 1.40 (95% CI: 0.90-2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%. CONCLUSION: Our tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients. TRIAL REGISTRATION: Registered clinicaltrials.gov number: NCT01287741.

RIPK3 deficiency or catalytically inactive RIPK1 provides greater benefit than MLKL deficiency in mouse models of inflammation and tissue injury.

Necroptosis is a caspase-independent form of cell death that is triggered by activation of the receptor interacting serine/threonine kinase 3 (RIPK3) and phosphorylation of its pseudokinase substrate mixed lineage kinase-like (MLKL), which then translocates to membranes and promotes cell lysis. Activation of RIPK3 is regulated by the kinase RIPK1. Here we analyze the contribution of RIPK1, RIPK3, or MLKL to several mouse disease models. Loss of RIPK3 had no effect on lipopolysaccharide-induced sepsis, dextran sodium sulfate-induced colitis, cerulein-induced pancreatitis, hypoxia-induced cerebral edema, or the major cerebral artery occlusion stroke model. However, kidney ischemia-reperfusion injury, myocardial infarction, and systemic inflammation associated with A20 deficiency or high-dose tumor necrosis factor (TNF) were ameliorated by RIPK3 deficiency. Catalytically inactive RIPK1 was also beneficial in the kidney ischemia-reperfusion injury model, the high-dose TNF model, and in A20(-/-) mice. Interestingly, MLKL deficiency offered less protection in the kidney ischemia-reperfusion injury model and no benefit in A20(-/-) mice, consistent with necroptosis-independent functions for RIPK1 and RIPK3. Combined loss of RIPK3 (or MLKL) and caspase-8 largely prevented the cytokine storm, hypothermia, and morbidity induced by TNF, suggesting that the triggering event in this model is a combination of apoptosis and necroptosis. Tissue-specific RIPK3 deletion identified intestinal epithelial cells as the major target organ. Together these data emphasize that MLKL deficiency rather than RIPK1 inactivation or RIPK3 deficiency must be examined to implicate a role for necroptosis in disease.

Mouse Models of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disorder characterized by synovitis that leads to cartilage and bone erosion by invading fibrovascular tissue. Mouse models of RA recapitulate many features of the human disease. Despite the availability of medicines that are highly effective in many patient populations, autoimmune diseases (including RA) remain an area of active biomedical research, and consequently mouse models of RA are still extensively used for mechanistic studies and validation of therapeutic targets. This review aims to integrate morphologic features with model biology and cover the key characteristics of the most commonly used induced and spontaneous mouse models of RA. Induced models emphasized in this review include collagen-induced arthritis and antibody-induced arthritis. Collagen-induced arthritis is an example of an active immunization strategy, whereas antibody- induced arthritis models, such as collagen antibody-induced arthritis and K/BxN antibody transfer arthritis, represent examples of passive immunization strategies. The coverage of spontaneous models in this review is focused on the TNFΔ (ARE) mouse, in which arthritis results from overexpression of TNF-α, a master proinflammatory cytokine that drives disease in many patients.

Fully-automated, high-throughput micro-computed tomography analysis of body composition enables therapeutic efficacy monitoring in preclinical models.

BACKGROUND: The ability to non-invasively measure body composition in mouse models of obesity and obesity-related disorders is essential for elucidating mechanisms of metabolic regulation and monitoring the effects of novel treatments. These studies aimed to develop a fully automated, high-throughput micro-computed tomography (micro-CT)-based image analysis technique for longitudinal quantitation of adipose, non-adipose and lean tissue as well as bone and demonstrate utility for assessing the effects of two distinct treatments. METHODS: An initial validation study was performed in diet-induced obesity (DIO) and control mice on a vivaCT 75 micro-CT system. Subsequently, four groups of DIO mice were imaged pre- and post-treatment with an experimental agonistic antibody specific for anti-fibroblast growth factor receptor 1 (anti-FGFR1, R1MAb1), control immunoglobulin G antibody, a known anorectic antiobesity drug (rimonabant, SR141716), or solvent control. The body composition analysis technique was then ported to a faster micro-CT system (CT120) to markedly increase throughput as well as to evaluate the use of micro-CT image intensity for hepatic lipid content in DIO and control mice. Ex vivo chemical analysis and colorimetric analysis of the liver triglycerides were performed as the standard metrics for correlation with body composition and hepatic lipid status, respectively. RESULTS: Micro-CT-based body composition measures correlate with ex vivo chemical analysis metrics and enable distinction between DIO and control mice. R1MAb1 and rimonabant have differing effects on body composition as assessed by micro-CT. High-throughput body composition imaging is possible using a modified CT120 system. Micro-CT also provides a non-invasive assessment of hepatic lipid content. CONCLUSIONS: This work describes, validates and demonstrates utility of a fully automated image analysis technique to quantify in vivo micro-CT-derived measures of adipose, non-adipose and lean tissue, as well as bone. These body composition metrics highly correlate with standard ex vivo chemical analysis and enable longitudinal evaluation of body composition and therapeutic efficacy monitoring.

Cerebral hemodynamics in a healthy population measured by dynamic susceptibility contrast MR imaging.

PURPOSE: To establish reference data and to study age-dependency for cerebral perfusion in various regions of the brain in a healthy population. MATERIAL AND METHODS: Eighty healthy subjects of both genders from 22 to 85 years of age were studied with spin echo echo-planar dynamic susceptibility contrast MR imaging (DSC MRI) at 1.5 T. Cerebral blood volume (CBV), cerebral blood flow (CBF), and contrast agent mean transit time (MTT) were calculated bilaterally for 20 distinct neuroanatomic structures. RESULTS: In gray matter, the following values were found (mean +/- SD): CBV (4.6 +/- 1.0 ml/100 g), CBF (94.2 +/- 23.0 ml/100 g/min), and MTT (3.0 +/- 0.6 s), and in white matter: CBV (1.3 +/- 0.4 ml/100 g), CBF (19.6 +/- 5.8 ml/100 g/min), and MTT (4.3 +/- 0.7 s). The perfusion parameters did not change with age, except for a tendency to an increase in gray matter MTT and CBV. Males exhibited higher MTT and CBV than females. No hemispheric difference was found in either gender. CONCLUSION: Cerebral hemodynamics can be assessed with DSC MRI. Age itself seems to have only a marginal effect on cerebral perfusion in healthy population.