Search Results (4)

Background/Objectives: Delayed cerebral ischemia (DCI) after an aneurysmal subarachnoid hemorrhage (aSAH) often presents with bilateral vasospasm and cortical spreading depolarizations. Computer tomography perfusion (CTP) is the prevailing screening method for detecting early changes in the cerebral blood flow. Commonly used CTP thresholds include an rCBF < 30% for the core volume and a Tmax > 6 s for hypoperfused tissue detection in acute ischemic stroke. These stroke algorithm computing thresholds compared to the contralateral hemisphere may or may not apply to detect tissue at risk of DCI. We aimed to quantify the volumetric agreement of three different stroke algorithms compared to the final infarct volumes as the standard. Methods: Furthermore, 123 CTP datasets of 75 patients with aSAH suspicious of DCI were processed using Intellispace Portal (ISP), Cercare Threshold, and Cercare Artificial Intelligence (AI) to calculate the tissue-at-risk (hypoperfused) and non-viable tissue (core) volumes. CT infarct volumes in plain CTs were segmented in the follow-up study by using a 3D slicer. Results: The calculated core volumes corresponded best to the final infarct volumes if DCI-related treatment was performed subsequently. Additional postprocessing improved the calculation of core volumes but overestimated the tissue at risk of hypoperfusion in DCI. Whereas the accuracy of tissue-at-risk prediction accelerated without treatment, underlining the importance of intra-arterial spasmolysis and induced hypertension in the prevention of DCI. Conclusions: Cercare AI and ISP revealed a sensitivity of 100% each, with a serious low specificity of <5% that was independent of treatment. Overall, the Cercare Threshold, applying the commonly used stroke thresholds, performed the best in predicting tissue at risk of hypoperfusion in DCI. Full article

Background: Body composition assessment using computed tomography (CT) scans may be hampered by software costs. To facilitate its implementation in resource-limited settings, two open-source segmentation programs (Horos and CoreSlicer) were transversally validated in colorectal cancer patients. Methods: Contrast-enhanced abdominal CT scans were analyzed following the Alberta protocol. The Cross-Sectional Area (CSA) and intensities of skeletal muscle tissue (MT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intramuscular adipose tissue (IMAT) were measured. The Skeletal Muscle Index (SMI) was calculated. Cutoff points were applied to the SMI, MT intensity, and VAT CSA to define muscle atrophy, myosteatosis, and abdominal obesity. The inter-software agreement was evaluated using different statistical tools. Results: A total of 68 participants were measured. The MT CSA and SMI displayed no differences. The MT CSA agreement was excellent, and both programs provided equal muscle atrophy prevalences. CoreSlicer underestimated the MT intensity, with a non-significant myosteatosis prevalence increase (+5.88% and +8.82%) using two different operative definitions. CoreSlicer overestimated the CSA and intensity in both VAT and SAT, with a non-significant increase (+2.94%) in the abdominal obesity prevalence. Conclusions: Both software programs were feasible tools in the study group. The MT CSA showed great inter-software agreement and no muscle atrophy misdiagnosis. Segmentation differences in the MT intensity and VAT CSA caused limited diagnostic misclassification in the study sample. Full article

Together, the Camper’s and Scarpa’s fasciae form the superficial fat layer of the abdominal wall. Though they have clinical and surgical relevance, little is known about their role in body composition across diverse patient populations. This study aimed to determine the relationship between patient characteristics, including sex and body mass index, and the distribution of Camper’s and Scarpa’s fascial layers in the abdominal wall. A total of 458 patients’ abdominal CT examinations were segmented via CoreSlicer 1.0 to determine the surface area of each patient’s Camper’s, Scarpa’s, and visceral fascia layers. The reproducibility of segmentation was corroborated by an inter-rater analysis of segmented data for 20 randomly chosen patients divided between three study investigators. Pearson correlation and Student’s t-test analyses were performed to characterize the relationship between fascia distribution and demographic factors. The ratios of Camper’s fascia, both as a proportion of superficial fat (r = −0.44 and p < 0.0001) and as a proportion of total body fat (r = −0.34 and p < 0.0001), showed statistically significant negative correlations with BMI. In contrast, the ratios of Scarpa’s fascia, both as a proportion of superficial fat (r = 0.44 and p < 0.0001) and as a proportion of total body fat (r = 0.41 and p < 0.0001), exhibited statistically significant positive correlations with BMI. Between sexes, the females had a higher ratio of Scarpa’s facia to total body fat compared to the males (36.9% vs. 31% and p < 0.0001). The ICC values for the visceral fat, Scarpa fascia, and Camper fascia were 0.995, 0.991, and 0.995, respectively, which were all within the ‘almost perfect’ range (ICC = 0.81–1.00). These findings contribute novel insights by revealing that as BMI increases the proportion of Camper’s fascia decreases, while the ratio of Scarpa’s fascia increases. Such insights expand the scope of body composition studies, which typically focus solely on superficial and visceral fat ratios. Full article

Reaction-diffusion models have been proposed for decades to capture the growth of gliomas. Nevertheless, these models require an initial condition: the tumor cell density distribution over the whole brain at diagnosis time. Several works have proposed to relate this distribution to abnormalities visible on magnetic resonance imaging (MRI). In this work, we verify these hypotheses by stereotactic histological analysis of a non-operated brain with glioblastoma using a 3D-printed slicer. Cell density maps are computed from histological slides using a deep learning approach. The density maps are then registered to a postmortem MR image and related to an MR-derived geodesic distance map to the tumor core. The relation between the edema outlines visible on T2-FLAIR MRI and the distance to the core is also investigated. Our results suggest that (i) the previously proposed exponential decrease of the tumor cell density with the distance to the core is reasonable but (ii) the edema outlines would not correspond to a cell density iso-contour and (iii) the suggested tumor cell density at these outlines is likely overestimated. These findings highlight the limitations of conventional MRI to derive glioma cell density maps and the need for other initialization methods for reaction-diffusion models to be used in clinical practice. Full article