ABSTRACT
OBJECTIVE: This study aimed to determine the feasibility and limitations of deep learning-based coronary calcium scoring using positron emission tomography-computed tomography (PET-CT) in comparison with coronary calcium scoring using ECG-gated non-contrast-enhanced cardiac computed tomography (CaCT). MATERIALS AND METHODS: A total of 215 individuals who underwent both CaCT and PET-CT were enrolled in this retrospective study. The Agatston method was used to calculate the coronary artery calcium scores (CACS) from CaCT, PET-CT(reader), and PET-CT(AI) to analyse the effect of using different modalities and AI-based software on CACS measurement. The total CACS and CACS classified according to the CAC-DRS guidelines were compared between the three sets of CACS. The differences, correlation coefficients, intraclass coefficients (ICC), and concordance rates were analysed. Statistical significance was set at p < 0.05. RESULTS: The correlation coefficient of the total CACS from CaCT and PET-CT(reader) was 0.837, PET-CT(reader) and PET-CT(AI) was 0.894, and CaCT and PET-CT(AI) was 0.768. The ICC of CACS from CaCT and PET-CT(reader) was 0.911, PET-CT(reader) and PET-CT(AI) was 0.958, and CaCT and PET-CT(AI) was 0.842. The concordance rate between CaCT and PET-CT(AI) was 73.8%, with a false-negative rate of 37.3% and a false-positive rate of 4.4%. Age and male sex were associated with an increased misclassification rate. CONCLUSIONS: Artificial intelligence-assisted CACS measurements in PET-CT showed comparable results to CACS in coronary calcium CT. However, the relatively high false-negative results and tendency to underestimate should be of concern. CLINICAL RELEVANCE STATEMENT: Application of automated calcium scoring to PET-CT studies could potentially select patients at high risk of coronary artery disease from among cancer patients known to be susceptible to coronary artery disease and undergoing routine PET-CT scans. KEY POINTS: ⢠Cancer patients are susceptible to coronary disease, and PET-CT could be potentially used to calculate coronary artery calcium score (CACS). ⢠Calcium scoring using artificial intelligence in PET-CT automatically provides CACS with high ICC to CACS in coronary calcium CT. ⢠However, underestimation and false negatives of CACS calculation in PET-CT should be considered.
ABSTRACT
Purpose/objectives: Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. Methods and materials: A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. Results: RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). Conclusion: RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment.
ABSTRACT
Hypoxic-ischemic encephalopathy (HIE) is a devastating neonatal brain condition caused by lack of oxygen and limited blood flow. Environmental enrichment (EE) is a classic paradigm with a complex stimulation of physical, cognitive, and social components. EE can exert neuroplasticity and neuroprotective effects in immature brains. However, the exact mechanism of EE on the chronic condition of HIE remains unclear. HIE was induced by a permanent ligation of the right carotid artery, followed by an 8% O2 hypoxic condition for 1 h. At 6 weeks of age, HIE mice were randomly assigned to either standard cages or EE cages. In the behavioral assessments, EE mice showed significantly improved motor performances in rotarod tests, ladder walking tests, and hanging wire tests, compared with HIE control mice. EE mice also significantly enhanced cognitive performances in Y-maze tests. Particularly, EE mice showed a significant increase in Cav 2.1 (P/Q type) and presynaptic proteins by molecular assessments, and a significant increase of Cav 2.1 in histological assessments of the cerebral cortex and hippocampus. These results indicate that EE can upregulate the expression of the Cav 2.1 channel and presynaptic proteins related to the synaptic vesicle cycle and neurotransmitter release, which may be responsible for motor and cognitive improvements in HIE.
