Published

Abstract

Objective

This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations for dual-energy X-ray absorptiometry (DXA) scans from chest low-dose computed tomography (LDCT).

Methods

The framework was developed using data from 321 individuals and validated with an independent test cohort of 186 individuals. It addresses two classification tasks: (1) distinguishing normal from abnormal bone mineral density (BMD) and (2) differentiating osteoporosis from non-osteoporosis. Three field-of-view (FOV) settings—fitFOV (entire vertebra), halfFOV (vertebral body only), and largeFOV (fitFOV + 20 %)—were analyzed to assess their impact on model performance. Model predictions were weighted and combined to enhance classification accuracy, and visualizations were generated to improve decision transparency. DXA scans were recommended for individuals classified as having abnormal BMD or osteoporosis.

Results

The ensemble framework significantly outperformed individual models in both classification tasks (McNemar test, p < 0.001). In the development cohort, it achieved 91.6 % accuracy for task 1 with largeFOV (area under the receiver operating characteristic curve [AUROC]: 0.97) and 86.0 % accuracy for task 2 with fitFOV (AUROC: 0.94). In the test cohort, it demonstrated 86.6 % accuracy for task 1 (AUROC: 0.93) and 76.9 % accuracy for task 2 (AUROC: 0.99). DXA recommendation accuracy was 91.6 % and 87.1 % in the development and test cohorts, respectively, with notably high accuracy for osteoporosis detection (98.7 % and 100 %).

Conclusions

This combined ViT–CNN framework effectively assesses bone status from LDCT images, particularly when utilizing fitFOV and largeFOV settings. By visualizing classification confidence and vertebral abnormalities, the proposed framework enhances decision transparency and supports clinicians in making informed DXA recommendations following opportunistic osteoporosis screening.

Abstract
Lung cancer remains the leading cause of cancer deaths in many developed countries, primarily due to late-stage diagnosis. Histopathology, the gold standard for diagnosis, often results in semi-structured pathological reports containing complex information that can hinder timely clinical decision-making. This study evaluates the effectiveness of pre-trained language models (PLMs) in extracting 16 critical features from pathology reports (as defined by National Comprehensive Cancer Network (NCCN) guidelines) and then using them for survival prediction in patients with advanced-stage (AJCC stage IIIB–IV) lung cancer. Approximately 20,000 pathology reports from 4600 lung cancer patients across three Taipei Medical University (TMU) hospitals were analyzed. Rigorous validation included 10-fold cross-validation on 3047 annotated reports from TMU Hospital and external validation with 1258 reports from National Taiwan University Hospital (NTUH). Among the models tested, the fine-tuned LLaMA 3 demonstrated superior accuracy, achieving a 92 % F1-score in feature extraction and a 70 % F1-score in survival prediction. Cross-hospital validation confirmed its robustness and generalizability, highlighting its potential for clinical application. This research underscores the transformative potential of PLMs in lung cancer care to enable automated extraction of critical clinical indicators and improve survival prediction, which can enhance decision-making efficiency and patient outcomes.

Abstract

Background: Glioblastoma multiforme (GBM) is an aggressive brain tumor with chemoresistant, immunosuppressive, and invasive properties. Despite standard therapies, including surgery, radiotherapy, and temozolomide (TMZ) chemotherapy, tumors inevitably recur in the peritumoral region. Targeting GBM-mediated immunosuppressive and invasive properties is a promising strategy to improve clinical outcomes.

Methods: We utilized clinical and genomic data from the Taiwan GBM cohort and The Cancer Genome Atlas (TCGA) to analyze RNA sequencing data from patient tumor samples, determining the association of interleukin-19 (Il-19) expression with survival and immunosuppressive activity. Gene set enrichment analysis (GSEA) was performed to assess the relationship between the enrichment levels of immune subsets and Il-19 expression level, and Ingenuity Pathway Analysis (IPA) was used to predict immune responses. Cytokine array and single-cell RNA sequencing were used to examine the effects of IL-19 blockade on tumor immune microenvironment, including tumor-infiltrating leukocyte profiles, differentiation and immunosuppressive genes expression in tumor associated macrophages (TAM). CRISPR Il-19-/- cell lines and Il-19-/- mice were used to examine the role of IL-19 in tumor invasion and M2-like macrophage-mediated immunosuppression. Additionally, we developed novel cholesterol-polyethylene glycol-superparamagnetic iron oxide-IL-19 antibody nanoparticles (CHOL-PEG-SPIO-IL-19), characterized them using dynamic light scattering and transmission electron microscopy, Fourier-Transform Infrared spectroscopy, prussian blue assay, and conducted in vivo magnetic resonance imaging (MRI) in a human glioblastoma stem cell-derived GBM animal model.

Result: Genomic screening and IPA analysis identified IL-19 as a predicted immunosuppressive cytokine in the peritumoral region, associated with poor survival in patients with GBM. Blocking IL-19 significantly inhibited tumor progression of both TMZ-sensitive (TMZ-S) and TMZ-resistant (TMZ-R) GBM-bearing mice, and modulated the immune response within the GBM microenvironment. Single-cell transcriptome analysis reveal that IL-19 antibody treatment led to a marked increase in dendritic cells and monocyte/macrophage subsets associated with interferon-gamma signaling pathways. IL-19 blockade promoted T cell activation and reprogrammed tumor-associated macrophages toward weakened pro-tumoral phenotypes with reduced Arginase 1 expression. Il19-/- M2-like bone marrow-derived macrophages with lower Arginase 1 level lost their ability to suppress CD8 T cell activation. These findings indicated that IL-19 suppression limits TAM-mediated immune suppression. Molecular studies revealed that IL-19 promotes TMZ-resistant GBM cell migration and invasion through a novel IL-19/WISP1 signaling pathway. For clinical translation, we developed a novel CHOL-PEG-SPIO-IL-19 nanoparticles to target IL-19 expression in glioblastoma tissue. MRI imaging demonstrated enhanced targeting efficiency in brain tumors, with in vivo studies showing prominent hypointense areas in T2*-weighted MRI scans of tumor-bearing mice injected with CHOL-PEG-SPIO-IL-19, highlighting nanoparticle presence in IL-19-expressing regions. Prussian blue staining further confirmed the localization of these nanoparticles in tumor tissues, verifying their potential as a diagnostic tool for detecting IL-19 expression in glioblastoma. This system offers a theranostic approach, integrating diagnostic imaging and targeted therapy for IL-19-expressing GBM.

Conclusion: IL-19 is a promising theranostic target for reversing immunosuppression and restricting the invasive activity of chemoresistant GBM cells.

Abstract
Ischemic stroke can lead to systemic inflammation, which can activate peripheral immune cells, causing neuroinflammation and brain injury. Meningeal lymphatics play a crucial role in transporting solutes and immune cells out of the brain and draining them into cervical lymph nodes (CLNs). However, the role of meningeal lymphatics in regulating systemic inflammation during the reperfusion stage after ischemia is not well understood. In this study, we demonstrated that brain infarct size, neuronal loss, and the effector function of inflammatory macrophage subsets were reduced after ischemia-reperfusion and disruption of meningeal lymphatics. Spatial memory function was improved in the late stage of ischemic stroke following meningeal lymphatic disruption. Brain-infiltrating immune cells, including neutrophils, monocytes, and T and natural killer cells, were reduced after cerebral ischemia-reperfusion and meningeal lymphatic disruption. Single-cell RNA sequencing analysis revealed that meningeal lymphatic disruption reprogrammed the transcriptome profile related to chemotaxis and leukocyte migration in CLN lymphatic endothelial cells (LECs), and it also decreased chemotactic CCN1 expression in floor LECs. Replenishment of CCN1 through intraventricular injection increased brain infarct size and neuronal loss, while restoring numbers of macrophages/microglia in the brains of meningeal lymphatic-disrupted mice after ischemic stroke. Blocking CCN1 in cerebrospinal fluid reduced brain infarcts and improves spatial memory function after ischemia-reperfusion injury. In summary, this study indicates that CCN1-mediated detrimental inflammation was alleviated after cerebral ischemia-reperfusion injury and meningeal lymphatic disruption. CCN1 represents a novel therapeutic target for inhibiting systemic inflammation in the brain-CLN axis after ischemia-reperfusion injury.

Abstract

While gadolinium-enhanced magnetic resonance imaging (MRI) enables assessment of blood–brain barrier (BBB) integrity post-stroke, it presents inherent risks and limitations. We evaluated diffusion tensor imaging (DTI) as a contrast-free method for monitoring BBB integrity during ischemia–reperfusion (I/R) in a rat stroke model. Seventeen Sprague–Dawley rats underwent one-hour transient middle cerebral artery occlusion (MCAO). Serial MRI sequences, including DTI, perfusion-weighted imaging, and T1 mapping, were performed to monitor BBB permeability (BBBP) dynamics in regions initially identified as ischemic penumbra (IP) and ischemic core (IC) during MCAO. The fractional anisotropy (FA) ratio (rFA) was compared with BBBP changes throughout the observation period. In the IC, rFA values exhibited a 13% increase during ischemia, followed by a 10% decrease post-recanalization, and a 36% decline at 120 h post-reperfusion. Although BBBP in the IC decreased during ischemia and normalized post-reperfusion, persistent leakage was observed. The IP maintained stable rFA and BBB integrity throughout I/R. Strong negative correlations between rFA and BBBP were found in ischemic regions (IC: r = − 0.90, p < 0.001; IP: r = − 0.78, p = 0.007), with FA reductions at 24 h post-reperfusion predicting BBB disruption at 120 h. These findings suggest that DTI metrics correlate significantly with BBB integrity during I/R, indicating potential utility as a contrast-free monitoring tool for early detection of BBB impairment following stroke.

Abstract
Addressing the challenges of identifying suitable targets and effective delivery strategies is critical in pursuing therapeutic solutions for glioblastoma (GBM). This study focuses on the therapeutic potential of microRNA-124 (miR-124), known for its tumor-suppressing properties, by investigating its ability to target key oncogenic pathways in GBM. The results reveal that CDK4 and CDK6-cyclin-dependent kinases that promote cell cycle progression-are significantly overexpressed in GBM brain samples, underscoring their role in tumor proliferation and identifying them as critical targets for miR-124 intervention. However, delivering miRNA-based therapies remains a major obstacle due to the instability of RNA molecules and the difficulty in achieving targeted, efficient delivery. To address these issues, this research introduces an innovative, non-viral dual-gene delivery platform that utilizes umbilical cord mesenchymal stem cells (UMSCs) and their exosomes to transport miR-124 and programmed cell death protein-1 (PD-1). The efficacy of this dual-gene delivery system was validated using an orthotopic GBM model, which closely mimics the tumor microenvironment seen in patients. Experimental results demonstrate that the UMSC/miR-124-PD-1 complex and its exosomes successfully induce apoptosis in GBM cells, significantly inhibiting tumor growth. Notably, these treatments show minimal cytotoxic effects on normal glial cells, highlighting their safety and selectivity. Moreover, the study highlights the immunomodulatory properties of UMSC/miR-124-PD-1 and its exosomes, enhancing the activation of immune cells such as T cells and dendritic cells, while reducing immunosuppressive cells populations like regulatory T cells and myeloid-derived suppressor cells. The orchestrated dual-gene delivery system by UMSCs and exosomes showcased targeted tumor inhibition and positive immune modulation, emphasizing its potential as a promising therapeutic approach for GBM.

Abstract

Background: Diffusion tensor imaging (DTI) biomarkers can be used to quantify microstructural changes in the cerebral white matter (WM) following injury.

Objectives: This prospective single-center study aimed to evaluate whether atlas-based DTI-derived metrics obtained within 1 week after stroke can predict the motor outcome at 3 months.

Methods: Forty patients with small acute stroke (2-7 days after onset) involving the corticospinal tract were included. Each patient underwent magnetic resonance imaging (MRI) within 1 week and at 3 months after stroke, and the changes based on DTI-derived metrics were compared by performing WM tract atlas-based quantitative analysis.

Results: A total of 40 patients were included, with median age 63.5 years and a majority of males (72.5%). Patients were classified into good-prognosis group (mRS 0-2, n = 27) and poor-prognosis group (mRS 3-5, n = 13) by outcome. The median (25th-75th percentile) of MD (0.7 (0.6-0.7) vs. 0.7 (0.7-0.8); p = 0.049) and AD (0.6 (0.5, 0.7) vs. 0.7 (0.6, 0.8); p = 0.023) ratios within 1 week were significantly lower in the poor-prognosis group compared to the good-prognosis group. The ROC curve of the combined DTI-derived metrics model showed comparable Youden index (65.5% vs. 58.4%-65.4%) and higher specificity (96.3% vs. 69.2%-88.5%) compared to clinical indexes. The area under the ROC curve of the combined DTI-derived metrics model is comparable to those of the clinical indexes (all p > 0.1) and higher than those of the individual DTI-derived metrics parameters.

Conclusions: Atlas-based DTI-derived metrics at acute stage provide objective information for prognosis prediction of patients with ischemic or lacunar stroke.

Abstract

Purpose: We investigated the volumetric changes in the components of the cholinergic pathway for patients with early mild cognitive impairment (EMCI) and those with late mild cognitive impairment (LMCI). The effect of patients’ apolipoprotein 4 (APOE-ε4) allele status on the structural changes were analyzed.

Methods: Structural magnetic resonance imaging data were collected. Patients’ demographic information, plasma data, and validated global cognitive composite scores were included. Relevant features were extracted for constructing machine learning models to differentiate between EMCI (n = 312) and LMCI (n = 541) and predict patients’ neurocognitive function. The data were analyzed primarily through one-way analysis of variance and two-way analysis of covariance.

Results: Considerable differences were observed in cholinergic structural changes between patients with EMCI and LMCI. Cholinergic atrophy was more prominent in the LMCI cohort than in the EMCI cohort (P < 0.05 family-wise error corrected). APOE-ε4 differentially affected cholinergic atrophy in the LMCI and EMCI cohorts. For LMCI cohort, APOE-ε4 carriers exhibited increased brain atrophy (left amygdala: P = 0.001; right amygdala: P = 0.006, and right Ch123, P = 0.032). EMCI and LCMI patients showed distinctive associations of gray matter volumes in cholinergic regions with executive (R2 = 0.063 and 0.030 for EMCI and LMCI, respectively) and language (R2 = 0.095 and 0.042 for EMCI and LMCI, respectively) function.

Conclusions: Our data confirmed significant cholinergic atrophy differences between early and late stages of mild cognitive impairment. The impact of the APOE-ε4 allele on cholinergic atrophy varied between the LMCI and EMCI groups.

Abstract

Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model’s recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer’s disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.

Abstract

Background: The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences.

Objective: This study aimed to develop a personalized predictive model, using artificial intelligence, to assess the 5-year and 10-year dementia risk among patients with type 2 diabetes mellitus (T2DM) who are prescribed antidiabetic medications.

Methods: This retrospective multicenter study used data from the Taipei Medical University Clinical Research Database, which comprises electronic medical records from 3 hospitals in Taiwan. This study applied 8 machine learning algorithms to develop prediction models, including logistic regression, linear discriminant analysis, gradient boosting machine, light gradient boosting machine, AdaBoost, random forest, extreme gradient boosting, and artificial neural network (ANN). These models incorporated a range of variables, encompassing patient characteristics, comorbidities, medication usage, laboratory results, and examination data.

Results: This study involved a cohort of 43,068 patients diagnosed with type 2 diabetes mellitus, which accounted for a total of 1,937,692 visits. For model development and validation, 1,300,829 visits were used, while an additional 636,863 visits were reserved for external testing. The area under the curve of the prediction models range from 0.67 for the logistic regression to 0.98 for the ANNs. Based on the external test results, the model built using the ANN algorithm had the best area under the curve (0.97 for 5-year follow-up period and 0.98 for 10-year follow-up period). Based on the best model (ANN), age, gender, triglyceride, hemoglobin A1c, antidiabetic agents, stroke history, and other long-term medications were the most important predictors.

Conclusions: We have successfully developed a novel, computer-aided, dementia risk prediction model that can facilitate the clinical diagnosis and management of patients prescribed with antidiabetic medications. However, further investigation is required to assess the model’s feasibility and external validity.

Abstract

Background: This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability.

Methods: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland-Altman analysis. Feature importance was determined for explainability.

Results: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6-157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1-144.9 mL). These PVs had a volume similarity of 0.88 (0.79-0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland-Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions: Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings.

Abstract

Introduction: Although ischemia-reperfusion (I/R) injury varies between cortical and subcortical regions, its effects on specific regions remain unclear. In this study, we used various magnetic resonance imaging (MRI) techniques to examine the spatiotemporal dynamics of I/R injury within the salvaged ischemic penumbra (IP) and reperfused ischemic core (IC) of a rodent model, with the aim of enhancing therapeutic strategies by elucidating these dynamics.

Materials and methods: A total of 17 Sprague-Dawley rats were subjected to 1 h of transient middle cerebral artery occlusion with a suture model. MRI, including diffusion tensor imaging (DTI), T2-weighted imaging, perfusion-weighted imaging, and T1 mapping, was conducted at multiple time points for up to 5 days during the I/R phases. The spatiotemporal dynamics of blood-brain barrier (BBB) modifications were characterized through changes in T1 within the IP and IC regions and compared with mean diffusivity (MD), T2, and cerebral blood flow.

Results: During the I/R phases, the MD of the IC initially decreased, normalized after recanalization, decreased again at 24 h, and peaked on day 5. By contrast, the IP remained relatively stable. Both the IP and IC exhibited hyperperfusion, with the IP reaching its peak at 24 h, followed by resolution, whereas hyperperfusion was maintained in the IC until day 5. Despite hyperperfusion, the IP maintained an intact BBB, whereas the IC experienced persistent BBB leakage. At 24 h, the IC exhibited an increase in the T2 signal, corresponding to regions exhibiting BBB disruption at 5 days.

Conclusions: Hyperperfusion and BBB impairment have distinct patterns in the IP and IC. Quantitative T1 mapping may serve as a supplementary tool for the early detection of malignant hyperemia accompanied by BBB leakage, aiding in precise interventions after recanalization. These findings underscore the value of MRI markers in monitoring ischemia-specific regions and customizing therapeutic strategies to improve patient outcomes.

Abstract
Objective: This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT).
Methods: A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient’s bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation.
Results: The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05.
Conclusion: The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening.
Key points: • This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.
Keywords: Bone density; Machine learning; Osteoporosis; Radiomics.

Abstract
Background: Diffusion tensor imaging (DTI) biomarkers can be used to quantify microstructural changes in the cerebral white matter (WM) following injury.
Objectives: This prospective single-center study aimed to evaluate whether atlas-based DTI-derived metrics obtained within 1 week after stroke can predict the motor outcome at 3 months.
Methods: Forty patients with small acute stroke (2-7 days after onset) involving the corticospinal tract were included. Each patient underwent magnetic resonance imaging (MRI) within 1 week and at 3 months after stroke, and the changes based on DTI-derived metrics were compared by performing WM tract atlas-based quantitative analysis.
Results: A total of 40 patients were included, with median age 63.5 years and a majority of males (72.5%). Patients were classified into good-prognosis group (mRS 0-2, n = 27) and poor-prognosis group (mRS 3-5, n = 13) by outcome. The median (25th-75th percentile) of MD (0.7 (0.6-0.7) vs. 0.7 (0.7-0.8); p = 0.049) and AD (0.6 (0.5, 0.7) vs. 0.7 (0.6, 0.8); p = 0.023) ratios within 1 week were significantly lower in the poor-prognosis group compared to the good-prognosis group. The ROC curve of the combined DTI-derived metrics model showed comparable Youden index (65.5% vs. 58.4%-65.4%) and higher specificity (96.3% vs. 69.2%-88.5%) compared to clinical indexes. The area under the ROC curve of the combined DTI-derived metrics model is comparable to those of the clinical indexes (all p > 0.1) and higher than those of the individual DTI-derived metrics parameters.
Conclusions: Atlas-based DTI-derived metrics at acute stage provide objective information for prognosis prediction of patients with ischemic or lacunar stroke.
Keywords: Corticospinal tracts; diffusion tensor imaging; ischemic stroke; motor function; prognosis.

Abstract
Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear, because abnormality is often absent in structural imaging findings. Previous studies have demonstrated that WM functional activity estimated from functional magnetic resonance imaging (fMRI) has a high sensitivity to postconcussion WM deficits and may be used to not only evaluate but guide treatment strategies, especially targeting brain areas involved in postconcussion cognitive decline. The purpose of the study was to determine whether machine learning-based models using fMRI biomarkers and demographic or neuropsychological measures at the baseline could effectively predict the 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically matched healthy controls enrolled between September 2015 and August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to their significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after a concussion. Approximately half of the patients experienced prolonged cognitive impairment at the 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy = 87.5%), 6 months (accuracy = 83.3%), and 1 year (accuracy = 83.3%) and performed worse at the 1-year follow-up compared to the baseline assessment (accuracy = 83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.
Keywords: concussion; long-term cognitive outcome; mild traumatic brain injury; personalized prediction; support vector machine classifier; working memory.

Abstract
Considering the potential chondrotoxic effects of lidocaine, this retrospective study aimed to examine whether ultrasound-guided hydrodilatation without concurrent lidocaine infusion can still provide comparable treatment benefits for patients with adhesive capsulitis (AC). Outpatient data from 104 eligible AC patients who received ultrasound-guided hydrodilatation between May 2016 and April 2021 were reviewed. A total of 59 patients received hydrodilatation with diluted corticosteroid only, while 45 patients received treatment with mixed, diluted corticosteroid and 1% lidocaine. The overall treatment outcome was documented as the percentage of clinical improvement, ranging from 0% to 100% compared to baseline, and it was ranked into poor, moderate and good treatment outcomes. The results show no significant group-wise difference in demographics, overall treatment outcome, and number of hydrodilatations, while most patients showed moderate and good treatment outcomes. Patients with lidocaine infusion did not show greater treatment benefit. Our results suggest that ultrasound-guided hydrodilatation without concurrent lidocaine infusion can still deliver good treatment benefits for AC patients, and the findings are supportive of a modified approach toward careful intra-articular local anesthetic use during management of AC in the primary care setting.
Keywords: adhesive capsulitis; chondrotoxicity; intra-articular lidocaine; ultrasound-guided hydrodilatation.

Abstract
The functional connectivity of the default-mode network (DMN) monitored by functional magnetic resonance imaging (fMRI) in Alzheimer’s disease (AD) patients has been found weaker than that in healthy participants. Since breathing and heart beating can cause fluctuations in the fMRI signal, these physiological activities may affect the fMRI data differently between AD patients and healthy participants. We collected resting-state fMRI data from AD patients and age-matched healthy participants. With concurrent cardiac and respiratory recordings, we estimated both physiological responses phase-locked and non-phase-locked to heart beating and breathing. We found that the cardiac and respiratory physiological responses in AD patients were 3.00 ± 0.51 s and 3.96 ± 0.52 s later (both p < 0.0001) than those in healthy participants, respectively. After correcting the physiological noise in the resting-state fMRI data by population-specific physiological response functions, the DMN estimated by seed-correlation was more localized to the seed region. The DMN difference between AD patients and healthy controls became insignificant after suppressing physiological noise. Our results indicate the importance of controlling physiological noise in the resting-state fMRI analysis to obtain clinically related characterizations in AD. Keywords: Respiratory response function; cardiac response function; classification; fMRI; network; physiological noise; resting-state.

Abstract
Objectives: To compare diffusion tensor (DT)-derived indices from the thalamic nuclei and cerebrospinal fluid (CSF) hydrodynamic parameters for the prediction of gait responsiveness to the CSF tap test in early iNPH patients.
Methods: In this study, 22 patients with iNPH and 16 normal controls were enrolled with the approval of an institutional review board. DT imaging and phase-contrast magnetic resonance imaging were performed in patients and controls to determine DT-related indices of the sensorimotor-related thalamic nuclei and CSF hydrodynamics. Gait performance was assessed in patients using gait scale before and after the tap test. The Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were applied to compare group differences between patients and controls and assess the predictive performance of gait responsiveness to the tap test in the patients.
Results: Fractional anisotropy (FA) and axial diffusivity showed significant increases in the ventrolateral (VL) and ventroposterolateral (VPL) nuclei of the iNPH group compared with those of the control group (p < 0.05). The predictions of gait responsiveness of ventral thalamic FA alone (area under the ROC curve [AUC] < 0.8) significantly outperformed those of CSF hydrodynamics alone (AUC < 0.6). The AUC curve was elevated to 0.812 when the CSF peak systolic velocity and FA value were combined for the VPL nucleus, yielding the highest sensitivity (0.769) and specificity (0.778) to predict gait responses. Conclusions: Combined measurements of sensorimotor-related thalamic FA and CSF hydrodynamics can provide potential biomarkers for gait response to the CSF tap test in patients with iNPH.
Key points: • Ventrolateral and ventroposterolateral thalamic FA may predict gait responsiveness to tap test. • Thalamic neuroplasticity can be assessed through DTI in idiopathic normal-pressure hydrocephalus. • Changes in the CST associated with gait control could trigger thalamic neuroplasticity. • Activities of sensorimotor-related circuits could alter in patients with gait disturbance. • Management of patients with iNPH could be more appropriate.
Keywords: Diffusion tensor imaging; Gait; Hydrocephalus, normal pressure; Neuronal plasticity; Thalamus.

Abstract
We designed and synthesized novel theranostic nanoparticles that showed the considerable potential for clinical use in targeted therapy, and non-invasive real-time monitoring of tumors by MRI. Our nanoparticles were ultra-small with superparamagnetic iron oxide cores, conjugated to erlotinib (FeDC-E NPs). Such smart targeted nanoparticles have the preference to release the drug intracellularly rather than into the bloodstream, and specifically recognize and kill cancer cells that overexpress EGFR while being non-toxic to EGFR-negative cells. MRI, transmission electron microscopy and Prussian blue staining results indicated that cellular uptake and intracellular accumulation of FeDC-E NPs in the EGFR overexpressing cells was significantly higher than those of the non-erlotinib-conjugated nanoparticles. FeDC-E NPs inhibited the EGFR-ERK-NF-κB signaling pathways, and subsequently suppressed the migration and invasion capabilities of the highly invasive and migrative CL1-5-F4 cancer cells. In vivo tumor xenograft experiments using BALB/c nude mice showed that FeDC-E NPs could effectively inhibit the growth of tumors. T2-weighted MRI images of the mice showed significant decrease in the normalized signal within the tumor post-treatment with FeDC-E NPs compared to the non-targeted control iron oxide nanoparticles. This is the first study to use erlotinib as a small-molecule targeting agent for nanoparticles.

Abstract
OBJECTIVE. The purpose of this article is to use a mechanism-based approach to review the neuroimaging findings of abusive head trauma to infants. Advanced neuroimaging provides insights into not only the underlying mechanisms of craniocerebral injuries but also the long-term prognosis of brain injury for children on whom these injuries have been inflicted.
CONCLUSION. Knowledge of the traumatic mechanisms, the key neuroimaging findings, and the implications of functional imaging findings should help radiologists characterize the underlying causes of the injuries inflicted, thereby facilitating effective treatment.

Abstract
The effects and possible underlying mechanism of curcumin combined with radiation in human hepatocellular carcinoma (HCC) cells in vitro were evaluated. The effects of curcumin, radiation, and combination of both on cell viability, apoptosis, NF-κB activation, and expressions of NF-κB downstream effector proteins were investigated with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), NF-κB reporter gene, mitochondrial membrane potential (MMP), electrophoretic mobility shift (EMSA), and Western blot assays in Huh7-NF-κB-luc2, Hep3B, and HepG2 cells. Effect of I kappa B alpha mutant (IκBαM) vector, a specific inhibitor of NF-κB activation, on radiation-induced loss of MMP was also evaluated. Results show that curcumin not only significantly enhances radiation-induced cytotoxicity and depletion of MMP but inhibits radiation-induced NF-κB activity and expressions of NF-κB downstream proteins in HCC cells. IκBαM vector also shows similar effects. In conclusion, we suggest that curcumin augments anticancer effects of radiation via the suppression of NF-κB activation.

Abstract
Recent advances in the treatment of cerebral gliomas have increased the demands on noninvasive neuroimaging for the diagnosis, therapeutic planning, tumor monitoring, and patient outcome prediction. In the meantime, improved magnetic resonance (MR) imaging techniques have shown much potentials in evaluating the key pathological features of the gliomas, including cellularity, invasiveness, mitotic activity, angiogenesis, and necrosis, hence, further shedding light on glioma grading before treatment. In this paper, an update of advanced MR imaging techniques is reviewed, and their potential roles as biomarkers of tumor grading are discussed.

Abstract
Background and purpose: Brain enhancement on contrast-enhanced T1-weighted imaging (CET1-WI) after ischemic stroke is generally accepted as an indicator of the blood-brain barrier disruption. However, this phenomenon usually starts to become visible at the subacute phase. The purpose of this study was to evaluate the time-course profiles of K(trans), cerebral blood volume (vp), and CET1-WI with early detection of blood-brain barrier changes on K(trans) maps and their role for prediction of subsequent hemorrhagic transformation in acute middle cerebral arterial infarct.
Methods: Twenty-six patients with acute middle cerebral arterial stroke and early spontaneous reperfusion, whose MR images were obtained at predetermined stroke stages, were included. T2*-based MR perfusion-weighted images were acquired using the first-pass pharmacokinetic model to derive K(trans) and vp. Parenchymal enhancement observed on maps of K(trans), vp, and CET1-WI at each stage was compared. Association among these measurements and hemorrhagic transformation was analyzed.
Results: K(trans) map showed significantly higher parenchymal enhancement in ischemic parenchyma as compared with that of vp map and CET1-WI at early stroke stages (P<0.05). The increased K(trans) at acute stage was not associated with parenchymal enhancement in CET1-WI at the same stage. Parenchymal enhancement in CET1-WI started to occur at the late subacute stage and tended to be luxury reperfusion-dependent. Patients with hemorrhagic transformation showed higher mean K(trans) values as compared with patients without hemorrhagic transformation (P=0.02). Conclusions: Postischemic brain enhancement on routine CET1-WI seems to be closely related to the luxury reperfusion at the late subacute stage and is not dependent on microvascular permeability changes at the acute stage.
Keywords: Ktrans; blood–brain barrier; parenchymal enhancement.

Abstract
Objectives: To investigate the ability of susceptibility-weighted imaging (SWI) to predict stroke evolution in comparison with perfusion-weighted imaging (PWI).
Methods: In a retrospective analysis of 15 patients with non-lacunar ischaemic stroke studied no later than 24 h after symptom onset, we used the Alberta Stroke Program Early CT Score (ASPECTS) to compare lesions on initial diffusion-weighted images (DWI), SWI, PWI and follow-up studies obtained at least 5 days after symptom onset. The National Institutes of Health Stroke Scale scores at entry and stroke risk factors were documented. The clinical-DWI, SWI-DWI and PWI-DWI mismatches were calculated.
Results: SWI-DWI and mean transit time (MTT)-DWI mismatches were significantly associated with higher incidence of infarct growth (P = 0.007 and 0.028) and had similar ability to predict stroke evolution (P = 1.0). ASPECTS values on initial DWI, SWI and PWI were significantly correlated with those on follow-up studies (P ≤ 0.026) but not associated with infarct growth. The SWI ASPECTS values were best correlated with MTT ones (ρ = 0.8, P < 0.001). Conclusions: SWI is an alternative to PWI to assess penumbra and predict stroke evolution. Further prospective studies are needed to evaluate the role of SWI in guiding thrombolytic therapy. Key Points • SWI can provide perfusion information comparable to MTT • SWI-DWI mismatch can indicate ischaemic penumbra • SWI-DWI mismatch can be a predictor for stroke evolution.

Abstract
Susceptibility-weighted imaging (SWI) is commonly used to diagnose cerebral hemorrhage, calcification, and other T2* lesions. Its role in the detection of cerebral thromboemboli has been suggested for emboli of the anterior division of the middle cerebral artery (MCA). The purpose of our study was to determine SWI’s accuracy and sensitivity in detection of all sites of cerebral thromboemboli, not just MCA emboli. Two neuroradiologists retrospectively reviewed consecutive MRI brain examinations with SWI for cerebral thromboemboli in 100 patients with clinical suspicion for stroke determined by the NIH Stroke Scale (NIHSS) score. FLAIR, MRA, CT, and catheter angiography were reviewed for thromboemboli in the same patients. Thromboembolic sites included: the internal carotid artery (ICA) terminus, anterior MCA, posterior MCA, any other cerebral artery, or if not present. The exclusion criteria included: no magnetic resonance angiogram (MRA) or catheter angiogram for comparison, lack of restricted diffusion, lacunar infarcts, and the presence of massive hemorrhage. The accuracy, sensitivity, and specificity of each imaging modality were determined. Twenty-four patients were excluded based on the aforementioned criteria. Cerebral thromboemboli were identified in 35 of the remaining 76 patients. Of the 35 patients with thromboemboli, 30 were identified on SWI. FLAIR detected 22/35 emboli, MRA 30/33, CT 18/35, and catheter angiography 12/12. The accuracies for SWI, FLAIR, and CT were 97%, 84%, and 74%, respectively. The sensitivities for SWI, FLAIR, and CT were 85%, 61%, and 52%, respectively. The specificities for SWI, FLAIR, and CT were 100%, 98%, and 93%, respectively. There is an adjunctive role of SWI to identify cerebral thromboemboli in patients with acute infarction. SWI is superior to FLAIR and CT, and complementary to MRA and catheter angiography in emboli detection. This study supports SWI detection of MCA emboli, but also emphasizes its utility in emboli detection of other arteries based on a high accuracy and sensitivity.

Abstract
We describe findings suggestive of brain death on susceptibility-weighted imaging. We retrospectively reviewed brain magnetic resonance (MR) images of 15 patients who had cardiac arrest and found four cases with evidence of brain death. We then reviewed susceptibility-weighted imaging (SWI) findings on these cases. SWI images in the four cases with brain death showed deoxygenated blood in intracranial arteries. This preliminary result suggests that SWI may be used to diagnose brain death.

Abstract
Magnetic resonance susceptibility-weighted imaging (SWI), a novel 3D gradient echo MRI sequence exploiting phase and magnitude data for post-processing, is able to detect blood, iron, calcification and deoxygenated hemoglobin content for brain. SWI has been widely used to evaluate cerebral vascular disorders, trauma, multiple sclerosis, and tumors. We have also used SWI to evaluate acute stroke patients to identify thrombosis and possible penumbra. The acquisition was too long for examining acute stroke patients due to motion from agitation and mental changes. We have altered the parameters of phase resolution, voxel size, matrix size and partial Fourier to shorten the acquisition time to improve the diagnostic quality of SWI for acute stroke patients. The result was to reduce the acquisition time from 3:46 min to 2:14 min thereby providing a helpful tool in screening stroke patients.

Abstract
We aimed to demonstrate the complications of carotid-cavernous fistula (CCF) and their correlation with venous hypertension. From August 2000 to April 2008 we performed more than 2400 catheter angiographic procedures. Among those, six unusual cases presented acute complications of CCF. The presented complications of CCF from our cases showed a possible correlation with venous hypertension. With the experiences from our cases, venous hypertension may complicate CCF with a poor prognosis. The condition should be carefully evaluated and if present prompt treatment is necessary.

12345

Address: No.250, Wu Hsing Street, Taipei City 110, Taiwan (R.O.C.)
Telephone: +886 02-27361661 ext.8902
E-mail: tmutirc@tmu.edu.tw

© Copyright – TMU Translational Imaging Research Center – design by Morcept