Association between body mass index and glymphatic function using diffusion tensor image-along the perivascular space (DTI-ALPS) in patients with Parkinson's disease.

Background: Obesity is considered a risk factor for the development of several neurodegenerative diseases, including Parkinson's disease (PD). Recent studies have revealed that glymphatic function is compromised in PD patients. This study aims to investigate the impact of different body mass index (BMI) statuses on glymphatic system function in PD patients using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method. Methods: This study used a cross-sectional study design. A total of 145 PD patients were retrospectively enrolled in Parkinson's Progression Markers Initiative (PPMI) from 2010-2013. Eligibility criteria included diagnosis of PD based on PPMI criteria. Diffusion tensor image (DTI) scans (diffusion gradient =64, b-value =1,000 s/mm2, slice thickness =2 mm) were acquired, and the analysis along the perivascular space (ALPS) index of each subject was calculated. The patient cohort was categorized into three groups based on BMI: normal weight (N=49), overweight (N=69), and obese (N=27). The difference in ALPS index among groups was performed by one-way analysis of variance (ANOVA). Partial correlation analysis was used to observe the relationship between ALPS index, BMI status, and demographics. Spearman's rank correlation coefficient and multivariable linear regression analyses were used to identify factors associated with ALPS index. Results: PD patients with higher BMI exhibited a reduced ALPS index (normal weight > overweight > obese), and the ALPS index for patients with obesity was statistically significantly lower than that for patients with normal weight (P<0.001). After adjusting for age, sex, years of education, handedness, and disease duration, a significant negative correlation between the ALPS index and BMI was observed in the PD patients (R=-0.275, P<0.001). Furthermore, a negative correlation between the ALPS index and the severity of motor symptoms was identified in the subgroup of overweight (R=-0.318, P=0.01), rather than in the normal weight and obese groups. Conclusions: High BMI has a negative impact on the glymphatic function in PD patients, suggesting that weight control may have clinical relevance in the management of PD patients.

Radiogenomics for predicting microsatellite instability status and PD-L1 expression with machine learning in endometrial cancers: A multicenter study.

Purpose: To evaluate the effectiveness of machine learning model based on magnetic resonance imaging (MRI) in identifying microsatellite instability (MSI) status and PD-L1 expression in endometrial cancer (EC). Methods: This retrospective study included 82 EC patients from 2 independent centers. Radiomics features from the intratumoral and peritumoral regions, obtained from four conventional MRI sequences (T2-weighted images; contrast-enhanced T1-weighted images; diffusion-weighted images; apparent diffusion coefficient), were combined with clinicopathologic characteristics to develop machine learning model for predicting MSI status and PD-L1 expression. 60 patients from center 1 were used as the training set for model construction, while 22 patients from center 2 were used as an external validation set for model evaluation. Results: For predicting MSI status, the clinicopathologic model, radscore model, and combination model achieved area under the curves (AUCs) of 0.728, 0.833, and 0.889 in the training set, respectively, and 0.595, 0.790, and 0.848 in the validation set, respectively. For predicting PD-L1 expression, the clinicopathologic model, radscore model, and combination model achieved AUCs of 0.648, 0.814, and 0.834 in the training set, respectively, and 0.660, 0.708, and 0.764 in the validation set, respectively. Calibration curve analysis and decision curve analysis demonstrated good calibration and clinical utility of the combination model. Conclusion: The machine learning model incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for predicting MSI status and PD-L1 expression in EC. This approach may contribute to precision medicine for EC patients.

Development of a Clinicopathological-Radiomics Model for Predicting Progression and Recurrence in Meningioma Patients.

RATIONALE AND OBJECTIVES: Tumor progression and recurrence（P/R）after surgical resection are common in meningioma patients and can indicate poor prognosis. This study aimed to investigate the values of clinicopathological information and preoperative magnetic resonance imaging (MRI) radiomics in predicting P/R and progression-free survival (PFS) in meningioma patients. METHODS AND MATERIALS: A total of 169 patients with pathologically confirmed meningioma were included in this study, 54 of whom experienced P/R. Clinicopathological information, including age, gender, Simpson grading, World Health Organization (WHO) grading, Ki-67 index, and radiotherapy history, as well as preoperative traditional radiographic findings and radiomics features for each MRI modality (T1-weighted, T2-weighted, and enhanced T1-weighted images) were initially extracted. After feature selection, the optimal performance was estimated among the models established using different feature sets. Finally, Cox survival analysis was further used to predict PFS. RESULTS: Ki-67 index, Simpson grading, WHO grading, and radiotherapy history were found to be independent predictors for P/R in the multivariate regression analysis. This clinicopathological model had an area under the curve (AUC) of 0.865 and 0.817 in the training and testing sets, respectively. The performance of the combined radiomics model reached 0.85 and 0.84, respectively. A clinicopathological-radiomics model was then established, which significantly improved the prediction of meningioma P/R (AUC = 0.93 and 0.88, respectively). Finally, the risk ratio was estimated for each selected feature, and the C-index of 0.749 was obtained. CONCLUSION: Radiomics signatures of preoperative MRI have the ability to predict meningioma at the risk of P/R. By integrating clinicopathological information, the best performance was achieved.

Increased iron deposition in nucleus accumbens associated with disease progression and chronicity in migraine.

BACKGROUND: Migraine is one of the world's most prevalent and disabling diseases. Despite huge advances in neuroimaging research, more valuable neuroimaging markers are still urgently needed to provide important insights into the brain mechanisms that underlie migraine symptoms. We therefore aim to investigate the regional iron deposition in subcortical nuclei of migraineurs as compared to controls and its association with migraine-related pathophysiological assessments. METHODS: A total of 200 migraineurs (56 chronic migraine [CM], 144 episodic migraine [EM]) and 41 matched controls were recruited. All subjects underwent MRI and clinical variables including frequency/duration of migraine, intensity of migraine, 6-item Headache Impact Test (HIT-6), Migraine Disability Assessment (MIDAS), and Pittsburgh Sleep Quality Index (PSQI) were recorded. Quantitative susceptibility mapping was employed to quantify the regional iron content in subcortical regions. Associations between clinical variables and regional iron deposition were studied as well. RESULTS: Increased iron deposition in the putamen, caudate, and nucleus accumbens (NAC) was observed in migraineurs more than controls. Meanwhile, patients with CM had a significantly higher volume of iron deposits compared to EM in multiple subcortical nuclei, especially in NAC. Volume of iron in NAC can be used to distinguish patients with CM from EM with a sensitivity of 85.45% and specificity of 71.53%. As the most valuable neuroimaging markers in all of the subcortical nuclei, higher iron deposition in NAC was significantly associated with disease progression, and higher HIT-6, MIDAS, and PSQI. CONCLUSIONS: These findings provide evidence that iron deposition in NAC may be a biomarker for migraine chronicity and migraine-related dysfunctions, thus may help to understand the underlying vascular and neural mechanisms of migraine. TRIAL REGISTRATION: ClinicalTrials.gov, number NCT04939922.

Genetic impacts on nigral iron deposition in Parkinson's disease: A preliminary quantitative susceptibility mapping study.

BACKGROUND: Dysfunction of iron metabolism, especially in substantia nigra (SN), is widely acknowledged in Parkinson's disease (PD), but the genetic influence on iron deposition remains largely unknown. Thus, in this study, we aimed to investigate potential genetic impacts on iron deposition in PD. METHODS: Seventy-four subjects, including 38 patients with PD and 36 age-matched normal controls, participated in this study. Imaging genetic association analysis was used to identify the specific influence of single nucleotide polymorphism (SNP) on iron-related quantitative traits (QT). Genetic effects on iron deposition at the disease level, SNP level, and their interactive effect were highlighted. RESULTS: Four strong SNP-QT associations were detected: rs602201-susceptibility of bilateral SN, rs198440-susceptibility of left SN, and rs7895403-susceptibility of left caudate head. Detailed analyses showed that: (1) significant iron deposition was exclusively found in bilateral SN in PD; (2) altered polymorphisms of the A allele/A- genotype of rs602201 and G allele/G- genotype of rs198440 and rs7895403 were more frequently observed in PD; (3) for rs602201, among all subjects, A- genotype carriers showed significantly increased iron content than TT genotype in bilateral SN; for rs198440 and rs7895403, G- carriers showed increased iron content than AA genotype in left SN and left caudate head, respectively; and (4) rs602201 exhibited significant SNP-by-disease interaction in bilateral SN. CONCLUSIONS: This study shows that rs602201 and rs198440 have a stimulative impact on nigral iron deposition in PD, which provides improved understanding of iron-related pathogenesis in PD, and specifically, that vulnerability to iron deposition in SN is genetic-based.

White Matter Tract Injury by MRI in CADASIL Patients is Associated With Iron Accumulation.

BACKGROUND: Widespread white matter (WM) injury is a hallmark feature of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). However, controversies about the mechanism of WM tract injury exist persistently. Excessive iron accumulation, frequently reported in CADASIL patients, might cause WM tract injury. PURPOSE: To test the association between iron accumulation and WM tract injury in CADASIL patients. STUDY TYPE: Retrospective. POPULATION: A total of 35 CADASIL patients (age = 50.4 ± 6.4, 62.9% female) and 48 healthy controls (age = 55.7 ± 8.0, 68.8% female). FIELD STRENGTH/SEQUENCE: Diffusion-weighted spin-echo echo-planar sequence; enhanced susceptibility-weighted angiography (ESWAN) gradient echo sequence on a 3 T scanner. ASSESSMENT: The phase images acquired by ESWAN were used to calculate quantitative susceptibility mapping (QSM). Iron accumulation was evaluated in deep gray matters using QSM. WM tract injury was quantified by diffusion metrics based on WM major tracts skeleton. We compared iron deposition between groups and analyzed the correlation between WM tract injury and iron deposition in regions showing significant differences from healthy controls. Exploratory analysis was carried out to investigate whether WM tract injury mediated the relationship between iron deposition and cognitive impairment evaluated by Mini-Mental State Examination (MMSE). STATISTICAL TESTS: General linear model (GLM), partial correlation, stepwise linear regression and mediation analysis were used. The threshold of statistical significance was set as p < 0.05. RESULTS: Compared with healthy controls, CADASIL patients had significantly increased iron deposition in the caudate and putamen. Aberrant iron deposition in these two regions was significantly associated with decreased WM fractional anisotropy (FA) (caudate, r = -0.373； putamen, r = - 0.421), and increased radial diffusivity (RD) (caudate, r = 0.372; putamen, r = 0.386). Furthermore, WM tract injury mediated the relationship between iron deposition and cognitive impairment. DATA CONCLUSION: Patients with CADASIL show increased iron deposition in the caudate and putamen that is correlated to WM tract injury, which may in turn mediate the association with cognitive impairment. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Nigral Iron Deposition Influences Disease Severity by Modulating the Effect of Parkinson's Disease on Brain Networks.

BACKGROUND: In Parkinson's disease (PD), excessive iron deposition in the substantia nigra may exacerbate α-synuclein aggregation, facilitating the degeneration of dopaminergic neurons and their neural projection. OBJECTIVE: To investigate the interaction effect between nigral iron deposition and PD status on brain networks. METHODS: Eighty-five PD patients and 140 normal controls (NC) were included. Network function and nigral iron were measured using multi-modality magnetic resonance imaging. According to the median of nigral magnetic susceptibility of NC (0.095 ppm), PD and NC were respectively divided into high and low nigral iron group. The main and interaction effects were investigated by mixed effect analysis. RESULTS: The main effect of disease was observed in basal ganglia network (BGN) and visual network (VN). The interaction effect between nigral iron and PD status was observed in left inferior frontal gyrus and left insular lobe in BGN, as well as right middle occipital gyrus, right superior temporal gyrus, and bilateral cuneus in VN. Furthermore, multiple mediation analysis revealed that the functional connectivity of interaction effect clusters in BGN and medial VN partially mediated the relationship between nigral iron and Unified Parkinson's Disease Rating Scale II score. CONCLUSION: Our study demonstrates an interaction of nigral iron deposition and PD status on brain networks, that is, nigral iron deposition is associated with the change of brain network configuration exclusively when in PD. We identified a potential causal mediation pathway for iron to affect disease severity that was mediated by both BGN dysfunction and VN hyperfunction in PD.

Altered brain iron depositions from aging to Parkinson's disease and Alzheimer's disease: A quantitative susceptibility mapping study.

Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e.g., Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0.01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0.01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0.01), and significant iron deposition in SN compared with old adults (p < 0.01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0.443, Ppermutation = 0.001) and cognition score in MCI and AD (Rmean = 0.243, Ppermutation = 0.037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.

The protective role of cigarette smoking against Parkinson's disease via moderation of the interaction between iron deposition in the nigrostriatal pathway and clinical symptoms.

Background: Although cigarette smoking is a risk factor for multiple disorders, it has long been thought to protect against Parkinson's disease (PD). Quantitative susceptibility mapping (QSM) is a novel magnetic resonance imaging (MRI)-based technique for assessing iron accumulation in vivo that has been widely applied in PD studies. This study aimed to investigate how cigarette smoking affects clinical performance of PD using quantified iron deposition as a proxy for PD pathology. Methods: In this observational study, we enrolled 35 male PD patients and 47 male healthy controls (HCs) and divided them into four groups. We performed an enhanced T2 star-weighted angiography (ESWAN) MRI sequence to measure the iron content of the nuclei within the nigrostriatal pathway. With the age and total intracranial volume (TIV) controlled as covariates, we performed inter-group comparisons of QSM values and moderation analyses for PD patients using smoking status and the smoking index (SI), respectively, as moderator variables. Results: The 2-way multivariate analysis of covariance (MANCOVA) results showed higher QSM values in the left red nucleus (P=0.024) in PD patients compared with those in HCs, and in the bilateral globi pallidi [left/right (L/R): P=0.009/0.003], substantia nigra pars compacta (SNc; L/R: P=0.001/0.037), and right substantia nigra pars reticulata (SNr; P=0.002) in non-smokers compared with smokers, with no marked interaction effect between PD and smoking status observed when applying the Bonferroni adjustment for multiple comparisons. Using cigarette smoking status and the SI as separate moderator variables, the moderation was shown up by a significant interaction effect in a disordinal and double-edged form. In our results, smoking-moderated protection for PD movement deficits emerged when PD was progressed. Among the affected deep brain nuclei, the nuclei most moderated by the impact of cigarette smoking on the interaction between brain iron and PD symptoms were the thalamus [smoking status associated with the Unified Parkinson's Disease Rating Scale (UPDRS) total score, P=0.04 (L); rigidity, P=0.03 (L); SI associated with UPDRS-III, P (L/R) =0.049/0.0497; rigidity, P (L/R) =0.01/0.02; bradykinesia, P (L/R) =0.048/0.04], the right red nucleus (SI associated with rigidity, P=0.04; bradykinesia, P=0.02), and the left SNc [smoking status associated with the Hoehn and Yahr (H&Y) stage, P=0.01]. Conclusions: This was the first study investigating the impacts of current cigarette smoking on PD using quantified iron deposition. Our study confirmed the protective role of cigarette smoking against PD, consistent with the findings of previous studies. Furthermore, neuroprotection was present only when the PD pathology had progressed to a certain extent. In the interaction between iron deposition and clinical PD symptoms, our findings suggest that the thalamus, red nucleus, and SNc are likely to be the most affected nuclei moderated by cigarette smoking.

Interactions between cigarette smoking and cognitive status on functional connectivity of the cortico-striatal circuits in individuals without dementia: A resting-state functional MRI study.

AIMS: Cigarette smoking is a modifiable risk factor for Alzheimer's disease (AD), and controlling risk factors may curb the progression of AD. However, the underlying neural mechanisms of the effects of smoking on cognition remain largely unclear. Therefore, we aimed to explore the interaction effects of smoking × cognitive status on cortico-striatal circuits, which play a crucial role in addiction and cognition, in individuals without dementia. METHODS: We enrolled 304 cognitively normal (CN) non-smokers, 44 CN smokers, 130 mild cognitive impairment (MCI) non-smokers, and 33 MCI smokers. The mixed-effect analysis was performed to explore the interaction effects between smoking and cognitive status (CN vs. MCI) based on functional connectivity (FC) of the striatal subregions (caudate, putamen, and nucleus accumbens [NAc]). RESULTS: The significant interaction effects of smoking × cognitive status on FC of the striatal subregions were detected in the left inferior parietal lobule (IPL), bilateral cuneus, and bilateral anterior cingulate cortex (ACC). Specifically, increased FC of right caudate to left IPL was found in CN smokers compared with non-smokers. The MCI smokers showed decreased FC of right caudate to left IPL and of right putamen to bilateral cuneus and increased FC of bilateral NAc to bilateral ACC compared with CN smokers and MCI non-smokers. Furthermore, a positive correlation between FC of the NAc to ACC with language and memory was detected in MCI smokers. CONCLUSIONS: Cigarette smoking could affect the function of cortico-striatal circuits in patients with MCI. Our findings suggest that quitting smoking in the prodromal stage of AD may have the potential to prevent disease progression.

Association between cigarette smoking and Parkinson's disease: a neuroimaging study.

Background: Mounting evidence has revealed an inverse association between cigarette smoking and the risk of Parkinson's disease (PD). Meanwhile, cigarette smoking has been found to be associated with cognitive impairment in PD patients. However, the neural mechanisms of the association between cigarette smoking and PD are not fully understood. Objective: The aim of this study is to explore the neural mechanisms of the association between cigarette smoking and PD. Methods: A total of 129 PD patients and 69 controls were recruited from the Parkinson's Progression Markers Initiative (PPMI) cohort, including 39 PD patients with regular smoking history (PD-S), 90 PD patients without regular smoking history (PD-NS), 26 healthy controls with regular smoking history (HC-S), and 43 healthy controls without regular smoking history (HC-NS). Striatal dopamine transporter (DAT) binding and gray matter (GM) volume of the whole brain were compared among the four groups. Results: PD patients showed significantly reduced striatal DAT binding compared with healthy controls, and HC-S showed significantly reduced striatal DAT binding compared with HC-NS. Moreover, smoking and PD showed a significant interaction effect in the left medial prefrontal cortex (mPFC). PD-S showed reduced GM volume in the left mPFC compared with PD-NS. Conclusion: The degeneration of dopaminergic neurons in PD results in a substantial reduction of the DAT and dopamine levels. Nicotine may act as a stimulant to inhibit the action of striatal DAT, increasing dopamine levels in the synaptic gap. The inverse alteration of dopamine levels between PD and nicotine addiction may be the reason for the inverse association between smoking and the risk of PD. In addition, the mPFC atrophy in PD-S may be associated with cognitive impairment.

Effects of Cigarette Smoking on Resting-State Functional Connectivity of the Nucleus Basalis of Meynert in Mild Cognitive Impairment.

Background: Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer's disease (AD) and has a high risk of progression to AD. Cigarette smoking is one of the important modifiable risk factors in AD progression. Cholinergic dysfunction, especially the nucleus basalis of Meynert (NBM), is the converging target connecting smoking and AD. However, how cigarette smoking affects NBM connectivity in MCI remains unclear. Objective: This study aimed to evaluate the interaction effects of condition (non-smoking vs. smoking) and diagnosis [cognitively normal (CN) vs. MCI] based on the resting-state functional connectivity (rsFC) of the NBM. Methods: After propensity score matching, we included 86 non-smoking CN, 44 smoking CN, 62 non-smoking MCI, and 32 smoking MCI. All subjects underwent structural and functional magnetic resonance imaging scans and neuropsychological tests. The seed-based rsFC of the NBM with the whole-brain voxel was calculated. Furthermore, the mixed effect analysis was performed to explore the interaction effects between condition and diagnosis on rsFC of the NBM. Results: The interaction effects of condition × diagnosis on rsFC of the NBM were observed in the bilateral prefrontal cortex (PFC), bilateral supplementary motor area (SMA), and right precuneus/middle occipital gyrus (MOG). Specifically, the smoking CN showed decreased rsFC between left NBM and PFC and increased rsFC between left NBM and SMA compared with non-smoking CN and smoking MCI. The smoking MCI showed reduced rsFC between right NBM and precuneus/MOG compared with non-smoking MCI. Additionally, rsFC between the NBM and SMA showed a significant negative correlation with Wechsler Memory Scale-Logical Memory (WMS-LM) immediate recall in smoking CN (r = -0.321, p = 0.041). Conclusion: Our findings indicate that chronic nicotine exposure through smoking may lead to functional connectivity disruption between the NBM and precuneus in MCI patients. The distinct alteration patterns on NBM connectivity in CN smokers and MCI smokers suggest that cigarette smoking has different influences on normal and impaired cognition.

High-resolution computed tomography findings independently predict epidermal growth factor receptor mutation status in ground-glass nodular lung adenocarcinoma.

BACKGROUND: For lung adenocarcinoma with epidermal growth factor receptor (EGFR) gene mutation, small molecule tyrosine kinase inhibitors are more effective. Some patients could not obtain enough histological specimens for EGFR gene mutation detection. Specific imaging features can predict EGFR mutation status to a certain extent. AIM: To assess the associations of EGFR mutations with high-resolution computerized tomography (HRCT) features in ground-glass nodular lung adenocarcinoma. METHODS: This study retrospectively assessed patients with ground-glass nodular lung adenocarcinoma diagnosed between January 2011 and March 2017. EGFR gene mutations in exons 18-21 were detected. The patients were classified into mutant EGFR and wild-type groups, and general data and HRCT image characteristics were assessed. RESULTS: Among 98 patients, 31 (31.6%) and 67 (68.4%) had mutated and wild-type EGFR in exons 18-21, respectively. Gender, age, smoking history, location of lesions, morphology, edges, borders, pleural indentations, and associations of nodules with bronchus and blood vessels were comparable in both groups (all P > 0.05). Patients with mutant EGFR had larger nodules than those with the wild-type (17.19 ± 6.79 and 14.37 ± 6.30 mm, respectively; P = 0.047). Meanwhile, the vacuole/honeycomb sign was more frequent in the mutant EGFR group (P = 0.011). The logistic regression prediction model included the combination of nodule size and vacuole/honeycomb sign (OR = 1.120, 95%CI: 1.023-1.227, P = 0.014) revealed a sensitivity of 83.9%, a specificity of 52.2% and an AUC of 0.698 (95%CI: 0.589-0.806; P = 0.002). CONCLUSION: Nodule size and vacuole/honeycomb features could independently predict EGFR mutation status in ground-glass nodular lung adenocarcinoma.

A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features.

BACKGROUND: Brain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI. METHODS: One hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed. RESULTS: Traditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set). CONCLUSIONS: This study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI.

Substantia nigra iron affects functional connectivity networks modifying working memory performance in younger adults.

Brain iron affects working memory (WM) but the impact of iron content in deep grey matter nuclei on WM networks is unknown. We aimed to test whether deep grey matter nuclei iron concentration can affect resting-state functional connectivity (rsFC) within brain networks modifying WM performance. An N-back WM paradigm was applied in a hundred healthy younger adults. The participants then underwent a resting-state functional magnetic resonance imaging (fMRI) for brain network analysis and quantitative susceptibility mapping (QSM) imaging for assessment of deep grey matter nuclei iron concentration. Higher substantia nigra (SN) iron concentration was associated with lower rsFC between SN and brain regions of the temporal/frontal lobe but with better WM performance after controlling for age, gender and education. A follow-up mediation analysis also indicated that functional connectivity may mediate the link between SN iron and WM performance. Our results suggest that high SN iron concentration may affect communication between the SN and temporal/frontal lobe and is associated with strengthened WM performance in younger adults.

Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics.

OBJECTIVES: To explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics. MATERIALS AND METHODS: Thirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feature selection methods, including variance thresholding, recursive feature elimination, and the elastic net, were used to analyze the radiomic features. Based on the results, related visually accessible features of the tumors were further evaluated. RESULTS: Patients with DMG-M were younger than those with diffuse midline gliomas with H3 K27M wild (DMG-W) (median, 25.5 and 48 years old, respectively; p=0.005). Principal component analysis showed that there were obvious overlaps in the first two principal components for both DMG-M and DMG-W tumors. The feature selection results showed that few features from T2-weighted images (T2WI) were useful for differentiating DMG-M and DMG-W tumors. Thereafter, four visually accessible features related to T2WI were further extracted and analyzed. Among these features, only cystic formation showed a significant difference between the two types of tumors (OR=7.800, 95% CI 1.476-41.214, p=0.024). CONCLUSIONS: DMGs with and without the H3 K27M mutation shared similar MRI characteristics. T2W sequences may be valuable, and cystic formation a useful MRI biomarker, for diagnosing brain DMG-M.

Cortical degeneration detected by neurite orientation dispersion and density imaging in chronic lacunar infarcts.

BACKGROUND: Although lacunar infarcts are focal lesions, they may also have more widespread effects. A reduction in cortical thickness in the remote cortex after lacunar infarcts has been detected by structural imaging; however, its underlying microstructural changes are yet to be elucidated. This study aimed to investigate the effects of lacunar infarcts on the microstructural abnormalities associated with cortical thickness reduction in the remote cortex. METHODS: Thirty-seven patients with chronic lacunar infarcts were included. Brain structural magnetic resonance images (MRIs) and diffusion tensor images were acquired. We constructed the white matter tracts connecting with the lacunar infarcts and identified the connected cortical area based on a standard brain atlas warped into the subject space. Cortical thickness and microstructural neurite orientation dispersion and density imaging (NODDI) metrics of the ipsilesional and contralesional cortices were compared, and correlations between cortical thickness and NODDI metrics were also investigated. RESULTS: We found decreased cortical thickness and reduced neurite orientation dispersion index (ODI) in the ipsilesional cortex (2.47 vs. 2.50 mm, P=0.008; 0.451 vs. 0.456, P=0.035, respectively). In patients with precentral gyrus involvement (n=23), we found that ODI in the ipsilesional cortex was correlated with cortical thickness (r=0.437, P=0.037), and ODI in the contralesional cortex was also correlated with contralesional cortical thickness (r=0.440, P=0.036). CONCLUSIONS: NODDI metrics could reflect cortical microstructural changes following lacunar infarcts. The correlation between decreased ODI and reduced cortical thickness suggests that dendrites' loss might contribute to lacunar infarct-related cortical atrophy.

Serum Ceruloplasmin Depletion is Associated With Magnetic Resonance Evidence of Widespread Accumulation of Brain Iron in Parkinson's Disease.

BACKGROUND: Excessive iron accumulation is one of the main pathogeneses of Parkinson's disease (PD). Ceruloplasmin plays an important role in keeping the iron homoeostasis. PURPOSE: To explore the association between serum ceruloplasmin depletion and subcortical iron distribution in PD. STUDY TYPE: Prospective. POPULATION: One hundred and twenty-one normal controls, 34 PD patients with low serum ceruloplasmin (PD-LC), and 28 patients with normal serum ceruloplasmin (PD-NC). SEQUENCE: Enhanced susceptibility-weighted angiography (ESWAN) on a 3 T scanner. ASSESSMENT: Quantitative susceptibility mapping was employed to quantify the regional iron content by using a semi-automatic method. Serum ceruloplasmin concentration was measured from peripheral blood sample. Clinical assessments were conducted by a neurologist. STATISTICAL TESTS: General linear model was used to compare the intergroup difference of region iron distribution among groups, and the statistics was adjusted by Bonferroni method (P < 0.01). Partial correlation analysis was used to detect the association between regional iron distribution and serum ceruloplasmin concentration (P < 0.05). RESULTS: Compared with normal controls, significant iron accumulation in substantia nigra, putamen, and red nucleus was observed in PD-LC, while the only region showing significant iron accumulation was SN in PD-NC. Between PD-NC and PD-LC, the iron accumulation in putamen remained significantly different, which had a negative correlation with serum ceruloplasmin in whole PD patients (r = -0.338, P = 0.008). DATA CONCLUSION: Nigral iron accumulation characterizes PD patients without significant association with serum ceruloplasmin. Differentially, when PD patients appear with reduced serum ceruloplasmin, more widespread iron accumulation would be expected with additionally involving putamen and red nucleus. All these findings provide insightful evidence for the abnormal iron metabolism behind the ceruloplasmin depletion in PD. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: 2.

Correlation between quantitative perfusion histogram parameters of DCE-MRI and PTEN, P-Akt and m-TOR in different pathological types of lung cancer.

BACKGROUND: To explore if the quantitative perfusion histogram parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) correlates with the expression of PTEN, P-Akt and m-TOR protein in lung cancer. METHODS: Thirty-three patients with 33 lesions who had been diagnosed with lung cancer were enrolled in this study. They were divided into three groups: squamous cell carcinoma (SCC, 15 cases), adenocarcinoma (AC, 12 cases) and small cell lung cancer (SCLC, 6 cases). Preoperative imaging (conventional imaging and DCE-MRI) was performed on all patients. The Exchange model was used to measure the phar- macokinetic parameters, including Ktrans, Vp, Kep, Ve and Fp, and then the histogram parameters meanvalue, skewness, kurtosis, uniformity, energy, entropy, quantile of above five parameters were analyzed. The expression of PTEN, P-Akt and m-TOR were assessed by immunohistochemistry. Spearman correlation analysis was used to compare the correlation between the quantitative perfusion histogram parameters and the expression of PTEN, P-Akt and m-TOR in different pathological subtypes of lung cancer. RESULTS: The expression of m-TOR (P = 0.013) and P-Akt (P = 0.002) in AC was significantly higher than those in SCC. Vp (uniformity) in SCC group, Ktrans (uniformity), Ve (kurtosis, Q10, Q25) in AC group, Fp (skewness, kurtosis, energy), Ve (Q75, Q90, Q95) in SCLC group was positively correlated with PTEN, and Fp (entropy) in the SCLC group was negatively correlated with PTEN (P < 0.05); Kep (Q5, Q10) in the SCLC group was positively correlated with P-Akt, and Kep (energy) in the SCLC group was negatively correlated with P-Akt (P < 0.05); Kep (Q5) in SCC group and Vp (meanvalue, Q75, Q90, Q95) in SCLC group was positively correlated with m-TOR, and Ve (meanvalue) in SCC group was negatively correlated with m-TOR (P < 0.05). CONCLUSIONS: The quantitative perfusion histogram parameters of DCE-MRI was correlated with the expression of PTEN, P-Akt and m-TOR in different pathological types of lung cancer, which may be used to indirectly evaluate the activation status of PI3K/Akt/mTOR signal pathway gene in lung cancer, and provide important reference for clinical treatment.

Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study.

PURPOSE: The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes. METHODS: We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size (according to average tumor radius) to explore the influence of peritumor of different sizes and types of tumors. Finally, we visualized the deep learning and radiomic models to observe the influence of the peritumor in three datasets. RESULTS: The performance of intra-peritumors are better than intratumors alone in three datasets. Specifically, the comparisons of area under receiver operating characteristic curve in the testing set between intra-peritumoral and intratumoral models are: 0.908 vs 0.873 (P value: 0.037) in gastrointestinal stromal tumor datasets, 0.796 vs 0.756 (P value: 0.188) in laryngeal carcinoma datasets and 0.660 vs 0.579 (P value: 0.431) in nasopharyngeal carcinoma datasets. Furthermore, for gastrointestinal stromal tumor datasets, deep learning is more stable to learn peritumors with both fixed and adaptive size than radiomics. For laryngeal carcinoma datasets, the intra-peritumoral radiomic model could make model performance more balanced. For nasopharyngeal carcinoma datasets, radiomics is also more suitable for modeling peritumors than deep learning. The size of the peritumor is critical in this task, and only the performance of 1.5 mm-4.5 mm peritumors is stable. CONCLUSIONS: Our results indicate that peritumors have additional predictive value in three tumor datasets through deep learning or radiomics. The definitions of the peritumoral region and artificial intelligence method also have great influence on the performance of the peritumor.