Nomogram for prediction of hearing rehabilitation outcome in children with congenital sensorineural hearing loss after cochlear implantation.

Background: Reliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI. Methods: Children with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling. Results: The final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95 % CI = 0.772-0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model. Conclusions: The nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

A medical image segmentation method for rectal tumors based on multi-scale feature retention and multiple attention mechanisms.

BACKGROUND: With the continuous development of deep learning algorithms in the field of medical images, models for medical image processing based on convolutional neural networks have made great progress. Since medical images of rectal tumors are characterized by specific morphological features and complex edges that differ from natural images, achieving good segmentation results often requires a higher level of enrichment through the utilization of semantic features. PURPOSE: The efficiency of feature extraction and utilization has been improved to some extent through enhanced hardware arithmetic and deeper networks in most models. However, problems still exist with detail loss and difficulty in feature extraction, arising from the extraction of high-level semantic features in deep networks. METHODS: In this work, a novel medical image segmentation model has been proposed for Magnetic Resonance Imaging (MRI) image segmentation of rectal tumors. The model constructs a backbone architecture based on the idea of jump-connected feature fusion and solves the problems of detail feature loss and low segmentation accuracy using three novel modules: Multi-scale Feature Retention (MFR), Multi-branch Cross-channel Attention (MCA), and Coordinate Attention (CA). RESULTS: Compared with existing methods, our proposed model is able to segment the tumor region more effectively, achieving 97.4% and 94.9% in Dice and mIoU metrics, respectively, exhibiting excellent segmentation performance and computational speed. CONCLUSIONS: Our proposed model has improved the accuracy of both lesion region and tumor edge segmentation. In particular, the determination of the lesion region can help doctors identify the tumor location in clinical diagnosis, and the accurate segmentation of the tumor edge can assist doctors in judging the necessity and feasibility of surgery.

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics.

OBJECTIVES: To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). MATERIALS AND METHODS: In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. RESULTS: The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. CONCLUSION: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. CLINICAL RELEVANCE STATEMENT: The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. KEY POINTS: • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

Application of the magnetic resonance 3D multiecho Dixon sequence for quantifying hepatic iron overload and steatosis in patients with thalassemia.

OBJECTIVE: To investigate the feasibility and diagnostic efficacy of a 3D multiecho Dixon (qDixon) research application for simultaneously quantifying the liver iron concentration (LIC) and steatosis in thalassemia patients. MATERIALS AND METHODS: This prospective study enrolled participants with thalassemia who underwent 3 T MRI of the liver for the evaluation of hepatic iron overload. The imaging protocol including qDixon and conventional T2* mapping based on 2D multiecho gradient echo (ME GRE) sequences respectively. Regions of interest (ROIs) were drawn in the liver on the qDixon maps to obtain R2* and proton density fat fraction (PDFF). The reference R2* value was measured and calculated on conventional T2* mapping using the CMRtools software. Correlation analysis, Linear regression analysis, and Bland-Altman analysis were performed. RESULTS: 84 patients were finally included in this study. The median R2*-ME-GRE was 366.97 (1/s), range [206.68 (1/s), 522.20 (1/s)]. 8 patients had normal hepatic iron deposition, 16 had Insignificant, 42 had mild, 18 had moderate. The median of R2*-qDixon was 376.88 (1/s) [219.33 (1/s), 491.75 (1/s)]. A strong correlation was found between the liver R2*-qDixon and the R2*-ME-GRE (r = 0.959, P < 0.001). The median value of PDFF was 1.76% (1.10%, 2.95%). 8 patients had mild fatty liver, and 1 had severe fatty liver. CONCLUSION: MR qDixon research sequence can rapidly and accurately quantify liver iron overload, that highly consistent with the measured via conventional GRE sequence, and it can also simultaneously detect hepatic steatosis, this has great potential for clinical evaluation of thalassemia patients.

Radiomics Analysis of Diffusion Kurtosis Imaging: Distinguishing Between Glioblastoma and Single Brain Metastasis.

RATIONALE AND OBJECTIVES: This study aimed to assess the value of diffusion kurtosis imaging (DKI)-based radiomics models in differentiating glioblastoma (GB) from single brain metastasis (SBM) and compare their diagnostic performance with that of routine magnetic resonance imaging (MRI) models. MATERIALS AND METHODS: A total of 110 patients who underwent DKI and were pathologically diagnosed with GB (n = 58) or SBM (n = 52) were enrolled in this study. Radiomics features were extracted from the manually delineated region of interest of the lesion. A training set for model development was constructed from the images of 88 random patients, and 22 patients were reserved for independent validation. Seven single-DKI-parametric models and a multi-DKI-parametric model were constructed using six classifiers, whereas four single-routine-sequence models (based on T2 weighted imaging, apparent diffusion coefficient, T2-dark-fluid, and contrast-enhanced T1 magnetization prepared rapid gradient echo) and a multisequence routine MRI model were constructed for comparison. Receiver operating characteristic curve analysis was conducted to assess the diagnostic performance. The areas under the curve (AUCs) of different models were compared using the DeLong test. RESULTS: The AUCs of the single-DKI-parametric models ranged from 0.800 to 0.933 (mean kurtosis [MK] model). The multi-DKI-parametric model had a slightly higher AUC (0.958) than the MK model; however, the difference was not statistically significant (P = 0.688). In comparison, the AUCs of the routine MRI models ranged from 0.633 to 0.733 (multisequence routine MRI model). The AUC of the multi-DKI-parametric model was significantly higher than that of the multisequence routine MRI model (P = 0.042). CONCLUSION: The multi-DKI-parametric radiomics model exhibited better performance than that of the single-DKI-parametric models and routine MRI models in distinguishing GB from SBM.

Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis.

BACKGROUND: We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM). METHODS: We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison. RESULTS: NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model. CONCLUSION: Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.

The alleviating effect of ellagic acid on DSS-induced colitis via regulating gut microbiomes and gene expression of colonic epithelial cells.

The anti-inflammatory effect of ellagic acid (EA) and its possible underlying mechanism in dextran sulfate sodium (DSS)-induced mouse chronic colonic inflammation were studied. It was observed that EA administration significantly alleviated the colonic inflammation phenotypes, including decreasing the disease activity index (DAI), enhancing the body weight loss, and improving the shortened length of the colon and pathological damage of colon tissue. Additionally, EA reshaped the constitution of the gut microbiota by elevating the ratio of Bacteroidetes along with Bacteroides and Muribaculaceae, while decreasing the proportion of Firmicutes. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) revealed that the metabolic function of the gut microbiota was also changed. Furthermore, mouse colon transcriptome analysis showed that the tight junction and peroxisome proliferator-activated receptor (PPAR) signaling pathways were activated and the expressions of related genes were upregulated after EA intervention. These results showed that EA could remodel the gut bacterial composition, change the intestinal epithelial cell gene expressions in mice, and consequently improve the colonic inflammatory symptoms.

Preoperative prediction of VETC in hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging at high b values: a pilot study.

Background: Vessels encapsulating tumor clusters (VETC) have been considered an important cause of hepatocellular carcinoma (HCC) metastasis. Purpose: To compare the potential of various diffusion parameters derived from the monoexponential model and four non-Gaussian models (DKI, SEM, FROC, and CTRW) in preoperatively predicting the VETC of HCC. Methods: 86 HCC patients (40 VETC-positive and 46 VETC-negative) were prospectively enrolled. Diffusion-weighted images were acquired using six b-values (range from 0 to 3000 s/mm2). Various diffusion parameters derived from diffusion kurtosis (DK), stretched-exponential (SE), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, together with the conventional apparent diffusion coefficient (ADC) derived from the monoexponential model were calculated. All parameters were compared between VETC-positive and VETC-negative groups using an independent sample t-test or Mann-Whitney U test, and then the parameters with significant differences between the two groups were combined to establish a predictive model by binary logistic regression. Receiver operating characteristic (ROC) analyses were used to assess diagnostic performance. Results: Among all studied diffusion parameters, only DKI_K and CTRW_α significantly differed between groups (P=0.002 and 0.004, respectively). For predicting the presence of VETC in HCC patients, the combination of DKI_K and CTRW_α had the larger area under the ROC curve (AUC) than the two parameters individually (AUC=0.747 vs. 0.678 and 0.672, respectively). Conclusion: DKI_K and CTRW_α outperformed traditional ADC for predicting the VETC of HCC.

Generalizability and Diagnostic Performance of AI Models for Thyroid US.

Background Artificial intelligence (AI) models have improved US assessment of thyroid nodules; however, the lack of generalizability limits the application of these models. Purpose To develop AI models for segmentation and classification of thyroid nodules in US using diverse data sets from nationwide hospitals and multiple vendors, and to measure the impact of the AI models on diagnostic performance. Materials and Methods This retrospective study included consecutive patients with pathologically confirmed thyroid nodules who underwent US using equipment from 12 vendors at 208 hospitals across China from November 2017 to January 2019. The detection, segmentation, and classification models were developed based on the subset or complete set of images. Model performance was evaluated by precision and recall, Dice coefficient, and area under the receiver operating characteristic curve (AUC) analyses. Three scenarios (diagnosis without AI assistance, with freestyle AI assistance, and with rule-based AI assistance) were compared with three senior and three junior radiologists to optimize incorporation of AI into clinical practice. Results A total of 10 023 patients (median age, 46 years [IQR 37-55 years]; 7669 female) were included. The detection, segmentation, and classification models had an average precision, Dice coefficient, and AUC of 0.98 (95% CI: 0.96, 0.99), 0.86 (95% CI: 0.86, 0.87), and 0.90 (95% CI: 0.88, 0.92), respectively. The segmentation model trained on the nationwide data and classification model trained on the mixed vendor data exhibited the best performance, with a Dice coefficient of 0.91 (95% CI: 0.90, 0.91) and AUC of 0.98 (95% CI: 0.97, 1.00), respectively. The AI model outperformed all senior and junior radiologists (P < .05 for all comparisons), and the diagnostic accuracies of all radiologists were improved (P < .05 for all comparisons) with rule-based AI assistance. Conclusion Thyroid US AI models developed from diverse data sets had high diagnostic performance among the Chinese population. Rule-based AI assistance improved the performance of radiologists in thyroid cancer diagnosis. © RSNA, 2023 Supplemental material is available for this article.

Value for combination of T1WI star-VIBE with TWIST-VIBE dynamic contrast-enhanced MRI in distinguishing lung nodules. / T1WI star-VIBE增强结合TWIST-VIBE动态增强MRI对肺结节诊断的应用价值（英文）.

OBJECTIVES: With the increasing detection rate of lung nodules, the qualitative problem of lung nodules has become one of the key clinical issues. This study aims to evaluate the value of combining dynamic contrast-enhanced (DCE) MRI based on time-resolved imaging with interleaved stochastic trajectories-volume interpolated breath hold examination (TWIST-VIBE) with T1 weighted free-breathing star-volumetric interpolated breath hold examination (T1WI star-VIBE) in identifying benign and malignant lung nodules. METHODS: We retrospectively analyzed 79 adults with undetermined lung nodules before the operation. All nodules of patients included were classified into malignant nodules (n=58) and benign nodules (n=26) based on final diagnosis. The unenhanced T1WI-VIBE, the contrast-enhanced T1WI star-VIBE, and the DCE curve based on TWIST-VIBE were performed. The corresponding qualitative [wash-in time, wash-out time, time to peak (TTP), arrival time (AT), positive enhancement integral (PEI)] and quantitative parameters [volume transfer constant (Ktrans), interstitium-to-plasma rate constant (Kep), and fractional extracellular space volume (Ve)] were evaluated. Besides, the diagnostic efficacy (sensitivity and specificity) of enhanced CT and MRI were compared. RESULTS: There were significant differences in unenhanced T1WI-VIBE hypo-intensity, and type of A, B, C DCE curve type between benign and malignant lung nodules (all P<0.001). Pulmonary malignant nodules had a shorter wash-out time than benign nodules (P=0.001), and the differences of the remaining parameters were not statistically significant (all P>0.05). After T1WI star-VIBE contrast-enhanced MRI, the image quality was further improved. Compared with enhanced CT scan, the sensitivity (82.76% vs 80.50%) and the specificity (69.23% vs 57.10%) based on MRI were higher than that of CT (both P<0.001). CONCLUSIONS: T1WI star-VIBE and dynamic contrast-enhanced MRI based on TWIST-VIBE were helpful to improve the image resolution and provide more information for clinical differentiation between benign and malignant lung nodules.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes.

Objective: To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models. Materials and Methods: A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations. Results: The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups. Conclusions: Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.

MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

Objective: This study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC). Methods: This retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training (n = 91) and testing (n = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics-clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model. Results: Six texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306-0.9939] and 0.904 (95% CI, 0.8431-0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841-1.0000) and 0.891 (95% CI, 0.7903-0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect. Conclusion: The radiomics-clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

Time-course effects and mechanisms of hypobaric hypoxia on nervous system in mice.

OBJECTIVE: The aim of this study was to investigate the effect of time course on neurological impairment after acute hypobaric hypoxia exposure in mice and clarify the mechanism of acclimatization, so as to provide a suitable mice model and identify potential target against hypobaric hypoxia for further drug research. METHOD: Male C57BL/6J mice were exposed to hypobaric hypoxia at a simulated altitude of 7000 m for 1, 3, and 7 days (1HH, 3HH and 7HH respectively). The behavior of the mice was evaluated by novel object recognition (NOR) and morris water maze test (MWM), then, the pathological changes of mice brain tissues were observed by H&E and Nissl staining. In addition, RNA sequencing (RNA-Seq) was performed to characterize the transcriptome signatures, and enzyme-linked immunosorbent assay (ELISA), Real-time polymerase chain reaction (RT-PCR), and western blot (WB) were used to verify the mechanisms of neurological impairment induced by hypobaric hypoxia. RESULT: The hypobaric hypoxia condition resulted in impaired learning and memory, decreased new object cognitive index, and increased escape latency to the hidden platform in mice, with significant changes seen in the 1HH and 3HH groups. Bioinformatic analysis of RNA-seq results of hippocampal tissue showed that 739 differentially expressed genes (DEGs) appeared in the 1HH group, 452 in the 3HH group, and 183 in the 7HH group compared to the control group. There were 60 key genes overlapping in three groups which represented persistent changes and closely related biological functions and regulatory mechanisms in hypobaric hypoxia-induced brain injuries. DEGs enrichment analysis showed that hypobaric hypoxia-induced brain injuries were associated with oxidative stress, inflammatory responses, and synaptic plasticity. ELISA and WB results confirmed that these responses occurred in all hypobaric hypoxic groups while attenuated in the 7HH group. VEGF-A-Notch signaling pathway was enriched by DEGs in hypobaric hypoxia groups and was validated by RT-PCR and WB. CONCLUSION: The nervous system of mice exposed to hypobaric hypoxia exhibited stress followed by gradual habituation and thus acclimatization over time, which was reflected in the biological mechanism involving inflammation, oxidative stress, and synaptic plasticity, and accompanied by activation of the VEGF-A-Notch pathway.

Potential Value of the Stretched Exponential and Fractional Order Calculus Model in Discriminating Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: an Animal Experiment of Orthotopic Xenograft Nude Mice.

BACKGROUND: In clinical practice, Preoperative differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma is challenging but critical for treatment decisions. OBJECTIVE: This study investigated the discriminatory power of the stretched-exponential model and fractional-order calculus model parameters for hepatocellular carcinoma versus intrahepatic cholangiocarcinoma in orthotopic xenograft nude mice. METHODS: Prototype orthotopic xenograft models of hepatocellular carcinoma and intrahepatic cholangiocarcinoma were developed using 20 nude mice divided into two groups and separately transplanted with MHCC97H and HUCCT1 cells. Readout-segmented diffusion-weighted imaging with multiple b-values (0-2000 s/mm2) was obtained using a 3.0-T magnetic resonance imaging scanner. The apparent diffusion coefficient was calculated using the mono-exponential model. The distributed diffusion coefficient and intravoxel water molecular diffusion heterogeneity (α) were calculated using the stretched-exponential model. The diffusion coefficient (D), fractional-order derivative in space (ß), and spatial parameter (µ) were calculated using the fractional-order calculus model. The liver and tumor specimens of nude mice were immunostained after euthanasia to clarify the liver cancer type. Differences in diffusion-related parameters between the groups were evaluated using Mann-Whitney U-test and univariate logistic analysis. Receiver operating characteristic curves were used to assess the diagnostic efficacy of each parameter. P<0.05 was deemed significant. RESULTS: α, D, and ß were significant discriminators between the groups. The area under the curve for these three variables was 0.890, 0.830, and 0.870, respectively, with cutoff values of 0.491, 0.435, and 0.782, respectively. CONCLUSION: The stretched-exponential model parameters α and the fractional-order calculus model parameters D and ß showed high diagnostic efficacy in discriminating intrahepatic cholangiocarcinoma from hepatocellular carcinoma in orthotopic xenograft nude mouse models.

The role of chemerin in the regulation of cGAS-STING pathway in gestational diabetes mellitus placenta.

Recent studies already confirmed that placenta mitochondrial dysfunction is associated with the progression of gestational diabetes mellitus (GDM). Besides, a possible relationship between adipokine chemerin and disulfide-bond A oxidoreductase-like protein (DsbA-L) had been revealed, whereas the potential interaction remains unclear. In addition, very little is still known about the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway and its mechanisms of action in the context of GDM. The present study aims to investigate the underlying mechanism of cGAS-STING pathway and its regulatory relationship with chemerin in GDM. A total of 50 participants, including 25 cases of GDM patients and 25 pregnant women with normal glucose tolerance, were enrolled, and their placenta tissues at term labor were collected. Besides, an insulin resistance cell model was established on the human trophoblastic cell line to explore the molecular mechanism of chemerin on cGAS-STING pathway. Results showed that there were mitochondrial pathological changes in GDM placenta, accompanied by the decreased expression of DsbA-L, increased level of chemerin, and the activation of cGAS-STING pathway. In the insulin resistant cell model, overexpression of chemerin upregulated protein expression of DsbA-L, and recombinant chemerin presented time-dependent inhibition on the cGAS-STING pathway, but this effect was not dependent on DsbA-L. In conclusion, elevated chemerin is probably a protective mechanism, which may be a potential therapeutic strategy for GDM.

Dual parallel net: A novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior.

Segmentation of rectal cancerous regions from Magnetic Resonance (MR) images can help doctor define the extent of the rectal cancer and judge the severity of rectal cancer, so rectal tumor segmentation is crucial to improve the accuracy of rectal cancer diagnosis. However, accurate segmentation of rectal cancerous regions remains a challenging task due to the shape of rectal tumor has significant variations and the tumor and surrounding tissue are indistinguishable. In addition, in the early research on rectal tumor segmentation, most deep learning methods were based on convolutional neural networks (CNNs), and traditional CNN have small receptive field, which can only capture local information while ignoring the global information of the image. Nevertheless, the global information plays a crucial role in rectal tumor segmentation, so traditional CNN-based methods usually cannot achieve satisfactory segmentation results. In this paper, we propose an encoder-decoder network named Dual Parallel Net (DuPNet), which fuses transformer and classical CNN for capturing both global and local information. Meanwhile, as for capture features at different scales as well as to avoid accuracy loss and parameters reduction, we design a feature adaptive block (FAB) in skip connection between encoder and decoder. Furthermore, in order to utilize the apriori information of rectal tumor shape effectively, we design a Gaussian Mixture prior and embed it in self-attention mechanism of the transformer, leading to robust feature representation and accurate segmentation results. We have performed extensive ablation experiments to verify the effectiveness of our proposed dual parallel encoder, FAB and Gaussian Mixture prior on the dataset from the Shanxi Cancer Hospital. In the experimental comparison with the state-of-the-art methods, our method achieved a Mean Intersection over Union (MIoU) of 89.34% on the test set. In addition to that, we evaluated the generalizability of our method on the dataset from Xinhua Hospital, the promising results verify the superiority of our method.

Multi-echo Dixon and breath-hold T2-corrected multi-echo single-voxel MRS for quantifying hepatic iron overload in rabbits.

BACKGROUND: Three-dimensional (3D) multi-echo-Dixon (ME-Dixon) and breath-hold T2-corrected multi-echo single-voxel MR spectroscopy (HISTO) can simultaneously quantify liver fat and liver iron. However, their diagnostic efficacy and application scope for quantitative iron in co-existing fatty liver have not been adequately evaluated. PURPOSE: To evaluate the accuracy of ME-Dixon and HISTO for quantitative analysis of hepatic iron in rabbits with iron deposition and fatty liver using liver-iron concentration (LIC) as a reference standard. MATERIAL AND METHODS: ME-Dixon, HISTO, and conventional two-dimensional multi-echo gradient echo (GRE) sequences were performed on 42 rabbits. The following parameters were calculated: R2* from ME-Dixon and GRE; proton density fat fraction (PDFF) from the ME-Dixon, HISTO (normal TE range), and HISTO-H (extended TE range); and R2_water from HISTO and HISTO-H. The LIC and liver-fat concentration (LFC) were measured through chemical analysis, and their relationship with the MRI parameters were assessed. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the diagnostic efficiency. RESULTS: LIC was significantly correlated with R2_HISTO-H, R2*_Dixon, and R2*_GRE (r = 0.858, 0.910, 0.931, respectively; P < 0.001) and weakly with R2_HISTO (r = 0.424; P = 0.008). A strong correlation was also observed between the LFC and PDFF obtained from HISTO, HISTO-H, and ME-Dixon (r = 0.776, 0.811, 0.888, respectively; P < 0.001). ME-Dixon showed the best performance with moderate iron overload (AUC = 0.983). CONCLUSION: 3D ME-Dixon is useful for quantifying the LIC, especially with co-existing fatty liver. Its diagnostic performance is also superior to that of the HISTO sequence.

In Vitro Investigation on the Effect of Dendrobine on the Activity of Cytochrome P450 Enzymes.

Dendrobine is the major active ingredient of Dendrobium nobile, Dendrobium chrysotoxum, and Dendrobium fimbriatum, all of which are used in traditional Chinese medicine owing to their antitumor and anti-inflammation activities. Hence, investigation on the interaction of dendrobine with cytochrome P450 enzymes could provide a reference for the clinical application of Dendrobium. The effects of dendrobine on cytochrome P450 enzymes activities were investigated in the presence of 0, 2.5, 5, 10, 25, 50, and 100 µM dendrobine in pooled human liver microsomes. The specific inhibitors were employed as the positive control and the blank groups were set as the negative control. The Lineweaver-Burk plots were plotted to characterize the specific inhibition model and obtain the kinetic parameters. The study reveals that dendrobine significantly inhibited the activity of CYP3A4, 2C19, and 2D6 with IC50 values of 12.72, 10.84, and 15.47 µM, respectively. Moreover, the inhibition of CYP3A4 was found to be noncompetitive (Ki = 6.41 µM) and time dependent (KI = 2.541 µM-1, Kinact = 0.0452 min-1), while the inhibition of CYP2C19 and 2D6 was found to be competitive with the Ki values of 5.22 and 7.78 µM, respectively, and showed no time-dependent trends. The in vitro inhibitory effect of dendrobine implies the potential drug-drug interaction between dendrobine and CYP3A4-, 2C9-, and 2D6-metabolized drugs. Nonetheless, these findings need further in vivo validation.

Histogram Analysis Based on Neurite Orientation Dispersion and Density MR Imaging for Differentiation Between Glioblastoma Multiforme and Solitary Brain Metastasis and Comparison of the Diagnostic Performance of Two ROI Placements.

BACKGROUND: Preoperative differentiation of glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) contributes to guide neurosurgical decision-making. PURPOSE: To explore the value of histogram analysis based on neurite orientation dispersion and density imaging (NODDI) in differentiating between GBM and SBM and comparison of the diagnostic performance of two region of interest (ROI) placements. STUDY TYPE: Retrospective. POPULATION: In all, 109 patients with GBM (n = 57) or SBM (n = 52) were enrolled. FIELD STRENGTH/SEQUENCE: A 3.0 T scanners. T2 -dark-fluid sequence, contrast-enhanced T1 magnetization-prepared rapid gradient echo sequence, and NODDI. ASSESSMENT: ROIs were placed on the peritumoral edema area (ROI1) and whole tumor area (ROI2, included the cystic, necrotic, and hemorrhagic areas). Histogram parameters of each isotropic volume fraction (ISOVF), intracellular volume fraction (ICVF), and orientation dispersion index (ODI) from NODDI images for two ROIs were calculated, respectively. STATISTICAL TESTS: Mann-Whitney U test, independent t-test, chi-square test, multivariate logistic regression analysis, DeLong's test. RESULTS: For the ROI1 and ROI2, the ICVFmin and ODImean obtained the highest area under curve (AUC, AUC = 0.741 and 0.750, respectively) compared to other single parameters, and the AUC of the multivariate logistic regression model was 0.851 and 0.942, respectively. DeLong's test revealed significant difference in diagnostic performance between optimal single parameter and multivariate logistic regression model within the same ROI, and the multivariate logistic regression models between two different ROIs. DATA CONCLUSION: The performance of multivariate logistic regression model is superior to optimal single parameter in both ROIs based on NODDI histogram analysis to distinguish SBM from GBM, and the ROI placed on the whole tumor area exhibited better diagnostic performance. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Multiple diffusion metrics in differentiating solid glioma from brain inflammation.

Background and purpose: The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models. Materials and methods: Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated. Results: 57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758). Conclusion: Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.