Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data.

Biomedical multi-modality data (also named multi-omics data) refer to data that span different types and derive from multiple sources in clinical practices (e.g. gene sequences, proteomics and histopathological images), which can provide comprehensive perspectives for cancers and generally improve the performance of survival models. However, the performance improvement of multi-modality survival models may be hindered by two key issues as follows: (1) how to learn and fuse modality-sharable and modality-individual representations from multi-modality data; (2) how to explore the potential risk-aware characteristics in each risk subgroup, which is beneficial to risk stratification and prognosis evaluation. Additionally, learning-based survival models generally refer to numerous hyper-parameters, which requires time-consuming parameter setting and might result in a suboptimal solution. In this paper, we propose an adaptive risk-aware sharable and individual subspace learning method for cancer survival analysis. The proposed method jointly learns sharable and individual subspaces from multi-modality data, whereas two auxiliary terms (i.e. intra-modality complementarity and inter-modality incoherence) are developed to preserve the complementary and distinctive properties of each modality. Moreover, it equips with a grouping co-expression constraint for obtaining risk-aware representation and preserving local consistency. Furthermore, an adaptive-weighted strategy is employed to efficiently estimate crucial parameters during the training stage. Experimental results on three public datasets demonstrate the superiority of our proposed model.

nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species.

Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.

OBJECTIVES: To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center. METHODS: This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations. RESULT: On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%). CONCLUSION: The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists. CLINICAL RELEVANCE STATEMENT: Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients. KEY POINTS: • The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.

An automated method for assessing condyle head changes in patients with skeletal class II malocclusion based on Cone-beam CT images.

OBJECTIVES: Currently, there is no reliable automated measurement method to study the changes in the condylar process after orthognathic surgery. Therefore, this study proposes an automated method to measure condylar changes in patients with skeletal class II malocclusion following surgical-orthodontic treatment. METHODS: Cone-beam CT (CBCT) scans from 48 patients were segmented using the nnU-Net network for automated maxillary and mandibular delineation. Regions unaffected by orthognathic surgery were selectively cropped. Automated registration yielded condylar displacement and volume calculations, each repeated three times for precision. Logistic regression and linear regression were used to analyse the correlation between condylar position changes at different time points. RESULTS: The Dice score for the automated segmentation of the condyle was 0.971. The intraclass correlation coefficients (ICCs) for all repeated measurements ranged from 0.93 to 1.00. The results of the automated measurement showed that 83.33% of patients exhibited condylar resorption occurring six months or more after surgery. Logistic regression and linear regression indicated a positive correlation between counterclockwise rotation in the pitch plane and condylar resorption (P < .01). And a positive correlation between the rotational angles in both three planes and changes in the condylar volume at six months after surgery (P ≤ .04). CONCLUSIONS: This study's automated method for measuring condylar changes shows excellent repeatability. Skeletal class II malocclusion patients may experience condylar resorption after bimaxillary orthognathic surgery, and this is correlated with counterclockwise rotation in the sagittal plane. ADVANCES IN KNOWLEDGE: This study proposes an innovative multi-step registration method based on CBCT, and establishes an automated approach for quantitatively measuring condyle changes post-orthognathic surgery. This method opens up new possibilities for studying condylar morphology.

Hepatoprotective effect of protocatechuic acid against type 2 diabetes-induced liver injury.

CONTEXT: Protocatechuic acid (PCA) has a protective effect on alcoholic liver injury, but the role of PCA in type 2 diabetes-induced liver injury is not well known. OBJECTIVES: This study explores the therapeutic effect and potential mechanism of PCA on type 2 diabetes-induced liver injury. MATERIALS AND METHODS: An insulin resistance/type 2 diabetic (IR/D) model was established by high-fat diet for 4 weeks + streptozotocin (35 mg/kg; i.p) in male Wistar rats pretreated with or without PCA (15 or 30 mg/kg for 6 d). RESULTS: PCA at 15 and 30 mg/kg significantly upregulated the levels of body weight (BW; 230.2, 257.8 g), high density lipids (22.68, 34.78 mg/dL), glutathione (10.24, 16.21 nmol/mg), superoxide dismutase (21.62, 29.34 U/mg), glucagon-like peptide-1, glucose transporter-4, Wnt1, and ß-catenin, while downregulating those of liver weight (LW; 9.4, 6.7 g), BW/LW (4.1, 2.6%), serum glucose (165, 120 mg/dL), serum insulin (13.46, 8.67 µIU/mL), homeostatic model assessment of insulin resistance (5.48, 2.57), total cholesterol (68.52, 54.31 mg/dL), triglycerides (72.15, 59.64 mg/dL), low density lipids (42.18, 30.71), aspartate aminotransferase (54.34 and 38.68 U/L), alanine aminotransferase (42.87, 29.98 U/L), alkaline phosphatase (210.16, 126.47 U/L), malondialdehyde (16.52, 10.35), pro-inflammatory markers (tumor necrosis factor α (TNF-α (149.67, 120.33 pg/mg)) , IL-6 (89.79, 73.69 pg/mg) and IL-1ß (49.67, 38.73 pg/mg)), nuclear factor kappa B (NF-κB), and interleukin-1ß, and ameliorated the abnormal pathological changes in IR/D rats. DISCUSSION AND CONCLUSION: PCA mitigates the IR, lipid accumulation, oxidative stress, and inflammation in liver tissues of IR/D rats by modulating the NF-κB and Wnt1/ß-catenin pathways.

Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.

Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.

Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume.

BACKGROUND: Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images. This study aims to develop an MRI segmentation model of intercondylar fossa based on deep learning to automatically measure notch volume, and explore its correlation with ACL injury. METHODS: The MRI data of 363 subjects (311 males and 52 females) with ACL injuries incurred during non-contact sports and 232 subjects (147 males and 85 females) with intact ACL were retrospectively analyzed. Each layer of intercondylar fossa was manually traced by radiologists on axial MR images. Notch volume was then calculated. We constructed an automatic segmentation system based on the architecture of Res-UNet for intercondylar fossa and used dice similarity coefficient (DSC) to compare the performance of segmentation systems by different networks. Unpaired t-test was performed to determine differences in notch volume between ACL-injured and intact groups, and between males and females. RESULTS: The DSCs of intercondylar fossa based on different networks were all more than 0.90, and Res-UNet showed the best performance. The notch volume was significantly lower in the ACL-injured group than in the control group (6.12 ± 1.34 cm3 vs. 6.95 ± 1.75 cm3, P < 0.001). Females had lower notch volume than males (5.41 ± 1.30 cm3 vs. 6.76 ± 1.51 cm3, P < 0.001). Males and females who had ACL injuries had smaller notch than those with intact ACL (p < 0.001 and p < 0.005). Men had larger notches than women, regardless of the ACL injuries (p < 0.001). CONCLUSION: Using a deep neural network to segment intercondylar fossa automatically provides a technical support for the clinical prediction and prevention of ACL injury and re-injury after surgery.

Physiological system analysis of the kidney by high-temporal-resolution T2∗ monitoring of an oxygenation step response.

PURPOSE: Examine the feasibility of characterizing the regulation of renal oxygenation using high-temporal-resolution monitoring of the T2∗ response to a step-like oxygenation stimulus. METHODS: For T2∗ mapping, multi-echo gradient-echo imaging was used (temporal resolution = 9 seconds). A step-like renal oxygenation challenge was applied involving sequential exposure to hyperoxia (100% O2 ), hypoxia (10% O2 + 90% N2 ), and hyperoxia (100% O2 ). In vivo experiments were performed in healthy rats (N = 10) and in rats with bilateral ischemia-reperfusion injury (N = 4). To assess the step response of renal oxygenation, a second-order exponential model was used (model parameters: amplitude [A], time delay [Δt], damping constant [D], and period of the oscillation [T]) for renal cortex, outer stripe of the outer medulla, inner stripe of the outer medulla, and inner medulla. RESULTS: The second-order exponential model permitted us to model the exponential T2∗ recovery and the superimposed T2∗ oscillation following renal oxygenation stimulus. The in vivo experiments revealed a difference in Douter medulla between healthy controls (D < 1, indicating oscillatory recovery) and ischemia-reperfusion injury (D > 1, reflecting aperiodic recovery). The increase in Douter medulla by a factor of 3.7 (outer stripe of the outer medulla) and 10.0 (inner stripe of the outer medulla) suggests that this parameter might be rather sensitive to (patho)physiological oxygenation changes. CONCLUSION: This study demonstrates the feasibility of monitoring the dynamic oxygenation response of renal tissues to a step-like oxygenation challenge using high-temporal-resolution T2∗ mapping. Our results suggest that the implemented system analysis approach may help to unlock questions regarding regulation of renal oxygenation, with the ultimate goal of providing imaging means for diagnostics and therapy of renal diseases.

Detection of gadolinium deposition in cortical bone with ultrashort echo time T1 mapping: an ex vivo study in a rabbit model.

OBJECTIVES: To investigate the capacity of ultrashort echo time (UTE) T1 mapping to non-invasively assess gadolinium deposition in cortical bone after gadolinium-based contrast agent (GBCA) administration. METHODS: Twenty-eight New Zealand rabbits (male, 3.0-3.5 kg) were randomly allocated into control, macrocyclic, high-dose macrocyclic, and linear GBCA groups (n = 7 for each group), and respectively given daily doses of 0.9 ml/kg bodyweight saline, 0.3 mmol/kg bodyweight gadobutrol, 0.9 mmol/kg bodyweight gadobutrol, and 0.3 mmol/kg bodyweight gadopentetate dimeglumine for five consecutive days per week over a period of 4 weeks. After a subsequent 4 weeks of recovery, the rabbits were sacrificed and their tibiae harvested. T1 value of cortical bone was measured using a combination of UTE actual flip angle imaging and variable repetition time on a 7T animal scanner. Gadolinium concentration in cortical bone was measured using inductively coupled plasma mass spectrometry (ICP-MS). Pearson's correlation between R1 value (R1 = 1/T1) and gadolinium concentration in cortical bone was assessed. RESULTS: Bone T1 values were significantly lower in the lower-dose macrocyclic (329.2 ± 21.0 ms, p < 0.05), higher-dose macrocyclic (316.8 ± 21.7 ms, p < 0.01), and linear (296.8 ± 24.1 ms, p < 0.001) GBCA groups compared with the control group (356.3 ± 19.4 ms). Gadolinium concentrations measured by ICP-MS in the control, lower-dose macrocyclic, higher-dose macrocyclic, and linear GBCA groups were 0.04 ± 0.02 µg/g, 2.60 ± 0.48 µg/g, 4.95 ± 1.17 µg/g, and 13.62 ± 1.55 µg/g, respectively. There was a strong positive correlation between R1 values and gadolinium concentrations in cortical bone (r = 0.73, p < 0.001). CONCLUSIONS: These results suggest that UTE T1 mapping has the potential to provide a non-invasive assessment of gadolinium deposition in cortical bone following GBCA administration. KEY POINTS: • Changes in T1 value related to gadolinium deposition were found in bone after both linear and macrocyclic GBCA administrations. • R1 relaxometry correlates strongly with gadolinium concentration in cortical bone. • UTE T1 mapping provides a potential tool for non-invasively monitoring gadolinium deposition in cortical bone.

(TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients.

Glioblastoma (GBM) brain tumor is the most aggressive white matter (WM) invasive cerebral primary neoplasm. Due to its inherently heterogeneous appearance and shape, previous studies pursued either the segmentation precision of the tumors or qualitative analysis of the impact of brain tumors on WM integrity with manual delineation of tumors. This paper aims to develop a comprehensive analytical pipeline, called (TS)2WM, to integrate both the superior performance of brain tumor segmentation and the impact of GBM tumors on the WM integrity via tumor segmentation and tract statistics using the diffusion tensor imaging (DTI) technique. The (TS)2WM consists of three components: (i) A dilated densely connected convolutional network (D2C2N) for automatically segment GBM tumors. (ii) A modified structural connectome processing pipeline to characterize the connectivity pattern of WM bundles. (iii) A multivariate analysis to delineate the local and global associations between different DTI-related measurements and clinical variables on both brain tumors and language-related regions of interest. Among those, the proposed D2C2N model achieves competitive tumor segmentation accuracy compared with many state-of-the-art tumor segmentation methods. Significant differences in various DTI-related measurements at the streamline, weighted network, and binary network levels (e.g., diffusion properties along major fiber bundles) were found in tumor-related, language-related, and hand motor-related brain regions in 62 GBM patients as compared to healthy subjects from the Human Connectome Project.

Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection.

MOTIVATION: The detection of potential biomarkers of Alzheimer's disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues. RESULTS: We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial-anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer's Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD/normal control subjects, and a high accuracy of AD/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD. AVAILABILITY AND IMPLEMENTATION: https://github.com/Meiyan88/SASM-VGWAS.

Identification of topological features in renal tumor microenvironment associated with patient survival.

Motivation: As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist's visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results: We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers. Availability and implementation: https://github.com/chengjun583/KIRP-topological-features. Contact: 1271992826@qq.com or kunhuang@iu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

MRI Gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks.

PURPOSE: To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact. THEORY AND METHODS: Gibbs-ringing artifact in MR images is caused by insufficient sampling of the high frequency data. Existing methods exploit smooth constraints to reduce intensity oscillations near sharp edges at the cost of blurring details. In this work, we developed a machine learning approach for removing the Gibbs-ringing artifact from MR images. The ringing artifact was extracted from the original image using a deep convolutional neural network and then subtracted from the original image to obtain the artifact-free image. Finally, its low-frequency k-space data were replaced with measured counterparts to enforce data fidelity further. We trained the convolutional neural network using 17,532 T2-weighted (T2W) normal brain images and evaluated its performance on T2W images of normal and tumor brains, diffusion-weighted brain images, and T2W knee images. RESULTS: The proposed method effectively removed the ringing artifact without noticeable smoothing in T2W and diffusion-weighted images. Quantitatively, images produced by the proposed method were closer to the fully-sampled reference images in terms of the root-mean-square error, peak signal-to-noise ratio, and structural similarity index, compared with current state-of-the-art methods. CONCLUSION: The proposed method presents a novel and effective approach for Gibbs-ringing reduction in MRI. The convolutional neural network-based approach is simple, computationally efficient, and highly applicable in routine clinical MRI.

Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation.

Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi-echo gradient-echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat-water separation (R2*-IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in-phase (IP) echo initialization of R2*-IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI. A 3D multi-echo GRE sequence was implemented to acquire out-phase and IP echoes. For the IP method, the R2* and field maps estimated by separately fitting the magnitude and phase of IP echoes were used to initialize gradient search R2*-IDEAL to obtain final R2*, field, water, and fat maps, and the final field map was used to generate QSM. The IP method was compared with the existing Zero method (initializing the field to zero), VARPRO-GC (variable projection using graphcuts but still initializing the field to zero), and SPURS (simultaneous phase unwrapping and removal of chemical shift using graphcuts for initialization) on both simulation and in vivo data. The single peak fat model was also compared with the multi-peak fat model. There was no substantial difference on QSM between the single peak and multi-peak fat models, but there were marked differences among different initialization methods. The simulations demonstrated that IP provided the lowest error in the field map. Compared to Zero, VARPRO-GC and SPURS, the proposed IP method provided substantially improved spine QSM in all 10 subjects.

Global cytokine/chemokine profile identifies potential progression prediction indicators in hand-foot-and-mouth disease patients with Enterovirus A71 infections.

OBJECTIVE: New clinical indicators are urgently needed for predicting the progression and complications of hand-foot-and-mouth disease (HFMD) caused by EV-A71 infections. MATERIALS AND METHODS: Serum specimens from 132 EV-A71 HFMD patients and 73 health children were collected during 2012-2014 in Shenzhen, China. The specific cytokines/chemokines were detected with a 274-human cytokine antibody array, followed by a 38-inflammation cytokine array, and further validated by ELISA. RESULTS: Cytokines varied in different severity of EV-A71 HFMD patients. The ROC curve analysis revealed 5 serum cytokines with high sensitivity and specificity in predicting the disease progression. Eotaxin, IL-8 and IP-10 have showed high AUC values (0.90-0.95) for discrimination between the health controls and the patient group. The three cytokines showed high sensitivity (80-91%) and specificity (88-95%). MMP-8 had a high sensitivity and specificity to predict mild HFMD (100%, 100%). IL-1b and leptin discriminated the severe/critical group from the mild group (79% and 69% in sensitivity, 73% and 63% in specificity). CONCLUSIONS: Eotaxin, IP-10 and IL-8 could be potential indicators for predicting HFMD progression with EV-A71 infection. MMP-8 is a specific indicator for mild infection, while IL-1b and leptin display potential for predicting the severity and criticality.

Magnetic Susceptibility and Fat Content in the Lumbar Spine of Postmenopausal Women With Varying Bone Mineral Density.

BACKGROUND: Osteoporosis is a systemic disease characterized by low bone mass with increased fracture risk. Quantitative imaging biomarkers are important for accurately predicting fracture risk in patients with osteoporosis. PURPOSE: To prospectively study the changes of magnetic susceptibility and fat content in the lumbar spine of postmenopausal females with varying bone mineral density (BMD), and investigate their application to osteoporosis assessment. STUDY TYPE: Cohort. POPULATION: In all, 108 postmenopausal females (58.2 ± 6.7 [range 45-79] years old). FIELD STRENGTH/SEQUENCE: Quantitative computed tomography (QCT) performed on a 64-detector CT scanner; quantitative susceptibility mapping (QSM) and mDixon quant MR imaging performed using a 3.0T imaging system with a 16-channel posterior coil. ASSESSMENT: QCT, QSM, and mDixon were performed in 108 postmenopausal females to measure vertebral BMD, susceptibility, and proton-density fat fraction (PDFF). Mean vertebral QSM and PDFF were compared among three BMD cohorts (normal, osteopenic, and osteoporotic). Receiver operating characteristic analyses were performed to evaluate the performance of QSM, PDFF, and QSM+PDFF for assessing osteoporosis. STATISTICAL TESTS: Parameters were compared using Kruskal-Wallis test and Pearson test. RESULTS: Compared with that of the normal BMD group (-17.0 ± 43.6 ppb), vertebral QSM was significantly increased in osteopenia (30.8 ± 47.0 ppb, P < 0.001), and further increased in osteoporosis (82.0 ± 39.9 ppb, P < 0.001). QSM was negatively correlated with BMD (r = -0.70, P < 0.001) and positively correlated with PDFF (r = 0.64, P < 0.001). Compared with the area under the curve (AUC) of PDFF, the AUC of QSM was higher in differentiating between normal and osteoporosis (P = 0.44), and between osteopenia and osteoporosis (P = 0.13), but without statistical significance. The AUC of QSM+PDFF was significantly higher than that of PDFF for differentiating between osteopenia and osteoporosis (0.82 vs. 0.70, P = 0.039). DATA CONCLUSION: The combination of vertebral susceptibility and fat content may be a promising marker for assessing postmenopausal osteoporosis. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1020-1028.

Correction to: Primary health information standard system based on semantic interoperability.

After publication of this supplement article [1], it was brought to our attention that the first and correspondence authors' affiliation information was incorrectly spelt. The original spelling was written as 'Sothern'. However, the correct spelling should be 'Southern'.

Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images.

Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography-computed tomography (PET-CT) is a suitable imaging technique to assess this disease. However, the large amount of data produced by numerous patients causes traditional manual delineation of tumor contour, a basic step for radiotherapy, to become time-consuming and labor-intensive. Thus, the demand for automatic and credible segmentation methods to alleviate the workload of radiologists is increasing. This paper presents a method that uses fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. This work is the first to segment NPC using dual-modality PET-CT images. This technique is identical to what is used in clinical practice and offers considerable convenience for subsequent radiotherapy. The deep supervision introduced by auxiliary paths can explicitly guide the training of lower layers, thus enabling these layers to learn more representative features and improve the discriminative capability of the model. Results of threefold cross-validation with a mean dice score of 87.47% demonstrate the efficiency and robustness of the proposed method. The method remarkably outperforms state-of-the-art methods in NPC segmentation. We also validated by experiments that the registration process among different subjects and the auxiliary paths strategy are considerably useful techniques for learning discriminative features and improving segmentation performance.

Functional MRI registration with tissue-specific patch-based functional correlation tensors.

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods.

Connectivity-based parcellation of the nucleus accumbens into core and shell portions for stereotactic target localization and alterations in each NAc subdivision in mTLE patients.

The nucleus accumbens (NAc), an important target of deep brain stimulation for some neuropsychiatric disorders, is thought to be involved in epileptogenesis, especially the shell portion. However, little is known about the exact parcellation within the NAc, and its structural abnormalities or connections alterations of each NAc subdivision in temporal lobe epilepsy (TLE) patients. Here, we used diffusion probabilistic tractography to subdivide the NAc into core and shell portions in individual TLE patients to guide stereotactic localization of NAc shell. The structural and connection abnormalities in each NAc subdivision in the groups were then estimated. We successfully segmented the NAc in 24 of 25 controls, 14 of 16 left TLE patients, and 14 of 18 right TLE patients. Both left and right TLE patients exhibited significantly decreased fractional anisotropy (FA) and increased radial diffusivity (RD) in the shell, while there was no significant alteration in the core. Moreover, relatively distinct structural connectivity of each NAc subdivision was demonstrated. More extensive connection abnormalities were detected in the NAc shell in TLE patients. Our results indicate that neuronal degeneration and damage caused by seizure mainly exists in NAc shell and provide anatomical evidence to support the role of NAc shell in epileptogenesis. Remarkably, those NAc shell tracts with increased connectivities in TLE patients were found decreased in FA, which indicates disruption of fiber integrity. This finding suggests the regeneration of aberrant connections, a compensatory and repair process ascribed to recurrent seizures that constitutes part of the characteristic changes in the epileptic network.