A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection.

Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS dataset and 0.7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.

Application of Spinal Subtraction and Bone Background Fusion CTA in the Accurate Diagnosis and Evaluation of Spinal Vascular Malformations.

BACKGROUND AND PURPOSE: Accurate pretreatment diagnosis and assessment of spinal vascular malformations using spinal CTA are crucial for patient prognosis, but the postprocessing reconstruction may not be able to fully depict the lesions due to the complexity inherent in spinal anatomy. Our purpose was to explore the application value of the spinal subtraction and bone background fusion CTA (SSBBF-CTA) technique in precisely depicting and localizing spinal vascular malformation lesions. MATERIALS AND METHODS: In this retrospective study, patients (between November 2017 and November 2022) with symptoms similar to those of spinal vascular malformations were divided into diseased (group A) and nondiseased (group B) groups. All patients underwent spinal CTA using Siemens dual-source CT. Multiplanar reconstruction; routine bone subtraction, and SSBBF-CTA images were obtained using the snygo.via and ADW4.6 postprocessing reconstruction workstations. Multiple observers researched the following 3 aspects: 1) preliminary screening capability using original images with multiplanar reconstruction CTA, 2) the accuracy and stability of the SSBBF-CTA postprocessing technique, and 3) diagnostic evaluation of spinal vascular malformations using the 3 types of postprocessing images. Diagnostic performance was analyzed using receiver operating characteristic analysis, while reader or image differences were analyzed using the Wilcoxon signed-rank test or the Kruskal-Wallis rank sum test. RESULTS: Forty-nine patients (groups A and B: 22 and 27 patients; mean ages, 44.0 [SD, 14.3] years and 44.6 [SD,15.2] years; 13 and 16 men) were evaluated. Junior physicians showed lower diagnostic accuracy and sensitivity using multiplanar reconstruction CTA (85.7% and 77.3%) than senior physicians (93.9% and 90.9%, 98% and 95.5%). Short-term trained juniors achieved SSBBF-CTA image accuracy similar to that of experienced physicians (P > .05). In terms of the visualization and localization of spinal vascular malformation lesions (nidus/fistula, feeding artery, and drainage vein), both multiplanar reconstruction and SSBBF-CTA outperformed routine bone subtraction CTA (P = .000). Compared with multiplanar reconstruction, SSBBF-CTA allowed less experienced physicians to achieve superior diagnostic capabilities (comparable with those of experienced radiologists) more rapidly (P < .05). CONCLUSIONS: The SSBBF-CTA technique exhibited excellent reproducibility and enabled accurate pretreatment diagnosis and assessment of spinal vascular malformations with high diagnostic efficiency, particularly for junior radiologists.

MnO2/Ce6 microbubble-mediated hypoxia modulation for enhancing sono-photodynamic therapy against triple negative breast cancer.

Sono-photodynamic therapy (SPDT) has emerged as a promising treatment modality for triple negative breast cancer (TNBC). However, the hypoxic tumor microenvironment hinders the application of SPDT. Herein, in this study, a multifunctional platform (MnO2/Ce6@MBs) was designed to address this issue. A sono-photosensitizer (Ce6) and a hypoxia modulator (MnO2) were loaded into microbubbles and precisely released within tumor tissues under ultrasound irradiation. MnO2in situ reacted with the excess H2O2 and H+ and produced O2 within the TNBC tumor, which alleviated hypoxia and augmented SPDT by increasing ROS generation. Meanwhile, the reaction product Mn2+ was able to achieve T1-weighted MRI for enhanced tumor imaging. Additionally, Ce6 and microbubbles served as a fluorescence imaging contrast agent and a contrast-enhanced ultrasound imaging agent, respectively. In in vivo anti-tumor studies, under the FL/US/MR imaging guidance, MnO2/Ce6@MBs combined with SPDT significantly reversed tumor hypoxia and inhibited tumor growth in 4T1-tumor bearing mice. This work presents a theragnostic system for reversing tumor hypoxia and enhancing TNBC treatment.

Correction: MnO2/Ce6 microbubble-mediated hypoxia modulation for enhancing sono-photodynamic therapy against triple negative breast cancer.

Correction for 'MnO2/Ce6 microbubble-mediated hypoxia modulation for enhancing sono-photodynamic therapy against triple negative breast cancer' by Ping Li et al., Biomater. Sci., 2024, https://doi.org/10.1039/d3bm00931a.

A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma.

Background: The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches. The adaption of segmentation architectures originally designed for road extraction for medical imaging represents an innovative step in this direction. By employing K-space data as the modal input, this method completely eliminates the information loss inherent in FFT, thereby potentially enhancing the precision and effectiveness of glioma diagnosis. Methods: In the study, a novel architecture based on a deep-residual U-net was developed to accomplish the challenging task of automatically segmenting brain tumors from K-space data. Brain tumors from K-space data with different under-sampling rates were also segmented to verify the clinical application of our method. Results: Compared to the benchmarks set in the 2018 Brain Tumor Segmentation (BraTS) Challenge, our proposed architecture had superior performance, achieving Dice scores of 0.8573, 0.8789, and 0.7765 for the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively. The corresponding Hausdorff distances were 2.5649, 1.6146, and 2.7187 for the WT, TC, and ET regions, respectively. Notably, compared to traditional image-based approaches, the architecture also exhibited an improvement of approximately 10% in segmentation accuracy on the K-space data at different under-sampling rates. Conclusions: These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the segmentation task to be accomplished more efficiently.

The CT and MRI features of benign calvarium and skull base osteoblastoma.

OBJECTIVE: We retrospectively reviewed the CT and MRI features of patients with benign osteoblastoma in the calvarium and skull base (CSBOB). METHODS: Nine cases of pathologically confirmed benign CSBOB were analysed retrospectively. The patients had undergone CT and/or MRI. Tumour location, size, and imaging features were reviewed and recorded. RESULTS: The patients included four males and five females with a mean age of 27.0 years (age 14-40 years). The tumours were located in the frontal bone in 3 patients, the occipital bone in 3 patients, and in the parietal bone, sphenoid bone, and skull base in 1 patient each. On CT, the tumours measured 5.1 ± 3.3 (1.8-8.4) cm. Seven tumours were shown to have caused expansile bony destruction with an eggshell appearance and varying degrees of calcification or matrix mineralization. Multiple septa were observed in 5 tumours. Intracranial growth was observed in 5 tumours. On MRI, 7 tumours showed heterogeneous hypo- to isointensity on T1WI. Heterogeneous high signal patterns with low signal rims and septa were observed in 6 tumours on T2WI, and 4 showed a fluid-fluid level. On contrast-enhanced imaging, 6 tumours showed peripheral and septal enhancement, and 2 showed the dural tail sign. CONCLUSIONS: Benign CSBOB is a rare tumour characterized by expansile bony destruction, septa, a sclerotic rim and calcification or matrix mineralization on CT and MRI. ADVANCES IN KNOWLEDGE: The findings from this study contribute to a better understanding of benign CSBOB and provide valuable imaging features that can aid in its diagnosis and differentiation from other tumours in the calvarium and skull base.

Demonstration of magnetic resonance Z-spectral imaging for fatty acid characterization of bone marrow at 3 T.

Magnetic resonance Z-spectral imaging (ZSI) has emerged as a new approach to measure fat fraction (FF). However, its feasibility for fat spectral imaging remains to be elucidated. In this study, a single-slice ZSI sequence dedicated to fat spectral imaging was designed, and its capability for fatty acid characterization was investigated on peanut oil samples, a multiple-vial fat-water phantom with varied oil volumes, and vertebral body marrow in healthy volunteers and osteoporosis patients at 3 T. The peanut oil spectrum was also recorded with a 400-MHz NMR spectrometer. A Gaussian-Lorentzian sum model was used to resolve water and six fat signals of the pure oil sample or four fat signals of the fat-water phantom or vertebral bone marrow from Z spectra. Fat peak amplitudes were normalized to the total peak amplitude of water and all fat signals. Normalized fat peak amplitudes and FF were quantified and compared among vials of the fat-water phantom or between healthy volunteers and osteoporosis patients. An unpaired student's t-test and Pearson's correlation were conducted, with p less than 0.05 considered statistically significant. The results showed that the peanut oil spectra measured with the ZSI technique were in line with respective NMR spectra, with amplitudes of the six fat signal peaks significantly correlated between the two methods (y = x + 0.001, r = 0.996, p < 0.001 under a repetition time of 1.6 s; and y = 1.026x - 0.003, r = 0.996, p < 0.001 under a repetition time of 3.1 s). Moreover, ZSI-measured FF exhibited a significant correlation with prepared oil volumes (y = 0.876x + 1.290, r = 0.996, p < 0.001). The osteoporosis patients showed significantly higher normalized fat peak amplitudes and FF in the L4 vertebral body marrow than the healthy volunteers (all p < 0.01). In summary, the designed ZSI sequence is feasible for fatty acid characterization, and has the potential to facilitate the diagnosis and evaluation of diseases associated with fat alterations at 3 T.

Fast and robust pulsed chemical exchange saturation transfer (CEST) MRI using a quasi-steady-state (QUASS) algorithm at 3 T.

Chemical exchange saturation transfer (CEST) has emerged as a powerful technique to image dilute labile protons. However, its measurement depends on the RF saturation duration (Tsat) and relaxation delay (Trec). Although the recently developed quasi-steady-state (QUASS) solution can reconstruct equilibrium CEST effects under continuous-wave RF saturation, it does not apply to pulsed-CEST MRI on clinical scanners with restricted hardware or specific absorption rate limits. This study proposed a QUASS algorithm for pulsed-CEST MRI and evaluated its performance in muscle CEST measurement. An approximated expression of a steady-state pulsed-CEST signal was incorporated in the off-resonance spin-lock model, from which the QUASS pulsed-CEST effect was derived. Numerical simulation, creatine phantom, and healthy volunteer scans were conducted at 3 T. The CEST effect was quantified with asymmetry analysis in the simulation and phantom experiments. CEST effects of creatine, amide proton transfer, phosphocreatine, and combined magnetization transfer and nuclear Overhauser effects were isolated from a multi-pool Lorentzian model in muscles. Apparent and QUASS CEST measurements were compared under different Tsat/Trec and duty cycles. Paired Student's t-test was employed with P < 0.05 as statistically significant. The simulation, phantom, and human studies showed the strong impact of Tsat/Trec on apparent CEST measurements, which were significantly smaller than the corresponding QUASS CEST measures, especially under short Tsat/Trec times. In comparison, the QUASS algorithm mitigates such impact and enables accurate CEST measurements under short Tsat/Trec times. In conclusion, the QUASS algorithm can accelerate robust pulsed-CEST MRI, promising the efficient detection and evaluation of muscle diseases in clinical settings.

Fatty Acids Composition of the Sacroiliac Joint in Axial Spondyloarthritis: Analysis Using 3.0 T Chemical Shift-Encoded MRI.

BACKGROUND: Axial spondyloarthritis (axSpA) is a group of inflammatory diseases that may lead to ankylosis of the sacroiliac joint and spine. Fat lesion in the sacroiliac joint is an important feature in diagnosis and disease progression of axSpA. However, whether there is alteration of fatty acids (FAs) composition has not been investigated using MRI. PURPOSE: To investigate bone marrow FA composition of the sacroiliac joint in patients with axSpA compared to controls. STUDY TYPE: Prospective. SUBJECTS: Eighty five participants (mean age, 32.3 ± 6.1 years): 48 axSpA (25 male, 23 female) and 37 non-SpA controls (18 male, 19 female). FIELD STRENGTH/SEQUENCE: 3.0 T/Two multiple gradient-echo chemical shift-encoded (CSE) MRI which differed only in echo times (TEs) were scanned consecutively. ASSESSMENT: Axial multi-echo CSE MRI was performed in the sacroiliac joints in vivo. Regions of interest (ROIs) were manually placed on subchondral bone with and without fat lesion in axSpA patients, and on subchondral bone without fat lesion in controls. FA composition was computed within the ROIs using a nonlinear least square method from literature. STATISTICAL TESTS: Intergroup comparisons were performed using t tests. RESULTS: In axSpA, male patients had significantly higher monounsaturated FA compared to controls in areas with fat lesion in the sacrum (+12%) and in the ilium (+9%), and in areas without fat lesion in the sacrum (+10%). Significantly lower polyunsaturated FAs were found in areas with fat lesion in the sacrum (-10%) and ilium (-11%), and lower saturated FAs were found in areas without fat lesion in the sacrum (-6%). In female, patients with axSpA had significantly higher saturated FAs in areas with fat lesion in the ilium (+7%) in comparison to controls. DATA CONCLUSION: FA composition of the sacroiliac joint alters in patients with axSpA, and it can be detected using CSE MRI based analysis.

Altered dynamic functional network connectivity in drug-naïve Parkinson's disease patients with excessive daytime sleepiness.

Background: Excessive daytime sleepiness (EDS) is a frequent nonmotor symptoms of Parkinson's disease (PD), which seriously affects the quality of life of PD patients and exacerbates other nonmotor symptoms. Previous studies have used static analyses of these resting-state functional magnetic resonance imaging (rs-fMRI) data were measured under the assumption that the intrinsic fluctuations during MRI scans are stationary. However, dynamic functional network connectivity (dFNC) analysis captures time-varying connectivity over short time scales and may reveal complex functional tissues in the brain. Purpose: To identify dynamic functional connectivity characteristics in PD-EDS patients in order to explain the underlying neuropathological mechanisms. Methods: Based on rs-fMRI data from 16 PD patients with EDS and 41 PD patients without EDS, we applied the sliding window approach, k-means clustering and independent component analysis to estimate the inherent dynamic connectivity states associated with EDS in PD patients and investigated the differences between groups. Furthermore, to assess the correlations between the altered temporal properties and the Epworth sleepiness scale (ESS) scores. Results: We found four distinct functional connectivity states in PD patients. The patients in the PD-EDS group showed increased fractional time and mean dwell time in state IV, which was characterized by strong connectivity in the sensorimotor (SMN) and visual (VIS) networks, and reduced fractional time in state I, which was characterized by strong positive connectivity intranetwork of the default mode network (DMN) and VIS, while negative connectivity internetwork between the DMN and VIS. Moreover, the ESS scores were positively correlated with fraction time in state IV. Conclusion: Our results indicated that the strong connectivity within and between the SMN and VIS was characteristic of EDS in PD patients, which may be a potential marker of pathophysiological features related to EDS in PD patients.

Classification of asymmetry in mammography via the DenseNet convolutional neural network.

Purpose: To investigate the effectiveness of a deep learning system based on the DenseNet convolutional neural network in diagnosing benign and malignant asymmetric lesions in mammography. Methods: Clinical and image data from 460 women aged 23-82 years (47.57 ± 8.73 years) with asymmetric lesions who underwent mammography at Shenzhen People's Hospital, Shenzhen Luohu District People's Hospital, and Shenzhen Hospital of Peking University from December 2019 to December 2020 were retrospectively analyzed. Two senior radiologists, two junior radiologists, and the DL system read the mammographic images of 460 patients, respectively, and finally recorded the BI-RADS classification of asymmetric lesions. We then used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the diagnostic efficacy and the difference between AUCs by the Delong method. Results: Specificity (0.909 vs. 0.835, 0.790, χ2=8.21 and 17.22, p＜0.05) and precision (0.872 vs. 0.763, 0.726, χ2=9.23 and 5.22, p＜0.05) of the DL system in the diagnosis of benign and malignant asymmetric lesions were higher than those of junior radiologist A and B, and there was a statistically significant difference between AUCs (0.778 vs. 0.579, 0.564, Z = 4.033 and 4.460, p＜0.05). Furthermore, the AUC (0.778 vs. 0.904, 0.862, Z = 3.191, and 2.167, p＜0.05) of benign and malignant asymmetric lesions diagnosed by the DL system was lower than that of senior radiologist A and senior radiologist B. Conclusions: The DL system based on the DenseNet convolution neural network has high diagnostic efficiency, which can help junior radiologists evaluate benign and malignant asymmetric lesions more accurately. It can also improve diagnostic accuracy and reduce missed diagnoses caused by inexperienced junior radiologists.

HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer.

BACKGROUND: High HLA-DQA1 expression is associated with a better prognosis in many cancers. However, the association between HLA-DQA1 expression and prognosis of breast cancer and the noninvasive assessment of HLA-DQA1 expression are still unclear. This study aimed to reveal the association and investigate the potential of radiomics to predict HLA-DQA1 expression in breast cancer. METHODS: In this retrospective study, transcriptome sequencing data, medical imaging data, clinical and follow-up data were downloaded from the TCIA ( https://www.cancerimagingarchive.net/ ) and TCGA ( https://portal.gdc.cancer.gov/ ) databases. The clinical characteristic differences between the high HLA-DQA1 expression group (HHD group) and the low HLA-DQA1 expression group were explored. Gene set enrichment analysis, Kaplan‒Meier survival analysis and Cox regression were performed. Then, 107 dynamic contrast-enhanced magnetic resonance imaging features were extracted, including size, shape and texture. Using recursive feature elimination and gradient boosting machine, a radiomics model was established to predict HLA-DQA1 expression. Receiver operating characteristic (ROC) curves, precision-recall curves, calibration curves, and decision curves were used for model evaluation. RESULTS: The HHD group had better survival outcomes. The differentially expressed genes in the HHD group were significantly enriched in oxidative phosphorylation (OXPHOS) and estrogen response early and late signalling pathways. The radiomic score (RS) output from the model was associated with HLA-DQA1 expression. The area under the ROC curves (95% CI), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the radiomic model were 0.866 (0.775-0.956), 0.825, 0.939, 0.7, 0.775, and 0.913 in the training set and 0.780 (0.629-0.931), 0.659, 0.81, 0.5, 0.63, and 0.714 in the validation set, respectively, showing a good prediction effect. CONCLUSIONS: High HLA-DQA1 expression is associated with a better prognosis in breast cancer. Quantitative radiomics as a noninvasive imaging biomarker has potential value for predicting HLA-DQA1 expression.

Synthetic high-energy computed tomography image via a Wasserstein generative adversarial network with the convolutional block attention module.

Background: Computed tomography (CT) is now universally applied into clinical practice with its non-invasive quality and reliability for lesion detection, which highly improves the diagnostic accuracy of patients with systemic diseases. Although low-dose CT reduces X-ray radiation dose and harm to the human body, it inevitably produces noise and artifacts that are detrimental to information acquisition and medical diagnosis for CT images. Methods: This paper proposes a Wasserstein generative adversarial network (WGAN) with a convolutional block attention module (CBAM) to realize a method of directly synthesizing high-energy CT (HECT) images through low-energy scanning, which greatly reduces X-ray radiation from high-energy scanning. Specifically, our proposed generator structure in WGAN consists of Visual Geometry Group Network (Vgg16), 9 residual blocks, upsampling and CBAM, a subsequent attention block. The convolutional block attention module is integrated into the generator for improving the denoising ability of the network as verified by our ablation comparison experiments. Results: Experimental results of the generator attention module ablation comparison indicate an optimization boost to the overall generator model, obtaining the synthesized high-energy CT with the best metric and denoising effect. In different methods comparison experiments, it can be clearly observed that our proposed method is superior in the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and most of the statistics (average CT value and its standard deviation) compared to other methods. Because P<0.05, the samples are significantly different. The data distribution at the pixel level between the images synthesized by the method in this paper and the high-energy CT images is also most similar. Conclusions: Experimental results indicate that CBAM is able to suppress the noise and artifacts effectively and suggest that the image synthesized by the proposed method is closest to the high-energy CT image in terms of visual perception and objective evaluation metrics.

Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model.

Background: Recent reports have shown the potential for deep learning (DL) models to automatically segment of Couinaud liver segments and future liver remnant (FLR) for liver resections. However, these studies have mainly focused on the development of the models. Existing reports lack adequate validation of these models in diverse liver conditions and thorough evaluation using clinical cases. This study thus aimed to develop and perform a spatial external validation of a DL model for the automated segmentation of Couinaud liver segments and FLR using computed tomography (CT) in various liver conditions and to apply the model prior to major hepatectomy. Methods: This retrospective study developed a 3-dimensional (3D) U-Net model for the automated segmentation of Couinaud liver segments and FLR on contrast-enhanced portovenous phase (PVP) CT scans. Images were obtained from 170 patients from January 2018 to March 2019. First, radiologists annotated the Couinaud segmentations. Then, a 3D U-Net model was trained in Peking University First Hospital (n=170) and tested in Peking University Shenzhen Hospital (n=178) in cases with various liver conditions (n=146) and in candidates for major hepatectomy (n=32). The segmentation accuracy was evaluated using the dice similarity coefficient (DSC). Quantitative volumetry to evaluate the resectability was compared between manual and automated segmentation. Results: The DSC in the test data sets 1 and 2 for segments I to VIII was 0.93±0.01, 0.94±0.01, 0.93±0.01, 0.93±0.01, 0.94±0.00, 0.95±0.00, 0.95±0.00, and 0.95±0.00, respectively. The mean automated FLR and FLR% assessments were 493.51±284.77 mL and 38.53%±19.38%, respectively. The mean manual FLR and FLR% assessments were 500.92±284.38 mL and 38.35%±19.14%, respectively, in test data sets 1 and 2. For test data set 1, when automated segmentation of the FLR% was used, 106, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively; however, when manual segmentation of the FLR% was used, 107, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively. For test data set 2, all cases were categorized as candidates for major hepatectomy when automated and manual segmentation of the FLR% was used. No significant differences in FLR assessment (P=0.50; U=185,545), FLR% assessment (P=0.82; U=188,337), or the indications for major hepatectomy were noted between automated and manual segmentation (McNemar test statistic 0.00; P>0.99). Conclusions: The DL model could be used to fully automate the segmentation of Couinaud liver segments and FLR with CT prior to major hepatectomy in an accurate and clinically practicable manner.

Improved breast lesion detection in mammogram images using a deep neural network.

PURPOSE: This study aimed to investigate the effect of using a deep neural network (DNN) in breast cancer (BC) detection. METHODS: In this retrospective study, a DNN-based model was constructed from a total of 880 mammograms that 220 patients underwent between April and June 2020. The mammograms were reviewed by two senior and two junior radiologists with and without the aid of the DNN model. The performance of the network was assessed by comparing the area under the curve (AUC) and receiver operating characteristic curves for the detection of four features of malignancy (masses, calcifications, asymmetries, and architectural distortions), with and without the aid of the DNN model and by the senior and junior radiologists. Additionally, the effect of utilizing the DNN on diagnosis time for both the senior and junior radiologists was evaluated. RESULTS: The AUCs of the model for the detection of mass and calcification were 0.877 and 0.937, respectively. In the senior radiologist group, the AUC values for evaluation of mass, calcification, and asymmetric compaction were significantly higher with the DNN model than those obtained without the model. Similar effects were observed in the junior radiologist group, but the increase in the AUC values was even more dramatic. The median mammogram assessment time of the junior and senior radiologists was 572 (357-951) s, and 273.5 (129-469) s, respectively, with the DNN model, and the corresponding assessment time without the model, was 739 (445-1003) s and 321 (195-491) s, respectively. CONCLUSION: The DNN model exhibited high accuracy in detecting the four named features of BC and effectively shortened the review time by both senior and junior radiologists.

Nucleos(t)ide analogues altered quasispecies composition of hepatitis B virus (HBV)-resistant mutations in serum HBV DNA and serum HBV RNA.

Serum hepatitis B virus (HBV) RNA is a new serological indicator reflecting viral replication with good clinical application prospects. This study aimed to clarify the dynamic changes of serum HBV RNA levels and the quasispecies of HBV RNA virus-like particles in nucleos(t)ide analogues (NAs)-experienced chronic hepatitis B (CHB) patients harboring NAs-resistant mutations and their identifiable effects on NAs resistance. We included CHB patients who were on long-term NAs treatment and with HBV DNA rebound. The longitudinally dynamics of serum HBV RNA levels were quantitatively detected, and the quasispecies differences between serum HBV DNA and serum HBV RNA were compared by high-throughput sequencing. The effect of NAs concentration pressure on altering the resistance mutations quasispecies proportion of HBV DNA and HBV RNA in cell supernatant was analyzed in vitro. A total of 447 serum samples from 36 CHB patients treated with NAs were collected. The median follow-up period was 47 months (about 4 years), and the longest follow-up period was 117 months (about 10 years). Our results showed that HBV RNA could reflect virological breakthrough in 23 (64%, 23/36) patients, and serum HBV RNA rebound earlier than HBV DNA in 12 (52%, 12/23) patients. However, serum HBV RNA remained at a consistently high level and did not fluctuate significantly with the HBV DNA rebound in 6 of 36 patients. In addition, serum HBV RNA was not consistently detectable in 7 of the 36 patients, and their serum HBV RNA was undetectable even after HBV DNA had rebounded. The proportion of drug-resistant mutations in HBV DNA was higher than that of HBV RNA by high-throughput sequencing. The results of in vitro experiments showed that the viral strains with drug-resistant mutation in HBV DNA in cell supernatants gradually become the dominant strains with the increase of NAs concentrations. Serum HBV RNA levels can reflect virological breakthrough in most NAs- treated CHB patients, but there are certain limitations. NAs alter the quasispecies composition of serum HBV DNA and serum HBV RNA, resulting in a higher detection rate of drug-resistant mutations in serum HBV DNA than in serum HBV RNA.

Intermediate-weighted MRI with fat suppression (IW-FS): diagnostic performance for bone marrow edema and erosion detection in axial spondyloarthritis.

BACKGROUND: Bone marrow edema (BME) and erosion of the sacroiliac joint are both key lesions for diagnosing axial spondyloarthritis (axSpA) on magnetic resonance imaging (MRI). PURPOSE: To qualitatively and quantitatively compare intermediate-weighted MRI with fat suppression (IW-FS) with T2-weighted short tau inversion recovery (T2-STIR) in assessment of sacroiliac BME and erosion in axSpA. MATERIAL AND METHODS: Patients aged 18-60 years with axSpA were prospectively enrolled. All patients underwent a 3.0-T MRI examination of the sacroiliac joints. Para-coronal IW-FS, T2-STIR, and T1-weighted (T1W) images were acquired. BME and erosion were scored by two readers in consensus on IW-FS and STIR using a modified Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system. Consensus scores on T1WI were used as the reference for erosion. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for BME. RESULTS: In total, 49 patients (mean age=33.4 ± 7.6 years) were included. More patients were scored as having BME on T2-STIR (36 vs. 29, P = 0.016). SPARCC-BME score on IW-FS was lower than that acquired on T2-STIR (mean, 11.5 vs. 14.7, P = 0.002). SNR and CNR of BME were both lower on IW-FS than on T2-STIR (mean SNR, 118 vs. 218, P < 0.001; mean CNR, 44 vs. 137, P < 0.001). The sensitivity of erosion detection was higher on IW-FS (83%) than on T2-STIR (54%, P = 0.006). CONCLUSION: IW-FS is not sufficient for BME detection using T2-STIR as the reference standard in patients with axSpA. IW-FS has a much higher sensitivity than T2-STIR for erosion detection in the sacroiliac joint.

Evaluation of atrial anatomical remodeling in atrial fibrillation with machine-learned morphological features.

PURPOSE: To elucidate the role of atrial anatomical remodeling in atrial fibrillation (AF), we proposed an automatic method to extract and analyze morphological characteristics in left atrium (LA), left atrial appendage (LAA) and pulmonary veins (PVs) and constructed classifiers to evaluate the importance of identified features. METHODS: The LA, LAA and PVs were segmented from contrast computed tomography images using either a commercial software or a self-adaptive algorithm proposed by us. From these segments, geometric and fractal features were calculated automatically. To reduce the model complexity, a feature selection procedure is adopted, with the important features identified via univariable analysis and ensemble feature selection. The effectiveness of this approach is well illustrated by the high accuracy of our models. RESULTS: Morphological features, such as LAA ostium dimensions and LA volume and surface area, statistically distinguished ([Formula: see text]) AF patients or AF with LAA filling defects (AF(def+)) patients among all patients. On the test set, the best model to predict AF among all patients had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI, 0.8-1) and the best model to predict AF(def+) among all patients had an AUC of 0.92 (95% CI, 0.81-1). CONCLUSION: This study automatically extracted and analyzed atrial morphology in AF and identified atrial anatomical remodeling that statistically distinguished AF or AF(def+). The importance of identified atrial morphological features in characterizing AF or AF(def+) was validated by corresponding classifiers. This work provides a good foundation for a complete computer-assisted diagnostic workflow of predicting the occurrence of AF or AF(def+).

Proton density fat fraction (PDFF) maps aid fat metaplasia evaluation in the sacroiliac joints in additional to T1WI: Improved diagnostic accuracy in axial spondyloarthritis.

PURPOSE: To evaluate the added value of qualitative and quantitative fat metaplasia analysis using proton-density fat fraction (PDFF) map in additional to T1-weighted imaging (T1WI) of the sacroiliac joints (SIJ) for diagnosis of axial spondyloarthritis (axSpA). METHOD: Patients aged 18-45 years with axSpA were enrolled. Non-SpA patients and healthy volunteers were included as controls. All participants underwent 3.0T MRI of the SIJs including semi-coronal T1WI and semi-coronal chemical-shift encoded MRI sequence for generating PDFF map. Each joint was divided into four quadrants for analysis. Two independent readers scored fat metaplasia on T1WI alone or with additional PDFF map and measured PDFF values in different reading sessions. Using clinical diagnosis as the reference, diagnostic accuracy of visual scores and PDFF measurements was evaluated by area under the receiver operating characteristic curve (AUC). Inter-reader agreement was evaluated by the intra-class correlation coefficient (ICC). RESULTS: Forty-nine patients with axSpA and thirty-six controls were included. Qualitative fat metaplasia scores using additional PDFF map performed better than using T1WI alone (AUC: Reader 1, 0.847 vs 0.795, p = 0.082; Reader 2, 0.785 vs 0.719, p = 0.048). AUCs of quantitative analysis using number of quadrants with PDFF value ≥75 % were higher than qualitative analysis using T1WI alone (Reader 1, 0.863 vs 0.795, p = 0.046; Reader 2, 0.823 vs 0.785, p = 0.011). ICCs were 0.854 to 0.922 for qualitative analysis and 0.935 for quantitative analysis. CONCLUSIONS: Additional PDFF map can increase the diagnostic accuracy for axSpA by qualitative and quantitative fat metaplasia analysis, in comparison to using T1WI alone.