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Abstract Background Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor–node–metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck–lymph node radiotherapy in patients with NPC. Methods Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression. Results DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73–0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively. Conclusion Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool.

Children with severe β-thalassemia major (β-TM) are at high risk of developing toxoplasmosis after allogeneic hematopoietic stem cell transplantation (allo-HSCT). The aim of this study was to identify the neuroimaging findings of cerebral toxoplasmosis in pediatric patient with β-TM for early diagnosis and treatment of cerebral toxoplasmosis. We performed a retrospective assessment of clinical and neuroimaging data of children with severe β-TM who had cerebral toxoplasmosis after allo-HSCT. Additionally, we reviewed and summarized the literature on cerebral toxoplasmosis in patients with other underlying conditions. This case series identified three children who had severe β-TM and had subsequent cerebral toxoplasmosis after allo-HSCT. In addition, we identified 23 patients from literature who had toxoplasmosis and had underlying conditions other than β-TM. We found that the most common clinical symptom among the patients from our series and the patients from literature was fever upon presentation. We identified the typical neuroimaging findings including brain lesions with ring enhancement and eccentric/central nuclear target-like enhancement, which should facilitate early diagnosis and treatment of cerebral toxoplasmosis.

Abstract Purpose Primary central nervous system post-transplantation lymphoproliferative disorder (PCNS-PTLD) is a rare but serious complication of hematopoietic stem cell transplantation (HSCT) in patients with severe β-thalassemia. This study aimed to assess the clinical presentation, pathological characteristics, neuroimaging findings, and treatment strategies in patients with β-thalassemia who developed PCNS-PTLD and to compare a case series from our transplant center to reported cases from literature. Methods We retrospectively reviewed our hospital database and identified four cases of pathologically confirmed PCNS-PTLD without a history of systemic PTLD in patients with severe β-thalassemia after HSCT. We also performed a relevant literature review on PCNS-PTLD. Results The median time from transplantation to diagnosis of PCNS-PTLD was 5.5 months. Intracerebral lesions were usually multiple involving both supratentorial and infratentorial regions with homogeneous or rim enhancement. All patients had pathologically confirmed PCNS-PTLD with three patients having diffuse large B-cell lymphoma and the fourth patient having plasmacytic hyperplasia. There was low response to treatment with a median survival of 83 days. Conclusion PCNS-PTLD should be considered in the differential diagnosis of patients with β-thalassemia who had an intracranial lesion on neuroimaging after HSCT. Critical relevance statement This case series with a comprehensive review of neuroimaging and clinical characteristics of children with primary central nervous system post-transplantation lymphoproliferative disorder should advance our understanding and improve management of this rare yet severe complication following transplant for β-thalassemia. Key points • We assessed clinical presentation, treatment strategies, and neuroimaging characteristics of PCNS-PTLD in patients with β-thalassemia after transplantation. • Patients with β-thalassemia may have post-transplantation lymphoproliferative disorder presenting as brain lesions on neuroimaging. • Neuroimaging findings of the brain lesions are helpful for prompt diagnosis and proper management. Graphical Abstract

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This study explores the potential of AI-assisted measurements in enhancing the prediction of major adverse cardiac events (MACE) in patients with coronary artery disease (CAD). We conducted a retrospective cohort study involving patients diagnosed with CAD who underwent coronary computed tomography angiography (CCTA). Participants were classified into MACE and non-MACE groups based on their clinical outcomes. Clinical characteristics and AI-assisted measurements of CCTA parameters, including CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI), were collected. Both univariate and multivariable logistic regression analyses were performed to identify independent predictors of MACE, which were used to build predictive models. Statistical analyses revealed three independent predictors of MACE: severe stenosis, CT-FFR ≤ 0.8, and mean FAI (P < 0.05). Seven predictive models incorporating various combinations of these predictors were developed. The model combining all three predictors demonstrated superior performance, as evidenced by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.811 (95% confidence interval [CI] 0.774 – 0.847), a sensitivity of 0.776, and a specificity of 0.726. Our findings suggest that AI-assisted CCTA analysis, particularly using fractional flow reserve (FFR) and FAI, could significantly improve the prediction of MACE in patients with CAD, thereby potentially aiding clinical decision making.

Background: Reliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI. Methods: Children with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling. Results: The final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95 % CI = 0.772–0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model. Conclusions: The nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

Abstract Background Intracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE. Methods A retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM. Results Compared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor‐enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively. Conclusion We identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor.

A 30-year-old male patient had a cyst on the left hip and progressive enlargement for more than 2 months. Combined blood tests, magnetic resonance imaging, and pathology findings, cysticercosis infection was suspected. However, the treatment for cysticercosis was ineffective. We conducted a metagenomic next-generation sequencing (mNGS) analysis on the formalin-fixed, paraffin-embedded specimen of the patient's surgically excised tissue, and the results suggested Spirometra mansoni, mNGS was further confirmed by polymerase chain reaction and phylogenetic analysis of cytochrome c oxidase subunit 1 (cox1) gene. Based on these results, we found that mNGS provided a better method of diagnosing parasitic infections.

Patients with β-thalassemia (β-TM) may have brain iron overload from long-term blood transfusions, ineffective erythropoiesis, and increased intestinal iron absorption, leading to cognitive impairment. Brain magnetic resonance imaging (MRI) methods such as the transverse relaxation rate, susceptibility-weighted imaging, and quantitative susceptibility mapping can provide quantitative, in vivo measurements of brain iron. This review assessed these MRI methods for brain iron quantification and the measurements for cognitive function in patients with β-TM. We aimed to identify the neural correlates of cognitive impairment, which should help to evaluate therapies for improving cognition and quality of life in patients with β-TM.

Purpose To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). Methods We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. A total of 144 texture features were extracted from the MRI before RHES + CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES + CCRT for NPC was explored. Results The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677). Conclusion The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES + CCRT early response in patients with NPC.

ObjectiveThis study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC).MethodsThis retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training (n = 91) and testing (n = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics–clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model.ResultsSix texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306–0.9939] and 0.904 (95% CI, 0.8431–0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841–1.0000) and 0.891 (95% CI, 0.7903–0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect.ConclusionThe radiomics–clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

Background: Brain functional alterations have been observed in children with congenital sensorineural hearing loss (CSNHL). The purpose of this study was to assess the alterations of regional homogeneity in children with CSNHL.Methods: Forty-five children with CSNHL and 20 healthy controls were enrolled into this study. Brain resting-state functional MRI (rs-fMRI) for regional homogeneity including the Kendall coefficient consistency (KCC-ReHo) and the coherence-based parameter (Cohe-ReHo) was analyzed and compared between the two groups, i.e., the CSNHL group and the healthy control group.Results: Compared to the healthy controls, children with CSNHL showed increased Cohe-ReHo values in left calcarine and decreased values in bilateral ventrolateral prefrontal cortex (VLPFC) and right dorsolateral prefrontal cortex (DLPFC). Children with CSNHL also had increased KCC-ReHo values in the left calcarine, cuneus, precentral gyrus, and right superior parietal lobule (SPL) and decreased values in the left VLPFC and right DLPFC. Correlations were detected between the ReHo values and age of the children with CSNHL. There were positive correlations between ReHo values in the pre-cuneus/pre-frontal cortex and age (p < 0.05). There were negative correlations between ReHo values in bilateral temporal lobes, fusiform gyrus, parahippocampal gyrus and precentral gyrus, and age (p < 0.05).Conclusion: Children with CSNHL had RoHo alterations in the auditory, visual, motor, and other related brain cortices as compared to the healthy controls with normal hearing. There were significant correlations between ReHo values and age in brain regions involved in information integration and processing. Our study showed promising data using rs-fMRI ReHo parameters to assess brain functional alterations in children with CSNHL.

Objective: This study aims to investigate the alterations in functional brain network in systemic lupus erythematosus patients without overt neuropsychiatric symptoms [neuropsychiatric systemic lupus erythematosus (non-NPSLE)] from the perspective of degree centrality (DC) and functional connectivity (FC) using resting-state functional magnetic resonance imaging (MRI) and multivariate pattern analysis (MVPA) approach.Methods: DC analysis was performed based on the resting-state functional MRI data derived from 47 non-NPSLE patients and 47 healthy controls (HCs). Nodes with abnormal DC were utilized as seeds for further FC analysis. The correlation between MRI variables and clinical or neuropsychological data was analyzed using Pearson correlation analysis. Finally, MVPA classification based on DC was performed.Results: When compared with the HCs, the non-NPSLE patients exhibited remarkably higher DC in the bilateral hippocampus (HIP), right insula (INS), and lower DC in the left superior parietal gyrus. Furthermore, the patients displayed significantly higher FC between the left HIP and the left INS/left dorsolateral middle frontal gyrus/left supramarginal gyrus and higher FC between the right HIP and the right middle temporal gyrus/right dorsolateral middle frontal gyrus/right dorsolateral inferior frontal gyrus/right supramarginal gyrus (all imaging variables mentioned earlier underwent cluster-level false discovery rate corrections, the voxel threshold was p < 0.001, cluster threshold was p < 0.05). Correlation analysis revealed significantly negative correlations between DC values of the right INS and disease activity and the DC values of the right HIP and the Montreal Cognitive Assessment scores. The accuracy, sensitivity, and specificity of MVPA classification based on DC were 72.34, 63.83, and 80.85%, respectively. The most discriminative power brain regions were chiefly located within the temporal, parietal, and frontal regions.Conclusion: Patients with non-NPSLE exhibited abnormal DC and FC in the brain network. MVPA based on DC possessed commendable classification ability. Our study may provide a novel perspective on the neuropathological mechanisms underlying subclinical brain damage in non-NPSLE.

ObjectivesTo assess the microstructural properties of cerebral white matter in children with congenital sensorineural hearing loss (CSNHL).MethodsChildren (>4 years of age) with profound CSNHL and healthy controls with normal hearing (the control group) were enrolled and underwent brain magnetic resonance imaging (MRI) scans with diffusion tensor imaging (DTI). DTI parameters including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were obtained from a whole-brain tract-based spatial statistics analysis and were compared between the two groups. In addition, a region of interest (ROI) approach focusing on auditory cortex, i.e., Heschl’s gyrus, using visual cortex, i.e., forceps major as an internal control, was performed. Correlations between mean DTI values and age were obtained with the ROI method.ResultsThe study cohort consisted of 23 children with CSHNL (11 boys and 12 girls; mean age ± SD: 7.21 ± 2.67 years; range: 4.1–13.5 years) and 18 children in the control group (11 boys and 7 girls; mean age ± SD: 10.86 ± 3.56 years; range: 4.5–15.3 years). We found the axial diffusivity values being significantly greater in the left anterior thalamic radiation, right corticospinal tract, and corpus callosum in the CSHNL group than in the control group (p < 0.05). Significantly higher radial diffusivity values in the white matter tracts were noted in the CSHNL group as compared to the control group (p < 0.05). The fractional anisotropy values in the Heschl’s gyrus in the CSNHL group were lower compared to the control group (p = 0.0015). There was significant negative correlation between the mean fractional anisotropy values in Heschl’s gyrus and age in the CSNHL group < 7 years of age (r = −0.59, p = 0.004).ConclusionOur study showed higher axial and radial diffusivities in the children affected by CNHNL as compared to the hearing children. We also found lower fractional anisotropy values in the Heschl’s gyrus in the CSNHL group. Furthermore, we identified negative correlation between the fractional anisotropy values and age up to 7 years in the children born deaf. Our study findings suggest that myelination and axonal structure may be affected due to acoustic deprivation. This information may help to monitor hearing rehabilitation in the deaf children.

BACKGROUND: Patients with somatization disorder (SD) have altered neural activity in the brain regions of the default mode network (DMN). However, the regional alteration of the DMN in SD remains unknown. The present study was designed to investigate the regional alterations of the DMN in patients with SD at rest. METHODS: Twenty-five first-episode, medication-naive patients with SD and 28 age-, sex-, education- matched healthy controls underwent a resting-state functional magnetic resonance imaging (fMRI) scan. The fractional amplitude of low-frequency fluctuations (fALFF) was applied to analyze the data. RESULTS: Patients with SD showed a dissociation pattern of resting-state fALFF in the DMN, with increased fALFF in the bilateral superior medial prefrontal cortex (MPFC, BA8, 9) and decreased fALFF in the left precuneus (PCu, BA7). Furthermore, significantly positive correlation was observed between the z values of the voxels within the bilateral superior MPFC and somatization subscale scores of the Symptom Check List (SCL-90) in patients with SD. CONCLUSIONS: Our findings indicate that there is a dissociation pattern of the anterior and posterior DMN in first-episode, treatment-naive patients with SD. The results provide new insight for the importance of the DMN in the pathophysiology of SD.

Background: Conductive or mixed hearing loss with an intact tympanic membrane is a group of diseases characterized by similar clinical symptoms. Definitive diagnosis depends on the findings of exploratory tympanic surgery. Cone-beam computed tomography (CBCT) has great potential for middle ear imaging. This study evaluated the diagnostic value of CBCT for conductive or mixed hearing loss with an intact tympanic membrane. Methods: CBCT and high-resolution computed tomography (HRCT) imaging data were collected from patients with an intact eardrum who received medical treatment in our hospital for conductive or mixed hearing loss from October 2020 to May 2023. The imaging characteristics and diagnostic values of CBCT and HRCT were analyzed. Results: A total of 137 patients who met the inclusion criteria and underwent CBCT were enrolled, including 89 with otosclerosis, 41 with ossicular chain interruption, and 7 with tympanosclerosis. CBCT clearly displayed a middle ear focus, such as low-density lesions located in the fissula ante fenestram, ossicular chain malformation or dislocation, and tympanic calcification foci. The area under the curve values for otosclerosis, ossicular chain interruption, and tympanic sclerosis were 0.934, 0.967, and 0.850, respectively. CBCT was more effective than HRCT for visualizing the lenticular process, incudostapedial joint, and stapes footplate. Conclusions: CBCT of the middle ear demonstrated higher-quality imaging to improve the diagnosis of conductive or mixed hearing loss with an intact tympanic membrane. Therefore, CBCT is recommended for further investigation of noninflammatory diseases of the middle ear with no special findings on HRCT. [ABSTRACT FROM AUTHOR]

Background: Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor–node–metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck–lymph node radiotherapy in patients with NPC. Methods: Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression. Results: DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73–0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively. Conclusion: Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool. [ABSTRACT FROM AUTHOR]

Purpose: Primary central nervous system post-transplantation lymphoproliferative disorder (PCNS-PTLD) is a rare but serious complication of hematopoietic stem cell transplantation (HSCT) in patients with severe β-thalassemia. This study aimed to assess the clinical presentation, pathological characteristics, neuroimaging findings, and treatment strategies in patients with β-thalassemia who developed PCNS-PTLD and to compare a case series from our transplant center to reported cases from literature. Methods: We retrospectively reviewed our hospital database and identified four cases of pathologically confirmed PCNS-PTLD without a history of systemic PTLD in patients with severe β-thalassemia after HSCT. We also performed a relevant literature review on PCNS-PTLD. Results: The median time from transplantation to diagnosis of PCNS-PTLD was 5.5 months. Intracerebral lesions were usually multiple involving both supratentorial and infratentorial regions with homogeneous or rim enhancement. All patients had pathologically confirmed PCNS-PTLD with three patients having diffuse large B-cell lymphoma and the fourth patient having plasmacytic hyperplasia. There was low response to treatment with a median survival of 83 days. Conclusion: PCNS-PTLD should be considered in the differential diagnosis of patients with β-thalassemia who had an intracranial lesion on neuroimaging after HSCT. Critical relevance statement: This case series with a comprehensive review of neuroimaging and clinical characteristics of children with primary central nervous system post-transplantation lymphoproliferative disorder should advance our understanding and improve management of this rare yet severe complication following transplant for β-thalassemia. Key points: • We assessed clinical presentation, treatment strategies, and neuroimaging characteristics of PCNS-PTLD in patients with β-thalassemia after transplantation. • Patients with β-thalassemia may have post-transplantation lymphoproliferative disorder presenting as brain lesions on neuroimaging. • Neuroimaging findings of the brain lesions are helpful for prompt diagnosis and proper management. [ABSTRACT FROM AUTHOR]

This document provides concise summaries of several research studies related to medical imaging techniques and their applications in diagnosing and treating various diseases. The studies cover a wide range of topics, including the use of AI algorithms in breast cancer detection, the effectiveness of diffusion tensor imaging in diagnosing brain tumors, and the use of PET imaging in detecting neurodegenerative diseases. The studies offer valuable insights into the potential benefits and limitations of these imaging techniques, but further research and validation are needed to confirm these findings. [Extracted from the article]

Age‐related variations in many regions and/or networks of the human brain have been uncovered using resting‐state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large‐scale cross‐sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency‐specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age‐related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro‐vascular coupling and physiological noises. Together, these results provide the first evidence for age‐related effects on global brain activity and its topographic‐dynamic representation in terms of spatiotemporal dedifferentiation. [ABSTRACT FROM AUTHOR]

Background: Intracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE. Methods: A retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM. Results: Compared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor‐enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively. Conclusion: We identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). Methods: We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. A total of 144 texture features were extracted from the MRI before RHES + CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES + CCRT for NPC was explored. Results: The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677). Conclusion: The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES + CCRT early response in patients with NPC. [ABSTRACT FROM AUTHOR]

The psychological and physiological meanings of resting-state global brain signal (GS) and GS topography have been well confirmed. However, the causal relationship between GS and local signals was largely unknown. Based on the Human Connectome Project dataset, we investigated the effective GS topography using the Granger causality (GC) method. In consistent with GS topography, both effective GS topographies from GS to local signals and from local signals to GS showed greater GC values in sensory and motor regions in most frequency bands, suggesting that the unimodal superiority is an intrinsic architecture of GS topography. However, the significant frequency effect for GC values from GS to local signals was primarily located in unimodal regions and dominated at slow 4 frequency band whereas that from local signals to GS was mainly located in transmodal regions and dominated at slow 6 frequency band, consisting with the opinion that the more integrated the function, the lower the frequency. These findings provided valuable insight for the frequency-dependent effective GS topography, improving the understanding of the underlying mechanism of GS topography. [ABSTRACT FROM AUTHOR]

We describe and apply novel methodology for whole-brain analysis of resting state fMRI functional connectivity data, combining conventional multi-channel Pearson correlation with covariance analysis. Unlike correlation, covariance analysis preserves signal amplitude information, which feature of fMRI time series may carry physiological significance. Additionally, we demonstrate that dimensionality reduction of the fMRI data offers several computational advantages including projection onto a space of manageable dimension, enabling linear operations on functional connectivity measures and exclusion of variance unrelated to resting state network structure. We show that group-averaged, dimensionality reduced, covariance and correlation matrices are related, to reasonable approximation, by a single scalar factor. We apply this methodology to the analysis of a large, resting state fMRI data set acquired in a prospective, controlled study of mindfulness training and exercise in older, sedentary participants at risk for developing cognitive decline. Results show marginally significant effects of both mindfulness training and exercise in both covariance and correlation measures of functional connectivity. [ABSTRACT FROM AUTHOR]

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Currently, DSC has been extensively studied in the diagnosis, differential diagnosis, and prognosis evaluation of brain lymphoma, but it has not obtained a uniform standard. By combining DSC imaging features, this study investigated the imaging features and diagnostic value of several types of tumors such as primary brain lymphoma. At the same time, this study obtained data from brain lymphoma patients by data collection and set up different groups to conduct experimental studies to explore the correlation between IVIMMRI perfusion parameters and DSC perfusion parameters in brain lymphoma. Through experimental research, it can be seen that the combination of two perfusion imaging techniques can more fully reflect the blood flow properties of the lesion, which is beneficial to determine the nature of the lesion. [ABSTRACT FROM AUTHOR]

Background: Chronic insomnia disorder (CID) is considered a major public health problem worldwide. Therefore, innovative and effective technical methods for studying the pathogenesis and clinical comprehensive treatment of CID are urgently needed. Methods: Real-time fMRI neurofeedback (rtfMRI-NF), a new intervention, was used to train 28 patients with CID to regulate their amygdala activity for three sessions in 6 weeks. Resting-state fMRI data were collected before and after training. Then, voxel-based degree centrality (DC) method was used to explore the effect of rtfMRI-NF training. For regions with altered DC, we determined the specific connections to other regions that most strongly contributed to altered functional networks based on DC. Furthermore, the relationships between the DC value of the altered regions and changes in clinical variables were determined. Results: Patients with CID showed increased DC in the right postcentral gyrus, Rolandic operculum, insula, and superior parietal gyrus and decreased DC in the right supramarginal gyrus, inferior parietal gyrus, angular gyrus, middle occipital gyrus, and middle temporal gyrus. Seed-based functional connectivity analyses based on the altered DC regions showed more details about the altered functional networks. Clinical scores in Pittsburgh sleep quality index, insomnia severity index (ISI), Beck depression inventory, and Hamilton anxiety scale decreased. Furthermore, a remarkable positive correlation was found between the changed ISI score and DC values of the right insula. Conclusions: This study confirmed that amygdala-based rtfMRI-NF training altered the intrinsic functional hubs, which reshaped the abnormal functional connections caused by insomnia and improved the sleep of patients with CID. These findings contribute to our understanding of the neurobiological mechanism of rtfMRI-NF in insomnia treatment. However, additional double-blinded controlled clinical trials with larger sample sizes need to be conducted to confirm the effect of rtfMRI-NF from this initial study. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the alterations in functional brain network in systemic lupus erythematosus patients without overt neuropsychiatric symptoms [neuropsychiatric systemic lupus erythematosus (non-NPSLE)] from the perspective of degree centrality (DC) and functional connectivity (FC) using resting-state functional magnetic resonance imaging (MRI) and multivariate pattern analysis (MVPA) approach. Methods: DC analysis was performed based on the resting-state functional MRI data derived from 47 non-NPSLE patients and 47 healthy controls (HCs). Nodes with abnormal DC were utilized as seeds for further FC analysis. The correlation between MRI variables and clinical or neuropsychological data was analyzed using Pearson correlation analysis. Finally, MVPA classification based on DC was performed. Results: When compared with the HCs, the non-NPSLE patients exhibited remarkably higher DC in the bilateral hippocampus (HIP), right insula (INS), and lower DC in the left superior parietal gyrus. Furthermore, the patients displayed significantly higher FC between the left HIP and the left INS/left dorsolateral middle frontal gyrus/left supramarginal gyrus and higher FC between the right HIP and the right middle temporal gyrus/right dorsolateral middle frontal gyrus/right dorsolateral inferior frontal gyrus/right supramarginal gyrus (all imaging variables mentioned earlier underwent cluster-level false discovery rate corrections, the voxel threshold was p < 0.001, cluster threshold was p < 0.05). Correlation analysis revealed significantly negative correlations between DC values of the right INS and disease activity and the DC values of the right HIP and the Montreal Cognitive Assessment scores. The accuracy, sensitivity, and specificity of MVPA classification based on DC were 72.34, 63.83, and 80.85%, respectively. The most discriminative power brain regions were chiefly located within the temporal, parietal, and frontal regions. Conclusion: Patients with non-NPSLE exhibited abnormal DC and FC in the brain network. MVPA based on DC possessed commendable classification ability. Our study may provide a novel perspective on the neuropathological mechanisms underlying subclinical brain damage in non-NPSLE. [ABSTRACT FROM AUTHOR]