Association of MRI Indexes of the Perivascular Space Network and Cognitive Impairment in Patients with Obstructive Sleep Apnea.

Background The role of perivascular space (PVS) dysfunction in obstructive sleep apnea (OSA) requires further study. Purpose To compare MRI indexes of PVS across patients with differing severities of OSA and relate them with disease characteristics and treatment. Materials and Methods This single-center prospective study included healthy controls (HCs) and patients with complaints of snoring who underwent MRI and cognitive evaluation between June 2021 and December 2022. Participants with complaints of snoring were classified into four groups (snoring, mild OSA, moderate OSA, and severe OSA). PVS networks were assessed at MRI using PVS volume fraction, extracellular free water (FW), and diffusion tensor imaging analysis along the PVS (DTI-ALPS). One-way analysis of variance and Pearson correlation were used for analysis. Alterations in PVS indexes and cognitive performance after treatment were assessed in 15 participants with moderate OSA. Results A total of 105 participants (mean age, 33.4 years ± 8.9 [SD]; 80 males) and 50 HCs (mean age, 37.0 years ± 8.6; 33 males) were included. Higher mean PVS volume fraction was observed in participants with severe OSA (n = 23) than in patients with mild OSA (n = 36) (0.11 vs 0.10; P = .03). Participants with severe OSA exhibited higher mean FW index (0.11) than both HCs (0.10; P < .001) and patients with mild OSA (0.10; P = .003). All patient groups had lower DTI-ALPS than HCs (range, 1.5-1.9 vs 2.1; all P < .001). DTI-ALPS correlated with cognitive performance on the Stroop Color and Word Test (r range, -0.23 to -0.24; P value range, .003-.005). After treatment, PVS indexes changed (P value range, <.001 to .01) and cognitive performance improved (P value range, <.001 to .03). Conclusion Differences in PVS indexes were observed among participants with differing severities of OSA and HCs. Indexes correlated with measures of cognitive function, and changes in indexes and improvement in cognitive performance were observed after treatment in participants with moderate OSA. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Port in this issue.

Reduced coupling between global signal and cerebrospinal fluid inflow in patients with depressive disorder: A resting state functional MRI study.

BACKGROUND: Depressed patients often suffer from sleep disturbance, which has been recognized to be responsible for glymphatic dysfunction. The purpose of this study was to investigate the coupling strength of global blood­oxygen-level-dependent (gBOLD) signals and cerebrospinal fluid (CSF) inflow dynamics, which is a biomarker for glymphatic function, in depressed patients and to explore its potential relationship with sleep disturbance by using resting-state functional MRI. METHODS: A total of 138 depressed patients (112 females, age: 34.70 ± 13.11 years) and 84 healthy controls (29 females, age: 36.6 ± 11.75 years) participated in this study. The gBOLD-CSF coupling strength was calculated to evaluate glymphatic function. Sleep disturbance was evaluated using the insomnia items (item 4 for insomnia-early, item 5 for insomnia-middle, and item 6 for insomnia-late) of The 17-item Hamilton Depression Rating Scale for depressed patients, which was correlated with the gBOLD-CSF coupling strength. RESULTS: The depressed patients exhibited weaker gBOLD-CSF coupling relative to healthy controls (p = 0.022), possibly due to impairment of the glymphatic system. Moreover, the gBOLD-CSF coupling strength correlated with insomnia-middle (r = 0.097, p = 0.008) in depressed patients. Limitations This study is a cross-sectional study. CONCLUSION: Our findings shed light on the pathophysiology of depression, indicating that cerebral waste clearance system deficits are correlated with poor sleep quality in depressed patients.

Modulation Effects of the CEP128 Gene on Radiotherapy-Related Brain Injury: A Longitudinal Structural Study Using Multi-Parametric Brain MR Images.

BACKGROUND: The promoter variant rs17111237 in the CEP128 closely relates to radiotherapy (RT)-related brain necrosis in nasopharyngeal carcinoma (NPC) patients. PURPOSE: To explore RT-related dynamic alterations in brain morphology and their potential genetic mechanism, and to explore the modulatory effects of CEP128 genetic variants on RT-related brain morphological alterations in NPC patients. STUDY TYPE: Prospective, longitudinal. POPULATION: One hundred one patients with histopathologic ally-proven NPC (age 41.64 ± 9.63, 46 male), analyzed at baseline (pre-RT), 3-months post-RT and 6 months post-RT, and 19 sex-, age- and education-matched healthy controls. FIELD STRENGTH/SEQUENCE: 3D gradient echo brain volume (3D-BRAVO) and diffusion-weighted single-shot spin-echo echo-planar sequences at 3.0 T. ASSESSMENT: rs17111237 in CEP128 was detected by Sanger sequencing. Structural and diffusion images were processed with FreeSurfer and FSL. Morphometric similarity network (MSN) was constructed with nine cortical indices derived from structural and diffusion images. STATISTICAL TESTS: One-way ANOVA, chi-square test. Pearson's correlation analysis was conducted to measure the relationship between CEP128 gene-expression level in human brain and MSN alterations. Repeated analysis of variance performed to assess group differences in MSN and the modulatory effects of the CEP128 gene within patients. Significance level: P < 0.05, false-discovery rate correction. RESULTS: RT-related significant widespread MSN alterations were observed in the cortices of NPC patients. Notably, regional MSN alterations had a weak but significant negative correlation with the cortical pattern of CEP128 gene expression (r = -0.152). Furthermore, rs17111237 in the CEP128 had significant modulatory effects on the observed MSN alterations in NPC patients, with the modulatory effects being most obvious at 3 months post-RT. CONCLUSIONS: MSN has potential to serve as a sensitive biomarker to detect RT-related brain injury. Inter-brain regional and inter-patient variability of RT-related brain injuries may be attributed to the cortical expression of the CEP128 gene and the modulatory effects of the promoter variant rs17111237 in CEP128. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Connectome architecture modulates the gray matter atrophy in major depression disorder patients with diverse suicidal ideations.

Gray matter (GM) atrophy is well documented in patients with major depressive disorder (MDD), but its underlying mechanism remains unknown. This study aimed to examine the GM atrophy in MDD patients with diverse suicidal ideations (SIs) and to explore whether those alterations were driven by connections. GM volume was estimated in 163 patients with recurrent MDD (comprising 122 with SI [MDDSI] and 41 without SI [MDDNSI]) and 134 health controls (HCs). A two-sample t-test was used to identify GM volume abnormalities in MDD patients and their subgroups. Functional connectivity was computed between pairs of aberrant GM in both patients and HCs, which were further compared with the connectivity of random brain regions. A permutation test was performed to assess its significance. Propensity score matching (PSM) was further performed to validate the main results. Compared with HCs, the MDDNSI group exhibited GM atrophy in 24 regions, with the largest effect sizes found in the frontal and parietal lobes, while the MDDSI group exhibited more widespread GM atrophy involving 49 regions, with the largest effect sizes in the frontal lobe, parietal lobe, temporal lobe, and the limbic system. Furthermore, patients and HCs exhibited significantly increased functional connectivity between regions with GM atrophy compared with randomly selected regions (p < 0.05). PSM analysis presented similar results to the main analysis. MDD patients had diverse GM atrophy features according to their SI tendency. Moreover, connectome architecture modulates the GM atrophy in MDD patients, implying the possibility that connections drive these pathological changes.

Pretreatment MRI-Based Radiomics for Prediction of Rectal Cancer Outcome: A Discovery and Validation Study.

RATIONALE AND OBJECTIVES: Accurate prediction of local recurrence or distant metastasis is critical for developing individualized therapies for locally advanced rectal cancer (LARC) patients after standard therapy. This study aims to develop and validate a multiparameter MRI-based radiomics signature (RS) for prognostic prediction in LARC patients receiving neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME) and to explore the ability of RS for personalized survival risk stratification. MATERIALS AND METHODS: In this multi-center study, 454 patients who received nCRT and TME and completed 3 years of follow-up participated. RS was constructed for prognostic prediction based on features extracted from pretreatment multiparameter MRI in a training cohort (TC; n = 298), which was tested in an internal validation cohort (IVC; n = 75) and further validated in an independent external validation cohort (EVC; n = 81). Furthermore, the ability of RS for personalized survival risk stratification was explored using the Kaplan-Meier survival curves. RESULTS: The RS model showed satisfactory accuracy for prognostic prediction with AUCs of 0.83, 0.81 and 0.82 in the TC, IVC and EVC, respectively. In addition, RS helped to refine risk stratification for LARC patients on the basis of significantly different 3-year disease-free survival rates, independent of their pathological stage, pre-surgery CEA, and even treatment modality. CONCLUSIONS: The proposed RS can be used not only to predict local recurrence or distant metastasis but also to serve as an effective postoperative survival risk stratification tool for clinicians to facilitate decision-making for LARC patients receiving standard treatment.

Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer.

BACKGROUND: To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI). METHODS: In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87). CONCLUSIONS: Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.

Shared and specific neurobiology in bipolar disorder and unipolar disorder: Evidence based on the connectome gradient and a transcriptome-connectome association study.

BACKGROUND: Distinguishing bipolar disorder (BD) and unipolar disorder (UD) remains challenging. To identify the common and diagnosis-specific neuropathological alterations and their potential molecular mechanisms in patients with UD and BD (with a current depressive episode). METHODS: Resting-state functional magnetic resonance imaging was obtained from 279 participants (95 BD patients, 107 UD patients and 77 health controls). Connectome gradients analysis was performed to explore the shared and diagnosis-specific gradient alterations in BD and UD. The Allen Human Brain Atlas data was used to explore the potential gene mechanisms of the gradient alterations. RESULTS: BD and UD had shared hierarchical disorganisation, including downgrading and contraction from the unimodal sensory networks (vision and sensorimotor) to the transmodal cognitive networks (limbic, frontoparietal, dorsal attention, and default) (all P < 0.05, FDR corrected) in gradient 1 and gradient 2. The BD patients had specific connectome gradient dysfunction in the subcortical network. Moreover, the hierarchical disorganisation was closely correlated with profiles of gene expression specific to the neuroglial cells in the prefrontal cortex in BD and UD, while the most correlated gene ontology biological processes and function were concentrated in synaptic signalling, calcium ion binding, and transmembrane transporter activity. CONCLUSION: These findings reveal the shared and diagnosis-specific neurobiological mechanism underlying BD and UD patients, which advances our understanding of the neuromechanisms of these disorders.

Transcriptional expression of radiation-induced early cortical morphological alterations and its association with radiation necrosis in patients with nasopharyngeal carcinoma.

PURPOSE: To explore the effects of standard radiotherapy on cortical morphology and its potential transcriptional expression, and to determine the predictive power of cortical morphological measurement at the early stage for radiation necrosis (RN) occurrence within 3 years post-radiotherapy in patients with nasopharyngeal carcinoma (NPC). METHODS: 185 NPC patients participated. Pre-treatment and post-radiotherapy (1-3 months) structural MRI were collected longitudinally and prospectively. Multiple cortical morphological indices were compared between pre-treatment and post-radiotherapy. Brain-wide gene expression was used to assess the transcriptional profiles associated with radiation-induced cortical morphological changes. Machine learning was used to construct predictive models for RN with cortical morphological alterations at the early stage. RESULTS: Relative to pre-treatment, NPC patients exhibited a widespread reduction in cortical volume (CV) and cortical thickness (CT) post-radiotherapy (p < 0.001). Partial least squares regression analysis revealed that radiotherapy-related cortical atrophy was closely related to transcriptional profiles (p < 0.001), with the most correlated genes enriched in ATPase Na+/K+ transporting alpha-1 and alpha-3 polypeptide and respiratory electron transport chain. Furthermore, models constructed with cortical morphological features at 1-3 months post-radiotherapy had favorable predictive power for RN occurrence in NPC patients within 3-year follow-up, the area under the curve was 0.854 and 0.843 for CV and CT, respectively. CONCLUSIONS: NPC patients exhibited widespread cortical atrophy at 1-3 months post-radiotherapy, which was closely correlated with dysfunction of the ATPase Na+/K+ transporting alpha-1 and alpha-3 polypeptide and respiratory electron transport chain. Cortical morphology at 1-3 months post-radiotherapy may serve as an early biomarker for identifying RN.

Association Between MRI-Assessed Patterns of Connectome Gradient and Gene-Expression Profiles in Two Independent Patient Cohorts With Hepatitis B Virus-Related Cirrhosis.

BACKGROUND: Patients with hepatitis B virus-related cirrhosis (HBV-RC) exhibit progressive neurologic dysfunction from primary sensorimotor to high-order cognition, as their disease advances. However, the exact neurobiologic mechanisms and the potential association with gene-expression profiles are not fully understood. PURPOSE: To explore the hierarchical disorganization in the large-scale functional connectomes in HBV-RC patients and to investigate its potential underlying molecular basis. STUDY TYPE: Prospective. POPULATION: Fifty HBV-RC patients and 40 controls (Cohort 1) and 30 HBV-RC patients and 38 controls (Cohort 2). FIELD STRENGTH/SEQUENCE: Gradient-echo echo-planar and fast field echo sequences at 3.0 T (Cohort 1) and 1.5 T (Cohort 2). ASSESSMENT: Data were processed with Dpabi and the BrainSpace package. Gradient scores were evaluated from global to voxel level. Cognitive measurement and patients grouping were based on psychometric hepatic encephalopathy scores. The whole-brain microarray-based gene-expression data were obtained from the AIBS website. STATISTICAL TESTS: One-way ANOVA, chi-square test, two-sample t-test, Kruskal-Wallis test, Spearman's correlation coefficient (r), the gaussian random field correction, false discovery rate (FDR) correction and the Bonferroni correction. Significance level: P < 0.05. RESULTS: HBV-RC patients exhibited a robust and replicable connectome gradient dysfunction, which was significantly associated with the gene-expression profiles in both cohorts (r = 0.52 and r = 0.56, respectively). The most correlated genes were enriched in γ-aminobutyric acid (GABA) and GABA-related receptor genes (FDR q value <0.05). Moreover, the connectome gradient dysfunction at network level observed in HBV-RC patients correlated with their poor cognitive performance (Cohort 2: visual network, r = -0.56; subcortical network, r = 0.66; frontoparietal network, r = 0.51). DATA CONCLUSION: HBV-RC patients had hierarchical disorganization in the large-scale functional connectomes, which may underly their cognitive impairment. In addition, we showed the potential molecular mechanism of the connectome gradient dysfunction, which suggested the importance of GABA and GABA-related receptor genes. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Suicide risk stratification among major depressed patients based on a machine learning approach and whole-brain functional connectivity.

BACKGROUND: Suicide risk stratification and individual-level prediction among major depressive disorder (MDD) is important but unrecognized. Here, we construct models to detect suicidality in MDD using machine learning (ML) and whole-brain functional connectivity (FC). METHODS: A cross-sectional assessment was conducted on 200 subjects, including 126 MDD with high suicide risk (HSR; 73 patients with suicidal ideation [SI], 53 patients with suicidal attempts [SA]), 36 patients with low suicide risk (LSR) and 38 healthy controls (HCs). Whole-brain FC features were calculated, the least absolute shrinkage and selection operator (LASSO) method was used for feature selection. A support vector machine (SVM) was performed to build models to distinguish MDD from HCs, and for suicide risk stratification among MDD. Leave-one-out cross-validation (LOOCV) was performed for validation. RESULTS: The models constructed using SVM on whole-brain FC had powerful classification efficiency in screening MDD from HCs (accuracy = 88.50 %), and in suicide risk stratification among MDD patients (with accuracy = 84.56 % and 74.60 % in classifying patients with HSR or LSR, and SA or SI, respectively). Subsequent analysis demonstrated that intra-network dysconnectivity in the sensorimotor network and inter-network dysconnectivity between the default and dorsal attention network could characterize HSR and SA in MDD, separately. LIMITATIONS: This study was a single center cohort study without external validation. CONCLUSION: These findings indicate ML approaches are useful in suicide risk stratification among MDD based on whole-brain FC, which may help to identify individuals with different suicide risks in MDD and provide an individual-level prediction.

Modified oropharyngeal muscle training and scientific vocalization are effective in treating mild-to-moderate obstructive sleep apnea hypoventilation syndrome in adults.

BACKGROUND: Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS) has become a major public health challenge globally. Most patients have a concomitant voice disorder. The existing treatment methods have problems.Aims/Objectives: This study investigates the therapeutic effect of adding scientific vocalization to oropharyngeal muscle training on OSAHS patients. MATERIAL AND METHODS: A total of 30 patients were selected from September 2020 to October 2022. They underwent overnight polysomnography (PSG) and were identified as having mild to moderate obstructive sleep apnea hypoventilation syndrome. They were then chosen for a three-month period of modified oropharyngeal muscle training combined with scientific vocalization. RESULTS: Out of the 30 selected patients, 26 patients completed the training. Results showed a significant changes in multiple sleep-related indicators. he clinical outcomes were as follows: 7 cases were cured, 13 cases were effective, and 6 cases were ineffective. The overall effective rate was 76.92%. CONCLUSIONS AND SIGNIFICANCE: The combination of oropharyngeal muscle training and scientific vocalization for the treatment of mild to moderate OSAHS in adults significantly improves several measures used in the treatment of the condition. The method is easy to learn, effective, safe to use, and affordable. It provides more options for the treatment of OSAHS.

The efficacy and safety of conventional transcatheter arterial chemoembolization combined with PD-1 inhibitor and anti-angiogenesis tyrosine kinase inhibitor treatment for patients with unresectable hepatocellular carcinoma: a real-world comparative study.

Aim: We sought to evaluate the efficacy and safety of conventional transcatheter arterial chemoembolization (cTACE) sequentially combined with systemic treatment by programmed cell death protein 1 (PD-1) inhibitor and anti-angiogenesis tyrosine kinase inhibitor (Anti-angiogenesis TKI) in patients with unresectable hepatocellular carcinoma (HCC). Materials and methods: One hundred and forty-seven advanced HCC patients who received PD-1 inhibitors and TKIs as first-line systemic treatment between August 2019 and April 2021 were collected retrospectively. Fifty-four patients were finally included and divided into cTACE and no-cTACE groups, according to whether cTACE treatment was performed within 8 weeks before systemic treatment. The tumor objective response ratio (ORR), progression-free survival (PFS), overall survival (OS), and adverse events (AEs) were compared between the groups. Significant factors affecting PFS and OS were determined by Cox regression. Results: Thirty-one patients received cTACE followed by systemic treatment and 23 patients received systemic treatment only. The ORRs of the cTACE group were 48.4% (after two cycles of systemic treatment) and 51.6% (after four cycles of systemic treatment), while those of the no-cTACE group were only 17.4% and 21.7%. cTACE patients also had a longer median PFS (11.70 vs. 4.00 months, P = 0.031) and median OS (19.80 vs. 11.6 months, P = 0.006) than no-cTACE patients. Regression analyses indicated that cTACE therapy and Eastern Cooperative Oncology Group performance status were independent risk factors for PFS and OS. AEs by type were similar between the cTACE and no-cTACE groups, except for liver function injury, which was more common among cTACE patients. Fourteen patients suffered with grade 1-2 of rash in 21 patients with objective response, while only 10 patients suffered with rash in 33 patients without objective response, the adjusted hazard ratio (HR) was 4.382 (1.297-14.803). Conclusions: The combination of cTACE and PD-1 inhibitors and anti-angiogenesis TKIs as therapy significantly improved markers of treatment efficacy, including ORR, PFS, and OS, in unresectable HCC patients, while no more serious AEs recorded in this population compared to those receiving systemic treatment alone. Skin rash might be a predict factor to the efficacy of PD-1 inhibitors and TKI treatment.

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning.

Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.

Divergent white matter changes in patients with nasopharyngeal carcinoma post-radiotherapy with different outcomes: a potential biomarker for prediction of radiation necrosis.

OBJECTIVES: To investigate the effects of standard radiotherapy on temporal white matter (WM) and its relationship with radiation necrosis (RN) in patients with nasopharyngeal carcinoma (NPC), and to determine the predictive value of WM volume alterations at the early stage for RN occurrence at the late-delay stage. METHODS: Seventy-four treatment-naive NPC patients treated with standard radiotherapy were longitudinally followed up for 36 months. Structural MRIs were collected at multiple time points during the first year post-radiotherapy. Longitudinal structural images were processed using FreeSurfer. Linear mixed models were used to delineate divergent trajectories of temporal WM changes between patients who developed RN and who did not. Four machine learning methods were used to construct predictive models for RN with temporal WM volume alterations at early-stage. RESULTS: The superior temporal gyrus (STG) had divergent atrophy trajectories in NPC patients with different outcomes (RN vs. NRN) post-radiotherapy. Patients with RN showed more rapid atrophy than those with NRN. A predictive model constructed with temporal WM volume alterations at early-stage post-radiotherapy had good performance for RN; the areas under the curve (AUC) were 0.879 and 0.806 at 1-3 months and 6 months post-radiotherapy, respectively. Moreover, the predictive model constructed with absolute temporal volume at 1-3 months post-radiotherapy also presented good performance; the AUC was 0.842, which was verified by another independent dataset (AUC = 0.773). CONCLUSIONS: NPC patients with RN had more sharp atrophy in the STG than those with NRN. Temporal WM volume at early-stage post-radiotherapy may serve as an in vivo biomarker to identify and predict RN occurrence. KEY POINTS: • The STG had divergent atrophy trajectories in NPC patients with different outcomes (RN vs. NRN) post-radiotherapy. • Although both groups exhibited time-dependent atrophy in the STG, the patients with RN showed a more rapid volume decrease than those with NRN. • Temporal WM volume alteration (or absolute volume) at the early stage could predict RN occurrence at the late-delay stage after radiotherapy.

Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma.

BACKGROUND: To investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes. METHODS: 36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose-volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined. RESULTS: Significant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a "decrease-increase/recovery" pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D20cc and V20 to right temporal lobe outperformed in this model. CONCLUSION: Our findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.

Progressive Brain Structural Impairment Assessed via Network and Causal Analysis in Patients With Hepatitis B Virus-Related Cirrhosis.

Objectives: This research amid to elucidate the disease stage-specific spatial patterns and the probable sequences of gray matter (GM) deterioration as well as the causal relationship among structural network components in hepatitis B virus-related cirrhosis (HBV-RC) patients. Methods: Totally 30 HBV-RC patients and 38 healthy controls (HC) were recruited for this study. High-resolution T1-weighted magnetic resonance imaging and psychometric hepatic encephalopathy score (PHES) were evaluated in all participants. Voxel-based morphometry (VBM), structural covariance network (SCN), and causal SCN (CaSCN) were applied to identify the disease stage-specific GM abnormalities in morphology and network, as well as their causal relationship. Results: Compared to HC (0.443 ± 0.073 cm3), the thalamus swelled significantly in the no minimal hepatic encephalopathy (NMHE) stage (0.607 ± 0.154 cm3, p <0.05, corrected) and further progressed and expanded to the bilateral basal ganglia, the cortices, and the cerebellum in the MHE stage (p < 0.05, corrected). Furthermore, the thalamus swelling had a causal effect on other parts of cortex-basal ganglia-thalamus circuits (p < 0.05, corrected), which was negatively correlated with cognitive performance (r = -0.422, p < 0.05). Moreover, the thalamus-related SCN also displayed progressive deterioration as the disease advanced in HBV-RC patients (p < 0.05, corrected). Conclusion: Progressive deterioration of GM morphology and SCN exists in HBV-RC patients during advanced disease, displaying thalamus-related causal effects. These findings indicate that bilateral thalamus morphology as well as the thalamus-related network may serve as an in vivo biomarker for monitoring the progression of the disease in HBV-RC patients.

Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features.

OBJECTIVE: To construct a predictive model of short-term response and overall survival for transcatheter arterial chemoembolization (TACE) treatment in hepatocellular carcinoma (HCC) patients based on non-contrast computed tomography (NC-CT) radiomics and clinical features. METHODS: Ninety-four HCC patients who underwent CT scanning 1 week before the first TACE treatment were retrospectively recruited and divided randomly into a training group (n = 47) and a validation group (n = 47). NC-CT radiomics data were extracted using MaZda software, and the compound model was calculated from radiomics and clinical features by logistic regression. The performance of the different models was compared by examining the area under the receiver operating characteristic curve (AUC). The prediction of prognosis was evaluated using survival analysis. RESULTS: Thirty NC-CT radiomic features were extracted and analyzed. The compound model was formed using four NC-CT run-length matrix (RLM) features and general image features, which included the maximum diameter (cm) of the tumor and the number of tumors (n). The AUCs of the model for TACE response were 0.840 and 0.815, whereas the AUCs of the six-and-twelve grade were 0.754 and 0.750 in the training and validation groups, respectively. HCC patients were divided into two groups using the cutoff value of the model: a group in which the TACE-response led to good survival and a group in which TACE-nonresponse caused poor prognosis. CONCLUSION: Radiomic features from NC-CT predicted TACE-response. The compound model generated by NC-CT radiomics and clinical features is effective and directly predicts TACE-response and overall survival. The model may be used repeatedly and is easy to operate.

Divergent effects of irradiation on brain cortical morphology in patients with nasopharyngeal carcinoma: one-year follow-up study using structural magnetic resonance imaging.

BACKGROUND: Increasing evidence indicates that radiotherapy (RT)-induced brain cortical deficits may play a critical role in developing radiation encephalopathy in patients with nasopharyngeal carcinoma (NPC). However, the evolutional processes of RT-induced cortical injury have not been sufficiently investigated. This study investigates RT-induced effects on cortical morphology using longitudinal structural magnetic resonance imaging (MRI) in NPC patients. METHODS: Using MRI-based morphometry with surface-based measures, we evaluated the longitudinal alterations of cortical volume (CV), cortical thickness (CT), and cortical surface area (CSA) in 104 NPC patients at pre-RT (n=104), within 3 months post-RT (n=92), 6 months post-RT (n=71), and 9-12 months post-RT (n=52). Twenty healthy controls were also evaluated in parallel. Linear mixed models were used to investigate the trajectories of RT-related changes in cortical brain morphology and its association with irradiation dose, with healthy controls data being used to construct a normal age-related benchmark. The level of statistical significance was set at P<0.05, corrected for multiple comparisons. RESULTS: The results showed that RT-related longitudinal alterations in cortical morphology underwent two diverse patterns during the first year of follow up in NPC patients. The temporal cortices (including the bilateral superior temporal gyrus, middle temporal gyrus, temporal pole, parahippocampal and fusiform gyrus, and the right inferior temporal and right transverse temporal gyrus), the basal occipital cortices (the right lingual gyrus and lateral occipital gyrus), and the basal frontal cortices (the right lateral orbitofrontal gyrus) showed time-dependent attenuation in cortical morphology indices. Furthermore, these effects on multiple cortices were dose-dependent, suggesting they were RT-associated. In contrast, in the left rostral middle frontal gyrus, there was a time-dependent increase in CT. CONCLUSIONS: Our preliminary findings revealed divergent effects of irradiation on cortical brain morphology. These results contribute to a more comprehensive understanding of the underlying neural mechanisms of irradiation-related neurotoxic effects on cortical brain morphology and will help guide the investigation of critically neuroprotective strategies.

Progressive Disruption of Dynamic Functional Network Connectivity in Patients With Hepatitis B Virus-related cirrhosis.

BACKGROUND: The diseased-related dynamic functional network connectivity (dFNC) disruption and its relationship with cognitive impairment in hepatitis B virus-related cirrhosis (HBV-RC) patients with minimal hepatic encephalopathy (MHE) and no MHE (NMHE) remain unknown. This knowledge would help identify MHE pathophysiology and monitor disease progression in HBV-RC patients. PURPOSE: To investigate the dFNC in patients with NMHE and MHE and the relationship between dFNC indices with the psychometric hepatic encephalopathy score (PHES). STUDY TYPE: Prospective. POPULATION: Thirty HBV-RC patients (including 17 NMHE and 13 MHE) and 38 healthy controls (HC). FIELD STRENGTH/SEQUENCE: A 1.5 T MRI and gradient-echo echo-planar imaging and fast field echo three-dimensional T1-weighted imaging. ASSESSMENT: The independent components, dFNC matrix and dFNC indices (mean dwell times [DT], number of states, number of transitions, and fraction time in each state), were obtained through GIFT package. Cognitive measurement and patients grouping were based on PHES tests. STATISTICAL TESTS: One-way ANOVA, chi-square test, two-sample t-test, Kruskal-Wallis test, spearman's correlation analysis and the false discovery rate. Significance level: P < 0.05. RESULTS: Compared to HC (21.1 ± 4.02), the DT of state 1 decreased in NMHE (9.0 ± 3.04, P = 0.062, 95% confidence interval [CI] is -0.65 to 24.88) and significantly in MHE stage (1.2 ± 1.01) and was significantly correlated with PHES (r = 0.5) for all patients. The DT of state 2 increased gradually in NMHE (75.2 ± 13.10, P = 0.052, 95% CI, -54.23 to 0.28) and significantly in MHE stage (94.6 ± 15.61) when compared to HC (48.2 ± 6.97). Moreover, the connectivity between cognitive control network (CCN) and visual network (VIS) in state 1 (0.7 ± 0.79) and between default mode (DMN) and VIS in state 2 (-0.2 ± 0.29) decreased significantly in MHE when compared to HC (0.1 ± 0.68 for CCN-VIS in state 1 and 0.1 ± 0.17 for DMN-VIS for state 2). DATA CONCLUSION: dFNC exhibited progressive impairment as the disease advances in patients with HBV-RC. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.