Background: Splenic contraction (SC) is characterized by reduced splenic volume (SV) due to the release of splenocytes into systemic circulation. This study aimed to investigate the differences between patients with acute ischemic stroke with and without SC and identify SC-associated factors.
Methods: In this retrospective study, 79 patients with acute ischemic stroke who underwent abdominal computed tomography within 5 years before and within 7 days after stroke onset were analyzed. Patients were categorized into SC (SV change ≤-5 cm3, n=39) and non-SC (SV change >0 cm3, n=40) groups based on changes in SV. Clinical characteristics and laboratory findings were compared between the two groups. Multivariate logistic regression analysis was performed to identify independent factors associated with SC.
Results: The SC group had significantly higher National Institutes of Health Stroke Scale (NIHSS) scores on admission compared to the non-SC group {median 7 [interquartile range (IQR) 3-12] vs. 5 [IQR 2-9], P=0.025}. Diabetes mellitus (DM) was less prevalent in the SC group than in the non-SC group (35.9% vs. 62.5%, P=0.018). Multivariate analysis revealed that higher NIHSS scores at admission were independently associated with SC [adjusted odds ratio (aOR) 1.119, 95% confidence interval (CI): 1.001-1.252, P=0.048], whereas DM was associated with a lower risk of SC (aOR 0.278, 95% CI: 0.097-0.798, P=0.017).
Conclusions: Patients with SC demonstrated significantly higher admission NIHSS scores, suggesting that SC is associated with greater neurological deficits during the acute phase of ischemic stroke. DM was associated with a lower incidence of post-stroke SC, possibly due to DM-associated pathophysiology such as blunted sympathetic response or oxidative stress-induced splenic dysfunction. These findings emphasize the potential role of the spleen in stroke pathophysiology and highlight its potential as a therapeutic target.
Background: Coronary artery disease (CAD) is a leading cause of death worldwide, and noninvasive diagnostic methods are essential. Although invasive coronary angiography (ICA) is the reference standard, it is invasive and carries procedural risks. Conventional coronary computed tomography angiography (CCTA) is limited by its dependence on electrocardiographic (ECG)-gating, which reduces its feasibility in patients with arrhythmias, high heart rates (HRs), or in emergency settings. Therefore, this study aimed to assess the diagnostic accuracy of a non-ECG-gated CCTA (ECG-less CCTA) protocol for identifying obstructive CAD, using ICA as the reference.
Methods: This retrospective single-center study included 110 patients with suspected CAD undergoing ECG-less CCTA [256-row computed tomography (CT) with simulated ECG signals, automated tube voltage selection (80-120 kV], and tube current modulation [noise index: 20 Hounsfield units (HU)]. Contrast administration (0.6 mL/kg) was optimized via bolus tracking. Images were reconstructed using deep learning (TrueFidelity™) and motion correction (SnapShot Freeze 2). Two blinded radiologists assessed stenosis ≥50% [Society of Cardiovascular Computed Tomography (SCCT) 18-segment model], with non-diagnostic segments classified as positive. Subgroups were stratified by HR [≤75 vs. >75 beats per minute (bpm)] and calcium burden (Agatston ≤400 vs. >400).
Results: ECG-less CCTA showed patient-level sensitivity of 92.1% [95% confidence interval (CI): 85.6-96.2%] and specificity of 91.5% (82.3-96.4%). Vessel- and segment-level specificity/negative predictive value (NPV) were 93.6%/95.1% and 96.2%/97.2%, respectively. Non-diagnostic segments (6.4%) were conservatively positive. Radiation dose was 1.4±0.5 mSv. Specificity decreased in Agatston >400 (84.6% vs. 94.1%, P=0.02), whereas HR >75 bpm did not significantly reduce sensitivity (89.7% vs. 94.1%, P=0.12).
Conclusions: ECG-less CCTA achieves high diagnostic concordance with ICA for obstructive CAD, demonstrating excellent specificity/NPV across analysis levels. Its tolerance to variable HRs and streamlined workflow support clinical utility in emergency settings or arrhythmic patients, avoiding ECG dependency and β-blockers.
Background: Gastrointestinal stromal tumors (GISTs) can undergo malignant transformation, and thus, the risk grading assessment of postoperative patients is highly critical. This study sought to investigate the correlation between ultrasonic characteristics, clinicopathological features, and biological risk grading in patients with gastric GISTs and to determine whether the preoperative prediction of biological risk grading is feasible.
Methods: The ultrasonic characteristics and clinicopathological data of gastric filling in 92 patients with GISTs confirmed by surgical pathology were retrospectively analyzed, the influencing factors of the biological risk classification of GISTs were assessed through univariate and multivariate analyses, and a prediction model was constructed. The receiver operating characteristic curve was plotted to analyze the predictive value of the logistic regression model.
Results: Univariate analysis revealed that melena was significantly more common in the high-risk group (P<0.05). Tumor size, morphology, echogenicity, calcification, and cystic changes also differed significantly between the risk groups (P<0.05), while location, growth pattern, blood flow grade, and ulceration did not (P>0.05). Multivariate analysis indicated that the independent risk predictors were tumor size [odds ratio (OR) =0.028; P=0.002] and echogenicity (OR =0.092; P=0.011). The derived logistic model (area under the curve =0.934; 95% confidence interval: 0.887-0.981) showed high sensitivity (76.4%) and specificity (97.3%). In terms of pathological findings, the Ki-67 index and mitotic count correlated strongly with risk level (P<0.05) and may serve as key prognostic markers.
Conclusions: Ultrasound-based tumor size and echogenicity are robust preoperative indicators for gastric GIST risk classification. The proposed model demonstrated excellent predictive performance and may be a practical tool for clinical assessment.
Background: White matter hyperintensity (WMH) has been reported to be associated with brain structure changes and Alzheimer's disease (AD) pathology in the aging process. This study sought to explore the underlying mechanisms linking cerebrovascular pathology, structural brain changes, and AD pathology in the aging process.
Methods: The routine magnetic resonance images of 218 cognitively normal elderly individuals who underwent venous blood sampling were retrospectively collected. The Fazekas score was used to stratify the cohort into mild (Fazekas scores of 0-1, n=113) and severe (Fazekas scores of 2-3, n=105) WMH groups. All the three-dimensional (3D) T1-weighted (T1W) images, including the original 3D T1W images and the 3D T1W images reconstructed from two-dimensional (2D) diagnostic images, were processed with FreeSurfer to determine the cortical thickness and subcortical nucleus volumes. The plasma amyloid-beta (Aβ)42 and phosphorylated tau (p-Tau) 181 levels were measured by enzyme-linked immunosorbent assay (ELISA). The cerebral small vessel disease (CSVD)-related imaging markers were assessed manually. Group comparisons of brain structures were performed using general linear models (GLMs). Partial correlation analyses were conducted to assess the associations between plasma Aβ42/p-Tau 181 and the subcortical volumes. A mediation analysis was conducted to evaluate the mediating role of the WMH burden in the relationship between plasma biomarker levels and brain structure.
Results: The participants with severe WMH were older (P<0.001) and exhibited higher plasma p-Tau 181 (P<0.001) than those in the mild WMH group, but no significant difference in plasma Aβ42 was found (P=0.065). Based on the original 3D T1W images only, the left caudate nucleus (P=0.042) was enlarged in the participants with severe WMH. Based on all the 3D T1W images, the plasma p-Tau 181 levels were found to be positively correlated with the Fazekas scores (r=0.165, P=0.015). A significant interaction was observed between age and groups in terms of the left caudate volume (β=1.288, P=0.047). More importantly, the Fazekas scores were found to partially mediate the relationship between the p-Tau 181 levels and left caudate volumes (indirect effect =1.761, P=0.035), accounting for 23.0% of the total effect.
Conclusions: Severe WMH is associated with caudate nucleus enlargement. WMH may partially mediate the association between elevated plasma p-Tau 181 and caudate nucleus enlargement, suggesting a mixed pathology in the aging process of the brain, and highlighting the importance of early vascular risk control.
Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are essential in clinical diagnosis and treatment planning, but their images are often compromised by limited contrast and insufficient detail, reducing diagnostic clarity. Traditional enhancement methods-such as histogram equalization (HE) can improve visibility but may introduce noise, over-enhancement, or structural distortion. Quantum-inspired computational techniques have recently emerged as promising tools for nonlinear and adaptive image processing. Building on the quantum signal processing (QSP) framework, this study proposes a quantum-inspired enhancement (QIE) algorithm designed to improve medical image contrast while preserving structural details.
Methods: We propose a QIE algorithm that embeds a three-pixel quantum-correlation system within a QSP framework. After normalizing grayscale values, each 3×3 neighborhood is mapped to superposition states; edge-sensitive basis states are selectively accumulated in four orientations to produce the enhanced output. The algorithm was evaluated using T2-weighted magnetic resonance (MR) brain images and CT lung images obtained from 10 different patients. Its performance was compared with four representative classical enhancement methods: HE, contrast-limited adaptive HE (CLAHE), fuzzy HE (FHE), and wavelet-based enhancement (WBE), employing quantitative metrics such as entropy, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and contrast-to-noise ratio (CNR). Paired two-sided t-tests (α=0.05) were used.
Results: QIE reached the highest mean entropy on both datasets (CT: 4.37±0.31; MR: 6.45±0.16) vs. HE 4.00±0.25 (P=2.8×10-4) and 5.67±0.16 (P=2.3×10-7) respectively, indicating superior information retention and detail enhancement. Its PSNR and SSIM were significantly better than HE, FHE, and WBE (all P<0.01), reflecting better signal fidelity and structural preservation; vs. CLAHE, QIE PSNR was -3.4 dB lower on CT and -3.3 dB lower on MR (both P<0.001), but SSIM differed by <0.001 (P≥0.13). CNR with QIE (CT: 4.00±3.54; MR: 3.66±2.81) was not statistically different from any method (P≥0.05).
Conclusions: The proposed QIE algorithm demonstrates superior performance in enhancing the contrast and preserving the structural details of medical images. By leveraging quantum-inspired mechanisms, the algorithm shows potential for improving diagnostic accuracy and supporting clinical treatment planning. Future work will explore the application of this algorithm to other imaging modalities, investigate its effectiveness as a preprocessing step for commercial artificial intelligence (AI) models, and study the integration with actual quantum computing platforms.
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