Optimization of Clustering and Trajectory for Minimizing Age of Information in Unmanned Aerial Vehicle-Assisted Mobile Edge Computing Network.

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV's flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN-collection point (CP) associations and CP locations for the parameter ε are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized ε, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study.

PURPOSE: Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios. METHODS: Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations. RESULTS: 64 patients (female, 21; male, 43; age, 47.0 ± 13.7 years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05). CONCLUSIONS: The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.

Corrigendum: Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.

[This corrects the article DOI: 10.3389/fonc.2021.699789.].

Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.

OBJECTIVE: To identify optimal machine-learning methods for the radiomics-based differentiation of gliosarcoma (GSM) from glioblastoma (GBM). MATERIALS AND METHODS: This retrospective study analyzed cerebral magnetic resonance imaging (MRI) data of 83 patients with pathologically diagnosed GSM (58 men, 25 women; mean age, 50.5 ± 12.9 years; range, 16-77 years) and 100 patients with GBM (58 men, 42 women; mean age, 53.4 ± 14.1 years; range, 12-77 years) and divided them into a training and validation set randomly. Radiomics features were extracted from the tumor mass and peritumoral edema. Three feature selection and classification methods were evaluated in terms of their performance in distinguishing GSM and GBM: the least absolute shrinkage and selection operator (LASSO), Relief, and Random Forest (RF); and adaboost classifier (Ada), support vector machine (SVM), and RF; respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of each method were analyzed. RESULTS: Based on tumor mass features, the selection method LASSO + classifier SVM was found to feature the highest AUC (0.85) and ACC (0.77) in the validation set, followed by Relief + RF (AUC = 0.84, ACC = 0.72) and LASSO + RF (AUC = 0.82, ACC = 0.75). Based on peritumoral edema features, Relief + SVM was found to have the highest AUC (0.78) and ACC (0.73) in the validation set. Regardless of the method, tumor mass features significantly outperformed peritumoral edema features in the differentiation of GSM from GBM (P < 0.05). Furthermore, the sensitivity, specificity, and accuracy of the best radiomics model were superior to those obtained by the neuroradiologists. CONCLUSION: Our radiomics study identified the selection method LASSO combined with the classifier SVM as the optimal method for differentiating GSM from GBM based on tumor mass features.

Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model.

BACKGROUND: Intracranial solitary fibrous tumor(SFT)/hemangiopericytoma (HPC) is an aggressive malignant tumor originating from the intracranial vasculature. Angiomatous meningioma (AM) is a benign tumor with a good prognosis. The imaging manifestations of the two are very similar. Thus, novel noninvasive diagnostic method is urgently needed in clinical practice. Texture analysis and model building through machine learning may have good prospects. AIM: To evaluate whether a 3D-MRI texture feature model could be used to differentiate malignant intracranial SFT/HPC from AM. METHOD: A total of 97 patients with SFT/HPC and 95 with AM were included in this study. Patients from each group were randomly divided into the train (70%) and test (30%) sets. ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI using ITK-SNAP software. The segmented image was imported into the AK software for texture feature extraction, and the 3D ROI signal intensity histograms of T1WI, T2WI, and contrasted T1WI were automatically obtained along with all the parameters. Modeling was performed using the language R. Confusion matrix was used to analyze the accuracy of the model. ROC curve was constructed to assess the grading ability of the logistic regression model. RESULTS: After Lasso dimension reduction, 5, 9, and 7 texture features were extracted from T1WI, T2WI, and contrasted T1WI, respectively; additional 8 texture features were extracted from the combined sequence for modeling. The ROC analyses on four models resulted in an area under the curve (AUC) of 0.885 (sensitivity 76.1%, specificity 87.9%) for T1WI model, 0.918 (73.1%, 95.5%) for T2WI model, 0.815 (55.2%, 93.9%) for contrasted T1WI model, and 0.959 (92.5%, 84.8%) for the combined sequence model and were enough to correctly distinguish the two groups in 71.2%, 81.4%, 69.5%, and 83.1% of cases in test set, respectively. CONCLUSIONS: The radiological model based on texture features could be used to differentiate SFT/HPC from AM.

Clinical and Diffusion Tensor Imaging to Evaluate Falls, Balance and Gait Dysfunction in Leukoaraiosis: an Observational, Prospective Cohort Study.

OBJECTIVE: To assess the correlation between leukoaraiosis (LA) and falls, to determine the risk factors for falls in patients with LA, and to detect specific white matter tracts are associated with the falls by using the diffusion tensor magnetic resonance imaging (DTI) screen. METHODS: For the elderly patients with LA, we collected demographic information and scores for the Tinetti Balance and Gait Scale, Berg Balance Scale, Timed up-and-go test, and Cognitive, Emotional, Sleep-related Scale. All the patients underwent DTI scanning and were followed up for 1 year. RESULTS: Ninety-four individuals were prospectively enrolled. After multivariable analyses, age, history of falls in the past year, antidepressants usage, and LA-Fazekas grade were reported to be risk factors for falls. In patients with severe LA, the fall incidence was higher than in those with mild LA. Tract-Based Spatial Statistics showed that fractional anisotropy values of the corpus callosum, cingulate gyrus, anterior limb of internal capsule, cerebral peduncle, anterior corona, and fronto-occipital fasciculus were significantly reduced in the patients who fell. The body of the corpus callosum and anterior corona radiate were significantly related to balance and gait function. CONCLUSIONS: Our findings indicated that age, history of falls in the past year, antidepressants usage, and LA-Fazekas grade were risk factors for falls in elderly patients with LA. Leukoaraiosis was relevant for falls, but LA severity had a threshold effect with falls. The loss of integrity of some white matter tracts might influence balance and gait function. The DTI had preeminent clinical application prospects for identifying fall risk in patients with LA.

Abnormal Interactions of the Salience Network, Central Executive Network, and Default-Mode Network in Patients With Different Cognitive Impairment Loads Caused by Leukoaraiosis.

Leukoaraiosis (LA) is associated with cognitive impairment in the older people which can be demonstrated in functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI). This study is to explore the FC changes in LA patients with different cognitive status by three network models. Fifty-three patients with LA were divided into three groups: the normal cognition (LA-NC; n = 14, six males), mild cognitive impairment (LA-MCI; n = 27, 13 males), and vascular dementia (LA-VD; n = 12, six males), according to the Mini Mental State Exam (MMSE) and Clinical Dementia Rating (CDR). The three groups and 30 matched healthy controls (HCs; 11 males) underwent rs-fMRI. The data of rs-fMRI were analyzed by independent components analysis (ICA) and region of interest (ROI) analysis by the REST toolbox. Then the FC was respectively analyzed by the default-mode network (DMN), salience networks (SNs) and the central executive network (CEN) with their results compared among the different groups. For inter-brain network analysis, there were negative FC between the SN and DMN in LA groups, and the FC decreased when compared with HC group. While there were enhanced inter-brain network FC between the SN and CEN as well as within the SN. The FC in patients with LA can be detected by different network models of rs-fMRI. The multi-model analysis is helpful for the further understanding of the cognitive changes in those patients.

MRI texture analysis based on 3D tumor measurement reflects the IDH1 mutations in gliomas - A preliminary study.

OBJECTIVE: To evaluate the differentiation efficiency of texture analysis of T1WI, T2WI and contrasted-enhanced T1WI MRI sequences in gliomas with and without IDH1 mutation based on entire tumor region. MATERIALS AND METHODS: A total of 42 patients with histopathologically confirmed gliomas, including 21 patients carrying IDH1 mutation (IDH1mutation group) and 21 with wild-type IDH1 (IDH1wt group) were included in this study. The preoperative MRI and clinical data were collected. The regions of interest (ROIs) covering the entire tumor and edema were manually delineated on axial slices using O.K. (Omni Kinetics, GE Healthcare, China) software; and the histogram and GLCM features based on T1WI, T2WI and contrasted-enhanced T1WI sequences were automatically generated. RESULTS: Based on contrasted-enhanced T1WI features, the inertia resulted as the best feature for diagnosis, with the AUC of 0.844. Furthermore, the AUC for gliomas prediction with IDH1mutation was 0.800 for cluster prominence. IDH1-mutation was differentiated on T2WI with the highest AUC of 0.848, which corresponded to GLCM Entropy. After modeling, the accuracy of the contrasted-enhanced T1WI, T1WI, and T2WI features model was 0.952, 0.857, and 0.738, respectively. The AUC of Joint VariableT1WI+C for predicting IDH1mutation was 0.984, while the AUC of Joint VariableT1WI for predicting the same mutation was 0.927. The diagnostic efficiency of Joint VariableT2WI was also desirable. CONCLUSION: MRI texture analysis could be used as a new noninvasive method for identification of gliomas with IDH1 mutation. The present results show that the Joint Variable derived from conventional MR imaging histogram and GLCM features is suitable for precise detection of IDH1-mutated gliomas.

Radiologic Features of Primary Intracranial Myxomas from the Skull Base: 14 Case Reports and Literature Review.

BACKGROUND: Primary intracranial myxomas (PIMs) are extremely rare benign tumors that arise from the skull base. The aim of this study was to characterize the radiologic manifestation of PIMs in a series of 14 cases. METHODS: We reviewed the imaging and clinical data of 14 patients with pathologically proven PIMs. Assessed features of lesions include shape, margin, bony destruction, attenuation and/or signal intensity, and pattern of enhancement. RESULTS: Extremely high-density foci indicating calcification or bony debris within the tumors were observed in 5 cases on computed tomography images. On magnetic resonance images, the tumors demonstrated heterogeneous hypointensity on T1-weighted images (T1WI) and hyperintensity on T2-weighted images (T2WI). A honeycomb pattern on enhanced T1WIs was observed in 63.6% (7/11) of the cases. CONCLUSIONS: Radiologic findings of PIMs include calcified foci or bone debris on computed tomography, heterogeneous hypointensity on T1WI and predominantly hyperintensity on T2WI, and honeycomb appearance on enhanced T1WI.

Iron deposition in substantia nigra: abnormal iron metabolism, neuroinflammatory mechanism and clinical relevance.

Parkinson disease (PD) is associated with multiple factors, including iron, which is demonstrated to deposit excessively in PD brains. We detected iron deposition by susceptibility weighted image (SWI) and measured the levels of iron metabolism-related proteins and inflammatory factors in cerebrospinal fluid (CSF) and serum of PD patients and control subjects. Clinical symptoms of PD were evaluated by series of rating scales. Relationships among above factors were analyzed. Results showed that corrected phase (CP) value of substantia nigra (SN) was significantly decreased in PD group compared to control group, hence, SN was the main region with excessive iron deposition. In PD group, ferritin was significantly elevated in CSF and reduced in serum compared to control group, and levels of ferritin in CSF and serum were both significantly and positively correlated with CP value of SN, thus, abnormal iron metabolism in central and peripheral systems was associated with iron deposition. CP value of SN in PD group was significantly and negatively correlated with interleukin-1ß level in CSF, so interleukin-1ß might be a neuroinflammatory factor produced by excessive iron in SN. Iron deposition in SN was significantly correlated with motor symptoms and part of non-motor symptoms of PD.

Radiologic features of primary intracranial ectopic germinomas: Case reports and literature review.

RATIONALE: Germinomas are sensitive to radiation therapy and chemotherapy; therefore, correct imaging diagnosis is crucial for them. However, the imaging findings of germinomas originating from off-midline regions displayed different patterns from those originating from midline areas. PATIENT CONCERNS: The objective of this study is to describe the radiologic features of primary ectopic germinoma. We reviewed the MR and CT findings of 12 patients with histologically proven off-midline ectopic germinomas with off-midline locations. INTERVENTIONS: All of these patients underwent conventional MR images and 3 of them underwent diffusion images. Additional CT images were available in 3 patients. Analysis was focused on the shape and entity of tumors in images, signs of hemiatrophy, and the involvement of fibers in diffusion images. OUTCOMES: Well-defined (8/12) and ill-defined margin masses (4/12) were identified according to the shape of the mass. Multicystic masses were seen in 11 of the 12 patients. The solid component of the tumors had a high density (3/3) with calcifications (2/3) on CT images, iso- to hypointensity in T2WI (11/12) and restricted diffusion on apparent diffusion coefficient (ADC) maps (3/3). Hemiatrophy was observed in 5 cases and progressive hemiatrophy was observed in 1 case. Other signs included mild peritumoral edema (10/12), and hydrocephalus (7/12). Additionally, infiltration of the corticospinal tract (CST) was identified on diffusion tensor imaging (DTI) (2/2). LESSONS: The results indicate that multicysitic entities and hypointensities in solid components on T2WI and hemiatrophy are the imaging features of ectopic germinomas. DTI has potential for assessing CST involvement.

Alteration of spontaneous neuronal activity in young adults with non-clinical depressive symptoms.

Non-clinical depressive symptoms (nCDSs) are highly prevalent in young adults and may be associated with the risk of developing full-fledged depressive disorders. However, the neural basis underlying nCDSs remains unknown. To explore the alteration of spontaneous brain activity in individuals with nCDSs compared with healthy controls (HCs), we investigated resting-state brain activity using the amplitude of low-frequency fluctuations (ALFF) in subjects with nCDSs (n=17) and HCs (n=20). All subjects were drawn from a sample of 1105 college students participating in a survey assessing depressive symptoms. We determined that nCDSs can lead to reduced ALFF in the right ventral lateral prefrontal cortex (VLPFC) and right dorsolateral prefrontal cortex (DLPFC) and to increased ALFF in the left fusiform, left posterior cerebellum, right cuneus, left inferior parietal lobule, right supramarginal gyrus and bilateral precuneus. In addition, with respect to Beck Depression Inventory (BDI) scores and ALFF values in subjects with nCDSs, a positive correlation was discovered in the right DLPFC, while a negative correlation was identified in left posterior cerebellum and bilateral precuneus after correction. These results indicate that nCDSs are characterized by altered spontaneous activity in several important functional regions. We suggest that altered ALFFs in the right DLPFC, left posterior cerebellum and bilateral precuneus may be biomarkers that are related to the pathophysiology of nCDSs in young adults.

Altered resting-state connectivity in college students with nonclinical depressive symptoms.

BACKGROUND: The underlying brain basis of nonclinical depressive symptoms (nCDSs) is largely unknown. Recently, the seed-based functional connectivity (FC) approach for analyzing resting-state fMRI (rs-fMRI) data has been increasingly used to explore the neural basis of depressive disorders. Other than common seed-based FC method using an a priori seed region, we conducted FC analysis based on regions with altered spontaneous activity revealed by the fractional amplitude of low-frequency fluctuations (fALFF) approach. The aim of the present study was to provide novel insight in the underlying mechanism of nCDSs in college students. METHODOLOGY/PRINCIPAL FINDINGS: A total number of 1105 college students were recruited to participant in a survey for assessing depressive symptoms. Subsequently, 17 individuals with nCDSs and 20 healthy controls (HCs) were enrolled to perform MR studies. Alternations of fALFF were identified in the right superior parietal lobule (SPL) and left lingual gyrus, both of which were used as ROIs for further FC analysis. With right SPL, compare with HCs, subjects with nCDSs showed reduced FCs in the bilateral dorsal lateral prefrontal cortex (DLPFC), left inferior frontal gurus (IFG), left premotor cortex (PMC), DMN network [i.e., bilateral precuneus, posterior cingulate cortex (PCC), right supramarginal gyrus (SMG), right parahippocampal gyrus (PHG), bilateral inferior temporal gurus (ITG)] and left cerebellum posterior lobe (CPL). In addition, increased FCs were observed between the left lingual gyrus and right fusiform gyrus as well as in the left precuneus. CONCLUSION/SIGNIFICANCE: Our results indicate the abnormalities of spontaneous activity in the right SPL and left lingual gyrus and their corresponding dysfunction of the brain circuits might be related to the pathophysiology of nCDSs.

In vivo measurement of oxygenation changes after stroke using susceptibility weighted imaging filtered phase data.

BACKGROUND AND PURPOSE: Cerebral blood oxygenation level is critical for following the evolution of stroke patients. The purpose of this study was to investigate the feasibility of measuring changes in blood oxygen levels for patients with acute stroke using SWI and to compare these changes with the patient's recovery over time. MATERIALS AND METHODS: A total 30 MRI scans was performed on 10 acute ischemic stroke patients. Every patient was followed at three time points: less than 24 hours; 2-3 weeks after stroke and 2 months after stroke. Both MRI scan and NIH stroke scale (NIHSS) were acquired for each patient at all three time points. Oxygen saturation changes were derived from phase values differences (Δφ) measured over 10 veins from each hemisphere for all 10 patients over 3 time points. The correlation of oxygen saturation and NIHSS was further evaluated. RESULTS: The stroke affected side of the brain showed moderate (r = -0.62) to strong (r = -0.70) correlation between the oxygenation change and NIHSS change. The oxygen saturation change from the normal side of the brain had essentially no association with recovery (r = -0.02 and-0.31). The results suggest that increases in oxygen saturation correspond to improved outcome and reductions in oxygen saturation correspond to worse outcome. CONCLUSION: High resolution SWI provided a novel method to measure changes in oxygenation change of the human brain in vivo. By using the phase values from the veins, both spatial and temporal information can be found that relates to patient outcome post stroke.

In ovo monitoring of smooth muscle fiber development in the chick embryo: diffusion tensor imaging with histologic correlation.

BACKGROUND: Magnetic resonance imaging is a noninvasive method of evaluating embryonic development. Magnetic resonance diffusion tensor imaging, which is based on the measuring the directional diffusivity of water molecules, is an established method of evaluating tissue structure. Prolonged imaging times have precluded the use of embryonic diffusion tensor imaging due to motion artifact. Using temperature-based motion suppression, we aimed to investigate whether diffusion tensor imaging can be used to monitor embryonic smooth muscle development in ovo, and to determine the correlation between histologically-derived muscle fiber fraction, day of incubation and diffusion tensor imaging fractional anisotropy values and length of tracked fibers. METHODOLOGY/PRINCIPAL FINDINGS: From a set of 82 normally developing fertile chicken eggs, 5 eggs were randomly chosen each day from incubation days 5 to 18 and cooled using a dual-cooling technique prior to and during magnetic resonance imaging at 3.0 Tesla. Smooth muscle fibers of the gizzard were tracked using region of interests placed over the gizzard. Following imaging, the egg was cracked and the embryo was fixated and sectioned, and a micrograph most closely corresponding to the acquired magnetic resonance image was made. Smooth muscle fiber fraction was determined using an automated computer algorithm. CONCLUSIONS/SIGNIFICANCE: We show that diffusion tensor images of smooth muscle within the embryonic gizzard can be acquired in ovo from incubation day 11 through hatching. Length of tracked fibers and day of incubation were found to have statistical significance (p<0.05) by multiple linear regression correlation with histologic specimens of sacrificed embryos from day 11 of incubation through hatching. The morphologic pattern of development in our histologic specimens corresponds to the development of embryonic gizzard as reported in the literature. These results suggest that diffusion tensor imaging can provide a noninvasive method of evaluating in ovo development of smooth muscle tissue.

[The relationship among CT appearances and dynamic enhancement and microvessel density of peripheral lung cancer].

BACKGROUND: To investigate the relationship among the CT appearances, the dynamic CT enhancement and the microvessel density (MVD) of peripheral lung cancer. METHODS: Thirty-three patients with peripheral lung cancer proved by surgery and pathology underwent enhancement dynamic CT scan before operation, including 14 squamous cell carcinoma and 19 adenocarcinoma. The MVD was measured in resected tumor specimens with immunohistochemical method of LSAB. RESULTS: The MVD value of adenocarcinoma was significantly higher than that of squamous cell carcinoma (63.4±11.9 versus 50.2±16.3, P < 0.05). The MVD values were higher in junction zone and interstitial areas than those in parenchymal areas, necrotic zones and scar areas of tumors. There were significant relationships among the MVD value and diameter of tumor, lobulation sign, vessel convergence sign, pleural retraction sign and lymph node metastasis (P < 0.05 ). The mean enhanced CT values was (43.4±11.8) HU in adenocarcinomas, and (34.6±10.7) HU in squamous cell carcinomas (P < 0.05). The CT values of both adenocarcinoma and squamous cell carcinoma had positive correlations to their corresponding MVD values (r=0.719, P < 0.01;r=0.819, P < 0.01). CONCLUSIONS: The CT appearances and the enhanced CT values of peripheral lung carcinomas are closely related to their MVD values, which might be an indicator to identify the histological classification and to predict the malignant degree of tumor.