Cross-ancestry genome-wide association studies of brain imaging phenotypes.

Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.

A nomogram for prediction of ERCP success in patients with bile duct leaks: a multicenter study.

BACKGROUND: Bile duct leaks (BDLs) are serious complications that occurs after hepatobiliary surgery and trauma, leading to rapid clinical deterioration. Endoscopic retrograde cholangiopancreatography (ERCP) is the first-line treatment for BDLs, but it is not clear which patients will respond to this therapy and which patients will require additional surgical intervention. The aim of our study was to explore the predictors of successful ERCP for BDLs. METHODS: A retrospective analysis was conducted using data from six centers' databases. All consecutive patients who were clinically confirmed as BDLs were included in the study. Collected data were demographics, disease severity, and ERCP procedure characteristics. Univariate and multivariate analysis were used to select independent predictive factors that affect the outcome of ERCP for BDLs, and a nomogram was established. Calibration and ROC curves were used to evaluate the models. RESULTS: Four hundred and forty-eight consecutive patients were clinically confirmed as BDLs and 347 were excluded. In the 101 patients included patients, clinical success was achieved in 78 patients (77.2%). In logistic multivariable regression, two independent factors were negatively associated with the success of ERCP: SIRS (OR, 0.183; 95% CI 0.039-0.864; P = 0.032) and high-grade leak (OR 0.073; 95% CI 0.010-0.539; P = 0.010). Two independent factors were positively associated with the success of ERCP: leak-bridging drainage (OR 4.792; 95% CI 1.08-21.21; P = 0.039) and cystic duct leak (OR 6.193; 95% CI 1.03-37.17; P = 0.046). The prediction model with these four factors was evaluated using a receiver-operating characteristic (ROC) curve, which demonstrated an area under the curve of 0.9351. The calibration curve showed that the model had good predictive accuracy. CONCLUSION: Leak-bridging drainage and cystic duct leak are positive predictors for the success of ERCP, while SIRS and high-grade leak are negative predictors. This prediction model with nomogram has good predictive ability and practical clinical value, and may be helpful in clinical decision-making and prognostication.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits.

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes.

The hippocampus is critical for memory and cognition and neuropsychiatric disorders, and its subfields differ in architecture and function. Genome-wide association studies on hippocampal and subfield volumes are mainly conducted in European populations; however, other ancestral populations are under-represented. Here we conduct cross-ancestry genome-wide association meta-analyses in 65,791 individuals for hippocampal volume and 38,977 for subfield volumes, including 7,009 individuals of East Asian ancestry. We identify 339 variant-trait associations at P < 1.13 × 10-9 for 44 hippocampal traits, including 23 new associations. Common genetic variants have similar effects on hippocampal traits across ancestries, although ancestry-specific associations exist. Cross-ancestry analysis improves the fine-mapping precision and the prediction performance of polygenic scores in under-represented populations. These genetic variants are enriched for Wnt signaling and neuron differentiation and affect cognition, emotion and neuropsychiatric disorders. These findings may provide insight into the genetic architectures of hippocampal and subfield volumes.

Abnormal cerebral blood flow in patients with Leber's hereditary optic neuropathy.

PURPOSE: The study aimed to unravel abnormal cerebral blood flow (CBF) in patients with Leber's hereditary optic neuropathy (LHON) using arterial spin labeling (ASL) and to investigate the associations among disrupted CBF, disease duration, and neuro-ophthalmological impairment. METHODS: ASL perfusion imaging data was collected from 20 patients with acute LHON, 29 patients with chronic LHON, and 37 healthy controls. We used a one-way analysis of covariance to test the intergroup differences in CBF. Linear and nonlinear curve fit models were applied to explore the associations among CBF, disease duration, and neuro-ophthalmological metrics. RESULTS: Brain regions differed in LHON patients, including the left sensorimotor and bilateral visual areas (p < 0.05, cluster-wise family-wise error correction). Acute and chronic LHON patients demonstrated lower CBF in bilateral calcarine than the healthy controls. Chronic LHON had lower CBF in the left middle frontal gyrus and sensorimotor cortex, and temporal-partial junction than the healthy controls and acute LHON. A significant logarithmic negative correlation was shown between CBF of left middle frontal gyrus and disease duration. A significant linear positive correlation was found between retinal nerve fiber layer thickness and CBF in left middle frontal gyrus, and negative correlations between loss of variance and CBF in left middle frontal gyrus and sensorimotor cortex (p < 0.05, Bonferroni correction). CONCLUSION: LHON patients exhibited reduced CBF in the visual pathway, sensorimotor and higher-tier cognitive areas. Disease duration and neuro-ophthalmological impairments can influence the metabolism of non-visual areas.

The impact of pre-adulthood urbanicity on hippocampal subfield volumes and neurocognitive abilities in young adults.

BACKGROUND: Urbanicity refers to the conditions that are particular to urban areas and is a growing environmental challenge that may affect hippocampus and neurocognition. This study aimed to investigate the effects of the average pre-adulthood urbanicity on hippocampal subfield volumes and neurocognitive abilities as well as the sensitive age windows of the urbanicity effects. PARTICIPANTS AND METHODS: We included 5,390 CHIMGEN participants (3,538 females; age: 23.69 ± 2.26 years, range: 18-30 years). Pre-adulthood urbanicity of each participant was defined as the average value of annual night-time light (NL) or built-up% from age 0-18, which were extracted from remote-sensing satellite data based on annual residential coordinates of the participants. The hippocampal subfield volumes were calculated based on structural MRI and eight neurocognitive measures were assessed. The linear regression was applied to investigate the associations of pre-adulthood NL with hippocampal subfield volumes and neurocognitive abilities, mediation models were used to find the underlying pathways among urbanicity, hippocampus and neurocognition, and distributed lag models were used to identify sensitive age windows of urbanicity effect. RESULTS: Higher pre-adulthood NL was associated with greater volumes in the left (ß = 0.100, 95%CI: [0.075, 0.125]) and right (0.078, [0.052, 0.103]) fimbria and left subiculum body (0.045, [0.020, 0.070]) and better neurocognitive abilities in information processing speed (-0.212, [-0.240, -0.183]), working memory (0.085, [0.057, 0.114]), episodic memory (0.107, [0.080, 0.135]), and immediate (0.094, [0.065, 0.123]) and delayed (0.087, [0.058, 0.116]) visuospatial recall, and hippocampal subfield volumes and visuospatial memory showed bilateral mediations for the urbanicity effects. Urbanicity effects were greatest on the fimbria in preschool and adolescence, on visuospatial memory and information processing from childhood to adolescence and on working memory after 14 years. CONCLUSION: These findings improve our understanding of the impact of urbanicity on hippocampus and neurocognitive abilities and will benefit for designing more targeted intervention for neurocognitive improvement.

MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method.

Mendelian randomization is a statistical method for inferring the causal relationship between exposures and outcomes using an economics-derived instrumental variable approach. The research results are relatively complete when both exposures and outcomes are continuous variables. However, due to the noncollapsing nature of the logistic model, the existing methods inherited from the linear model for exploring binary outcome cannot take the effect of confounding factors into account, which leads to biased estimate of the causal effect. In this article, we propose an integrated likelihood method MR-BOIL to investigate causal relationships for binary outcomes by treating confounders as latent variables in one-sample Mendelian randomization. Under the assumption of a joint normal distribution of the confounders, we use expectation maximization algorithm to estimate the causal effect. Extensive simulations demonstrate that the estimator of MR-BOIL is asymptotically unbiased and that our method improves statistical power without inflating type I error rate. We then apply this method to analyze the data from Atherosclerosis Risk in Communications Study. The results show that MR-BOIL can better identify plausible causal relationships with high reliability, compared with the unreliable results of existing methods. MR-BOIL is implemented in R and the corresponding R code is provided for free download.

Brain Gray Matter Atrophy and Functional Connectivity Remodeling in Patients With Chronic LHON.

Purpose: The aim of this study was to investigate the brain gray matter volume (GMV) and spontaneous functional connectivity (FC) changes in patients with chronic Leber's hereditary optic neuropathy (LHON), and their relations with clinical measures. Methods: A total of 32 patients with chronic LHON and matched sighted healthy controls (HC) underwent neuro-ophthalmologic examinations and multimodel magnetic resonance imaging (MRI) scans. Voxel-based morphometry (VBM) was used to detect the GMV differences between the LHON and HC. Furthermore, resting-state FC analysis using the VBM-identified clusters as seeds was carried out to detect potential functional reorganization in the LHON. Finally, the associations between the neuroimaging and clinical measures were performed. Results: The average peripapillary retinal nerve fiber layer (RNFL) thickness of the chronic LHON was significantly thinner (T = -16.421, p < 0.001), and the mean defect of the visual field was significantly higher (T = 11.28, p < 0.001) than the HC. VBM analysis demonstrated a significantly lower GMV of bilateral calcarine gyri (CGs) in the LHON than in the HC (p < 0.05). Moreover, in comparison with the HC, the LHON had significantly lower FC between the centroid of the identified left CG and ipsilateral superior occipital gyrus (SOG) and higher FC between this cluster and the ipsilateral posterior cingulate gyrus (p < 0.05, corrected). Finally, the GMV of the left CG was negatively correlated with the LHON duration (r = -0.535, p = 0.002), and the FC between the left CG and the ipsilateral posterior cingulate gyrus of the LHON was negatively correlated with the average peripapillary RNFL thickness (r = -0.522, p = 0.003). Conclusion: The atrophied primary visual cortex of the chronic LHON may be caused by transneuronal degeneration following the retinal damage. Moreover, our findings suggest that the functional organization of the atrophied primary visual cortex has been reshaped in the chronic LHON.

Clinical effectiveness of contrast medium injection protocols for 80-kV coronary and craniocervical CT angiography-a prospective multicenter observational study.

BACKGROUND AND OBJECTIVE: Decreasing X-ray tube voltage is an effective way to reduce radiation and contrast dose, especially in non-obese patients. The current study focuses on CTA in non-obese patients to evaluate image quality and feasibility of 80-kV acquisition protocols with varying iodine delivery rates (IDR) and contrast concentrations in routine clinical practice. METHODS: A prospective observational study in patients ≥ 18 years and ≤ 90 kg referred for coronary or craniocervical CTA at 10 centers in China (ClinicalTrials.gov: NCT02840903). Patients were divided into four groups: a standard 100-kV protocol (370 mgI/ml, IDR 1.48 gI/s), and three 80-kV protocols (370 mgI/ml, IDR 1.2 gI/s; 300 mgI/ml, IDR 1.2 gI/s; 300 mgI/ml, IDR 0.96gI/s). The primary outcome was contrast opacification of target vascular segments. Secondary outcomes were image quality (contrast-to-noise ratio, signal-to-noise ratio, visual image quality, and diagnostic confidence assessment), radiation, and iodine dose. RESULTS: From July 2016 to July 2017, 1213 patients were enrolled: 614 coronary and 599 craniocervical CTA. The mean contrast opacification was ≥ 300 HU for 80-kV 1.2 gI/s IDR scanned segments; IDR 0.96 gI/s led to lower opacification. Image quality and diagnostic confidence were fair to excellent (≥ 98% of images), despite lower contrast-to-noise ratios and signal-to-noise ratios in 80-kV images. Compared to the standard protocol, 80-kV protocols led to 44-52% radiation dose reductions (p < 0.001) and 19% iodine dose reductions (p < 0.001). CONCLUSION: Eighty-kilovolt 1.2 gI/s IDR protocols can be recommended for coronary and craniocervical CTA in non-obese patients, reducing radiation and iodine dose without compromising image quality. KEY POINTS: • Using low-voltage scanning CTA protocols, in which tube voltage and iodine delivery rate are reduced proportionally (voltage: 80 kV, IDR: 1.2 gI/s), reduces radiation and contrast dose without compromising image quality in routine clinical practice. • Reducing iodine delivery rate beyond direct proportionality to tube voltage is not beneficial.

Aberrant neurovascular coupling in Leber's hereditary optic neuropathy: Evidence from a multi-model MRI analysis.

The study aimed to investigate the neurovascular coupling abnormalities in Leber's hereditary optic neuropathy (LHON) and their associations with clinical manifestations. Twenty qualified acute Leber's hereditary optic neuropathy (A-LHON, disease duration ≤ 1 year), 29 chronic Leber's hereditary optic neuropathy (C-LHON, disease duration > 1 year), as well as 37 healthy controls (HCs) were recruited. The neurovascular coupling strength was quantified as the ratio between regional homogeneity (ReHo), which represents intrinsic neuronal activity and relative cerebral blood flow (CBF), representing microcirculatory blood supply. A one-way analysis of variance was used to compare intergroup differences in ReHo/CBF ratio with gender and age as co-variables. Pearson's Correlation was used to clarify the association between ReHo, CBF, and neurovascular coupling strength. Furthermore, we applied linear and exponential non-linear regression models to explore the associations among ReHo/CBF, disease duration, and neuro-ophthalmological metrics. Compared with HCs, A_LHON, and C_LHON patients demonstrated a higher ReHo/CBF ratio than the HCs in the bilateral primary visual cortex (B_CAL), which was accompanied by reduced CBF while preserved ReHo. Besides, only C_LHON had a higher ReHo/CBF ratio and reduced CBF in the left middle temporal gyrus (L_MTG) and left sensorimotor cortex (L_SMC) than the HCs, which was accompanied by increased ReHo in L_MTG (p < 1.85e-3, Bonferroni correction). A-LHON and C-LHON showed a negative Pearson correlation between ReHo/CBF ratio and CBF in B_CAL, L_SMC, and L_MTG. Only C_LHON showed a weak positive correlation between ReHo/CBF ratio and ReHo in L_SMC and L_MTG (p < 0.05, uncorrected). Finally, disease duration was positively correlated with ReHo/CBF ratio of L_SMC (Exponential: Radj2 = 0.23, p = 8.66e-4, Bonferroni correction). No statistical correlation was found between ReHo/CBF ratio and neuro-ophthalmological metrics (p > 0.05, Bonferroni correction). Brain neurovascular "dyscoupling" within and outside the visual system might be an important neurological mechanism of LHON.

Occult primary white matter impairment in Leber hereditary optic neuropathy.

BACKGROUND AND PURPOSE: Leber hereditary optic neuropathy (LHON) is a disease maternally inherited from mitochondria that predominantly impairs the retinal ganglion cells and their axons. To identify whether occult brain white matter (WM) impairment is involved, a voxel-based analysis (VBA) of diffusion metrics was carried out in LHON patients with normal-appearing brain parenchyma. METHODS: Fifty-four symptomatic LHON patients (including 22 acute LHON with vision loss for ≤12 months, and 32 chronic LHON) without any visible brain lesions and 36 healthy controls (HCs) were enrolled in this study. VBA was applied to quantify the WM microstructural changes of LHON patients. Finally, the associations of the severity of WM impairment with disease duration and ophthalmologic deficits were assessed. RESULTS: Compared with the HCs, the average retinal nerve fiber layer (RNFL) thickness was significantly reduced in patients with chronic LHON, whereas it was increased in patients with acute LHON (p < 0.05, corrected). VBA identified significantly decreased fractional anisotropy widely in WM in both the acute and chronic LHON patients, including the left anterior thalamic radiation and superior longitudinal fasciculus, and bilateral corticospinal tract, dentate nuclei, inferior longitudinal fasciculus, forceps major, and optic radiation (OR; p < 0.05, corrected). The integrity of most WM structures (except for the OR) was correlated with neither disease duration nor RNFL thickness (p > 0.05, corrected). CONCLUSIONS: Occult primary impairment of widespread brain WM is present in LHON patients. The coexisting primary and secondary WM impairment may jointly contribute to the pathological process of LHON.

Hybrid network with difference degree and attention mechanism combined with radiomics (H-DARnet) for MVI prediction in HCC.

MVI is a risk assessment factor related to hepatocellular carcinoma (HCC) recurrence after hepatectomy or liver transplantation. The goal of this paper is to study the preoperative diagnosis of microvascular invasion (MVI) by using a deep learning algorithm in non-contrast T2 weighted magnetic resonance imaging (MRI) images instead of pathological images. Herein, an ensemble learning algorithm named H-DARnet-based on the difference degree and attention mechanism, combined with radiomics, for MVI prediction-is proposed. Our hybrid network combines the fine-grained, high-level semantic, and radiomics features and exhibits a rich multilevel-feature architecture composed of global-local-prior knowledge with suitable complementarity. The total loss function comprises two regularization items--the triplet and the cross-entropy loss function--which are selected for the triplet network and SE-DenseNet, respectively. The hard triplet sample selection strategy for a triplet network and data augmentation for small-scale liver image datasets in convolutional neural network (CNN) training is indispensable. For 200 patch level test samples (135 positive samples and 65 negative samples), our method can obtain the best prediction results, the AUC, sensitivity, and specificity were 0.826, 79.5% and 73.8%, respectively. The experiment results show that MVI can be predicted by using MRI images, and the proposed method is better than other deep learning algorithms and hand-crafted feature algorithms. The proposed ensemble learning algorithm is proved to be an effective method for MVI prediction.

Abnormal large-scale structural rich club organization in Leber's hereditary optic neuropathy.

OBJECTIVE: The purpose of this study was to investigate whether the large-scale structural rich club organization was abnormal in patients with Leber's hereditary optic neuropathy (LHON) using diffusion tensor imaging (DTI), and the associations among disrupted brain structural connectivity, disease duration, and neuro-ophthalmological impairment. METHODS: Nineteen acute, 34 chronic LHON patients, and 36 healthy controls (HC) underwent DTI and neuro-ophthalmological measurements. The brain structural network and rich club organization were constructed based on deterministic fiber tracking at the individual level. Then intergroup differences among the acute, chronic LHON patients and healthy controls (HC) in three types of structural connections, including rich club, feeder, and local ones, were compared. Network-based Statistics (NBS) was also used to test the intergroup connectivity differences for each fiber. Several linear and nonlinear curve fit models were applied to explore the associations among large-scale brain structural connectivity, disease duration, and neuro-ophthalmological metrics. RESULTS: Compared to the HC, both the acute and chronic LHON patients had consistently significantly lower fractional anisotropy (FA) and higher radial diffusion (RD) for feeder connections (p < 0.05, FDR correction). Acute LHON patients had significantly lower FA and higher RD for local connections (p < 0.05, FDR correction). There was no significant difference in large-scale brain structural connectivity between acute and chronic LHON (p > 0.05, FDR correction). NBS also identified reduced FA of three feeder connections and five local ones linking visual, auditory, and basal ganglia areas in LHON patients (p < 0.05, FDR correction). No structural connections showed linear or nonlinear association with either disease duration or neuro-ophthalmological indicators (p > 0.05, FDR correction). A significant negative correlation was shown between the retinal nerve fiber layer (RNFL) thickness and disease duration (p < 0.05, FDR correction). CONCLUSIONS: Abnormal rich club organization of the structural network was identified in both the acute and chronic LHON. Furthermore, our findings suggest the coexistence of both primary and secondary connectivity damage in the LHON.

Noninvasive DW-MRI metrics for staging hepatic fibrosis and grading inflammatory activity in patients with chronic hepatitis B.

PURPOSE: To assess the value of various diffusion parameters obtained from monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging (DWI) models for staging hepatic fibrosis (HF) and grading inflammatory activity in patients with chronic hepatitis B (CHB). METHODS: 82 patients with CHB and 30 healthy volunteers underwent DWI with 13 b-values on a 3T MRI unit. The standard apparent diffusion coefficient (ADCst) was calculated using a monoexponential model. The true diffusion coefficient (Dt), pseudo-diffusion coefficient (Dp), and perfusion fraction (f) were calculated using a biexponential model. The distributed diffusion coefficient (DDC) and water-molecule diffusion heterogeneity index (α) were calculated using a stretched-exponential model. Receiver operating characteristic (ROC) curves were performed for diffusion parameters to compare the diagnosis performance. RESULTS: The distributions of hepatic fibrosis stages and the inflammatory activity grades (METAVIR scoring system) were as follows: F0, n = 1; F1, n = 16; F2, n = 31; F3, n = 19; and F4, n = 15. A0, n = 1; A1, n = 14; A2, n = 46; and A3, n = 21. ADCst, Dt and DDC values showed negative correlation with the fibrosis stage (r = - 0.418, - 0.717 and - 0.630, all P < 0.001) and the inflammatory activity grade (r = - 0.514, - 0.626 and - 0.550, all P < 0.001). The area under the ROC curve (AUC) of Dt (AUC = 0.854, 0.881) and DDC (AUC = 0.794, 0.834) were significantly higher than that of ADCst (AUC = 0.637, 0.717) in discriminating significant fibrosis (≥ F2) and advanced fibrosis (≥ F3) (all P < 0.05). Although Dt (AUC = 0.867, 0.836) and DDC (AUC = 0.810, 0.808) showed higher AUCs than ADCst (AUC = 0.767, 0.803), there was no significant difference in their ability in detecting inflammatory activity grade ≥ A2/A3 (P > 0.05). CONCLUSIONS: Dt and DDC are promising indicators and outperform ADCst for staging HF. While both Dt and DDC have similar diagnostic performance compared with ADCst for grading inflammatory activity.

Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.

To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variability adaptation problem of lymph node data which is related to the problem of domain adaptation in deep learning differs from the general domain adaptation problem because of the typically larger CT image size and more complex data distributions. Therefore, domain adaptation for this problem needs to consider the shared feature representation and even the conditioning information of each domain so that the adaptation network can capture significant discriminative representations in a domain-invariant space. This paper extracts domain-invariant features based on a cross-domain confounding representation and proposes a cycle-consistency learning framework to encourage the network to preserve class-conditioning information through cross-domain image translations. Compared with the performance of different domain adaptation methods, the accurate rate of our method achieves at least 4.4% points higher under multicenter lymph node data. The pixel-level cross-domain image mapping and the semantic-level cycle consistency provided a stable confounding representation with class-conditioning information to achieve effective domain adaptation under complex feature distribution.

Non-invasive assessment of hepatic fibrosis: comparison of MR elastography to transient elastography and intravoxel incoherent motion diffusion-weighted MRI.

OBJECTIVE: To compare the ability of MR elastography (MRE) with transient elastography (TE) and intravoxel incoherent motion (IVIM) diffusion-weighted MRI in staging hepatic fibrosis (HF). MATERIALS AND METHODS: 100 patients with chronic liver disease and 25 healthy volunteers underwent preoperative MRE, IVIM on a 3T MRI unit, and ultrasound-based TE. Liver stiffness measurement from MRE (LSM-MRE) and liver stiffness measurement from TE (LSM-TE) were measured; four diffusion parameters including the true diffusion coefficient (Dt), pseudo-diffusion coefficient, perfusion fraction (f), and apparent diffusion coefficient (ADC) were calculated. Receiver operating characteristic (ROC) curves were performed for significant parameters to compare the diagnosis performance for detecting HF. RESULTS: LSM-MRE and LSM-TE values showed positive correlation with the fibrosis stage (r = 0.910 and 0.813, P < 0.001). Dt, f, and ADC values showed negative correlation with the fibrosis stage (r = - 0.727, - 0.503, and - 0.601, all P < 0.001). The area under the ROC curve (AUC) of LSM-MRE (AUC = 0.965, 0.957, 0.983) was significantly higher than that of LSM-TE (AUC = 0.906, 0.913, 0.931) and Dt (AUC = 0.875, 0.879, 0.861) in discriminating significant HF (≥ F2), advanced HF (≥ F3), or cirrhosis (F4) (all P < 0.05). Although LSM-TE showed higher AUCs than Dt in detecting fibrosis stages, there were no significant differences between LSM-TE and Dt (P > 0.05) except for detecting F4 (P < 0.05). CONCLUSION: MRE shows excellent diagnostic performance for predicting significant fibrosis, advanced fibrosis compared with TE and IVIM, while TE and IVIM have comparable diagnostic performance.

Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma.

PURPOSE: To appraise the ability of the computed tomography (CT) radiomics signature for prediction of early recurrence (ER) in patients with hepatocellular carcinoma (HCC). METHODS: A set of 325 HCC patients were enrolled in this retrospective study and the whole dataset was divided into 2 cohorts, including "training set" (225 patients) and "test set" (100 patients). All patients who underwent partial hepatectomy were followed up at least within 1 year. 656 Radiomics features were extracted from arterial-phase and portal venous-phase CT images. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Univariate analysis was used to identify clinical and radiomics significant features. Models (radiomics signature, clinical model, and combined model) were evaluated by area under the curve (AUC) of receiver operating characteristic curve. The models' performances for prediction of ER were assessed. RESULTS: The radiomics signature was built by 14 selected radiomics features and was significantly associated with ER (P < 0.001); the AUCs of the "train set" and the "test set" were 0.818 (95% CI 0.760-0.865) and 0.719 (95% CI 0.621-0.805), respectively. The tumor size, tumor capsule, and γ-glutamyl transferase (GGT) were significantly associated with ER in the clinical model (P < 0.05). The combined model showed incremental prognostic value, with the AUCs of "training dataset" and "test dataset" were 0.846 (95% CI 0.792-0.890) and 0.737 (95% CI 0.640-0.820), respectively. The radiomics signature, tumor size, and the level of GGT were independent predictors of ER (P < 0.05). CONCLUSIONS: The CT radiomics signature can be conveniently used to predict the ER in patient with HCC. The combined model performed better for prediction of ER than radiomics signature or clinical model.

[Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images].

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.

Brain white matter changes in asymptomatic carriers of Leber's hereditary optic neuropathy.

OBJECTIVE: Subclinical abnormalities, including microangiopathy, swelling of nerve fibers, visual field abnormalities and visual functional impairments had been reported in Leber's hereditary optic neuropathy (LHON) carriers. The purpose of this study was to investigate microstructural changes of brain white matter in asymptomatic LHON carriers using DTI and tract-based spatial statistics (TBSS). METHODS: DTI and neuro-ophthalmologic measurements were acquired in 14 LHON carriers and 15 gender- and age-matched healthy controls, and diffusion metrics, including fractional anisotropy (FA), axial (AD), radial diffusion (RD) and mean diffusion (MD) were calculated. Intergroup differences in diffusion metrics were compared regressing out potential nuisance covariates of age and gender. A correlation analysis was performed to test associations between abnormal neuro-ophthalmologic measures and diffusion metrics while controlling the effects of age and gender. RESULTS: Compared to healthy controls, LHON carriers showed a weak increase of thickness of the retinal nerve fiber layer (RNFL) of the right inferior quadrant (F = 5.22, p = 0.032, before multiple comparison correction). LHON carriers exhibited widespread decreased FA value (bilateral anterior thalamic radiations, bilateral corticospinal tracts, major and minor forceps, bilateral inferior fronto-occipital fasciculi and left superior longitudinal fasciculus), increased RD value (bilateral anterior thalamic radiations, bilateral corticospinal tracts, major and minor forceps, bilateral inferior fronto-occipital fasciculi, bilateral inferior longitudinal fasciculi, bilateral superior longitudinal fasciculi and bilateral uncinate fasciculi) and increased MD value (bilateral anterior thalamic radiations, bilateral corticospinal tracts, minor forceps, bilateral inferior fronto-occipital fasciculi, bilateral inferior longitudinal fasciculi, left superior longitudinal fasciculus and bilateral uncinate fasciculi). Moreover, these changed diffusion metrics were not correlated with age, gender, LHON mutations and retinal measures in LHON carriers. CONCLUSION: Our results show microstructural alterations in brain white matter in asymptomatic LHON carriers, indicating that LHON-related genetic mutations themselves might result in occult white matter alterations in the brain.

Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation.

Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner. Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision. Multiple sampling rates of atrous convolution are concatenated to acquire different field-of-views of image features without adding additional number of parameters to avoid over fitting. Weighted loss function is also employed during training according to the proportion of the tumor pixels in the entire image, in order to weaken unbalanced classes problem. Qualitative and quantitative comparisons demonstrate that the proposed algorithm can achieve automatic tumor segmentation and has high segmentation precision for various size and shapes of tumor images without preprocessing and postprocessing.