Intensive Ambulance-Delivered Blood-Pressure Reduction in Hyperacute Stroke.

BACKGROUND: Treatment of acute stroke, before a distinction can be made between ischemic and hemorrhagic types, is challenging. Whether very early blood-pressure control in the ambulance improves outcomes among patients with undifferentiated acute stroke is uncertain. METHODS: We randomly assigned patients with suspected acute stroke that caused a motor deficit and with elevated systolic blood pressure (≥150 mm Hg), who were assessed in the ambulance within 2 hours after the onset of symptoms, to receive immediate treatment to lower the systolic blood pressure (target range, 130 to 140 mm Hg) (intervention group) or usual blood-pressure management (usual-care group). The primary efficacy outcome was functional status as assessed by the score on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days after randomization. The primary safety outcome was any serious adverse event. RESULTS: A total of 2404 patients (mean age, 70 years) in China underwent randomization and provided consent for the trial: 1205 in the intervention group and 1199 in the usual-care group. The median time between symptom onset and randomization was 61 minutes (interquartile range, 41 to 93), and the mean blood pressure at randomization was 178/98 mm Hg. Stroke was subsequently confirmed by imaging in 2240 patients, of whom 1041 (46.5%) had a hemorrhagic stroke. At the time of patients' arrival at the hospital, the mean systolic blood pressure in the intervention group was 159 mm Hg, as compared with 170 mm Hg in the usual-care group. Overall, there was no difference in functional outcome between the two groups (common odds ratio, 1.00; 95% confidence interval [CI], 0.87 to 1.15), and the incidence of serious adverse events was similar in the two groups. Prehospital reduction of blood pressure was associated with a decrease in the odds of a poor functional outcome among patients with hemorrhagic stroke (common odds ratio, 0.75; 95% CI, 0.60 to 0.92) but an increase among patients with cerebral ischemia (common odds ratio, 1.30; 95% CI, 1.06 to 1.60). CONCLUSIONS: In this trial, prehospital blood-pressure reduction did not improve functional outcomes in a cohort of patients with undifferentiated acute stroke, of whom 46.5% subsequently received a diagnosis of hemorrhagic stroke. (Funded by the National Health and Medical Research Council of Australia and others; INTERACT4 ClinicalTrials.gov number, NCT03790800; Chinese Trial Registry number, ChiCTR1900020534.).

Positive benefit-risk ratio of Psoraleae Fructus: Comprehensive safety assessment and osteogenic effects in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Psoraleae Fructus (PF), the dried fruit of Psoralea corylifolia L., is a commonly used traditional medicine that has contributed to the treatment of orthopedic diseases for thousands of years in China. However, recent PF-related liver injury reports have drawn widespread attention regarding its potential hepatotoxicity risks. AIM OF THE STUDY: This study was aimed to evaluate the long-term efficacy and chronic toxicity of PF using a 26-week administration experiment on rats in order to simulate the clinical usage situation. MATERIALS AND METHODS: The PF aqueous extract was consecutively administrated to rats daily at dosages of 0.7, 2.0, and 5.6 g/kg (equivalent to 1-8 times the clinical doses for humans) for as long as 26 weeks. Samples were collected after 13, 26, and 32 weeks (withdrawal for 6 weeks) since the first administration. The chronic toxicity of PF was evaluated by conventional toxicological methods, and the efficacy of PF was evaluated by osteogenic effects in the natural growth process. RESULTS: In our experiments, only the H group (5.6 g/kg) for 26-week PF treatment demonstrated liver or kidney injury, which the injuries were reversible after 6 weeks of withdrawal. Notably, the PF treatment beyond 13 weeks showed significant benefits for bone growth and development in rats, with a higher benefit-risk ratio in female rats. CONCLUSIONS: PF displayed a promising benefit-risk ratio in the treatment and prevention of osteoporosis, a disease that lacks effective medicine so far. This is the first study to elucidate the benefit-risk balance associated with clinical dosage and long-term use of PF, thereby providing valuable insights for rational clinical use and risk control of PF.

Adhesion molecule-targeted magnetic particle imaging nanoprobe for visualization of inflammation in acute lung injury.

PURPOSE: Uncontrolled intra-alveolar inflammation is a central pathogenic feature, and its severity translates into a valid prognostic indicator of acute lung injury (ALI). Unfortunately, current clinical imaging approaches are unsuitable for visualizing and quantifying intra-alveolar inflammation. This study aimed to construct a small-sized vascular cell adhesion molecule-1 (VCAM-1)-targeted magnetic particle imaging (MPI) nanoprobe (ESPVPN) to visualize and accurately quantify intra-alveolar inflammation at the molecular level. METHODS: ESPVPN was engineered by conjugating a peptide (VHPKQHRGGSK(Cy7)GC) onto a polydopamine-functionalized superparamagnetic iron oxide core. The MPI performance, targeting, and biosafety of the ESPVPN were characterized. VCAM-1 expression in HUVECs and mouse models was evaluated by western blot. The degree of inflammation and distribution of VCAM-1 in the lungs were assessed using histopathology. The expression of pro-inflammatory markers and VCAM-1 in lung tissue lysates was measured using ELISA. After intravenous administration of ESPVPN, MPI and CT imaging were used to analyze the distribution of ESPVPN in the lungs of the LPS-induced ALI models. RESULTS: The small-sized (~10 nm) ESPVPN exhibited superior MPI performance compared to commercial MagImaging® and Vivotrax, and ESPVPN had effective targeting and biosafety. VCAM-1 was highly expressed in LPS-induced ALI mice. VCAM-1 expression was positively correlated with the LPS-induced dose (R = 0.9381). The in vivo MPI signal showed positive correlations with both VCAM-1 expression (R = 0.9186) and representative pro-inflammatory markers (MPO, TNF-α, IL-6, IL-8, and IL-1ß, R > 0.7). CONCLUSION: ESPVPN effectively targeted inflammatory lungs and combined the advantages of MPI quantitative imaging to visualize and evaluate the degree of ALI inflammation.

Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework.

Magnetic particle imaging (MPI) is an emerging medical imaging technique that has high sensitivity, contrast, and excellent depth penetration. In MPI, x-space is a reconstruction method that transforms the measured voltages into particle concentrations. The reconstructed native image can be modeled as a convolution of the magnetic particle concentration with a point-spread function (PSF). The PSF is one of the important parameters in deconvolution. However, accurately measuring or modeling the PSF in the hardware used for deconvolution is challenging due to the various environment and magnetic particle relaxation. The inaccurate PSF estimation may lead to the loss of the content structure of the MPI image, especially in low gradient fields. In this study, we developed a Dual Adversarial Network (DAN) with patch-wise contrastive constraint to deblur the MPI image. This method can overcome the limitations of unpaired data in data acquisition scenarios and remove the blur around the boundary more effectively than the common deconvolution method. We evaluated the performance of the proposed DAN model on simulated and real data. Experimental results confirmed that our model performs favorably against the deconvolution method that is mainly used for deblurring the MPI image and other GAN-based deep learning models.

A deep learning-based approach for emotional analysis of sports dance.

There is a phenomenon of attaching importance to technique and neglecting emotion in the training of sports dance (SP), which leads to the lack of integration between movement and emotion and seriously affects the training effect. Therefore, this article uses the Kinect 3D sensor to collect the video information of SP performers and obtains the pose estimation of SP performers by extracting the key feature points. The Arousal-Valence (AV) emotion model, based on the Fusion Neural Network model (FUSNN), is also combined with theoretical knowledge. It replaces long short term memory (LSTM) with gate recurrent unit (GRU), adds layer-normalization and layer-dropout, and reduces stack levels, and it is used to categorize SP performers' emotions. The experimental results show that the model proposed in this article can accurately detect the key points in the performance of SP performers' technical movements and has a high emotional recognition accuracy in the tasks of 4 categories and eight categories, reaching 72.3% and 47.8%, respectively. This study accurately detected the key points of SP performers in the presentation of technical movements and made a major contribution to the emotional recognition and relief of this group in the training process.

Cross-domain knowledge transfer based parallel-cascaded multi-scale attention network for limited view reconstruction in projection magnetic particle imaging.

Projection magnetic particle imaging (MPI) can significantly improve the temporal resolution of three-dimensional (3D) imaging compared to that using traditional point by point scanning. However, the dense view of projections required for tomographic reconstruction limits the scope of temporal resolution optimization. The solution to this problem in computed tomography (CT) is using limited view projections (sparse view or limited angle) for reconstruction, which can be divided into: completing the limited view sinogram and image post-processing for streaking artifacts caused by insufficient projections. Benefiting from large-scale CT datasets, both categories of deep learning-based methods have achieved tremendous progress; yet, there is a data scarcity limitation in MPI. We propose a cross-domain knowledge transfer learning strategy that can transfer the prior knowledge of the limited view learned by the model in CT to MPI, which can help reduce the network requirements for real MPI data. In addition, the size of the imaging target affects the scale of the streaking artifacts caused by insufficient projections. Therefore, we propose a parallel-cascaded multi-scale attention module that allows the network to adaptively identify streaking artifacts at different scales. The proposed method was evaluated on real phantom and in vivo mouse data, and it significantly outperformed several advanced limited view methods. The streaking artifacts caused by an insufficient number of projections can be overcome using the proposed method.

Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging.

Objective. Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI.Methods. AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data.Results. The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods.Significance. The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.

PGNet: Projection generative network for sparse-view reconstruction of projection-based magnetic particle imaging.

BACKGROUND: Magnetic particle imaging (MPI) is a novel tomographic imaging modality that scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time-consuming to scan multiview two-dimensional (2D) projections for three-dimensional (3D) reconstruction in projection MPI, such as computed tomography (CT). An intuitive idea is to use the sparse-view projections for reconstruction to improve the temporal resolution. Tremendous progress has been made toward addressing the sparse-view problem in CT, because of the availability of large data sets. For the novel tomography of MPI, to the best of our knowledge, studies on the sparse-view problem have not yet been reported. PURPOSE: The acquisition of multiview projections for 3D MPI imaging is time-consuming. Our goal is to only acquire sparse-view projections for reconstruction to improve the 3D imaging temporal resolution of projection MPI. METHODS: We propose to address the sparse-view problem in projection MPI by generating novel projections. The data set we constructed consists of three parts: simulation data set (including 3000 3D data), four phantoms data, and an in vivo mouse data. The simulation data set is used to train and validate the network, and the phantoms and in vivo mouse data are used to test the network. When the number of novel generated projections meets the requirements of filtered back projection, the streaking artifacts will be absent from MPI tomographic imaging. Specifically, we propose a projection generative network (PGNet), that combines an attention mechanism, adversarial training strategy, and a fusion loss function and can generate novel projections based on sparse-view real projections. To the best of our knowledge, we are the first to propose a deep learning method to attempt to overcome the sparse-view problem in projection MPI. RESULTS: We compare our method with several sparse-view methods on phantoms and in vivo mouse data and validate the advantages and effectiveness of our proposed PGNet. Our proposed PGNet enables the 3D imaging temporal resolution of projection MPI to be improved by 6.6 times, while significantly suppressing the streaking artifacts. CONCLUSION: We proposed a deep learning method operated in projection domain to address the sparse-view reconstruction of MPI, and the data scarcity problem in projection MPI reconstruction is alleviated by constructing a sparse-dense simulated projection data set. By our proposed method, the number of acquisitions of real projections can be reduced. The advantage of our method is that it prevents the generation of streaking artifacts at the source. Our proposed sparse-view reconstruction method has great potential for application to time-sensitive in vivo 3D MPI imaging.

Delayed Reaction of Radiation on the Central Nervous System and Bone System in C57BL/6J Mice.

The aim of this study was to provide a suitable mouse model of radiation-induced delayed reaction and identify potential targets for drug development related to the prevention and treatment of radiation injury. C57BL/6J mice were subjected to singular (109 cGy/min, 5 Gy*1) and fractional (109 cGy/min, 5 Gy*2) total body irradiation. The behavior and activity of mice were assessed 60 days after ionizing radiation (IR) exposure. After that, the pathological changes and mechanism of the mouse brain and femoral tissues were observed by HE, Nissl, Trap staining micro-CT scanning and RNA sequencing (RNA-Seq), and Western blot. The results show that singular or fractional IR exposure led to a decrease in spatial memory ability and activity in mice, and the cognitive and motor functions gradually recovered after singular 5 Gy IR in a time-dependent manner, while the fractional 10 Gy IR group could not recover. The decrease in bone density due to the increase in osteoclast number may be relative to the down-regulation of RUNX2, sclerostin, and beta-catenin. Meanwhile, the brain injury caused by IR exposure is mainly linked to the down-regulation of BNDF and Tau. IR exposure leads to memory impairment, reduced activity, and self-recovery, which are associated with time and dose. The mechanism of cognitive and activity damage was mainly related to oxidative stress and apoptosis induced by DNA damage. The damage caused by fractional 10 Gy TBI is relatively stable and can be used as a stable multi-organ injury model for radiation mechanism research and anti-radiation medicine screening.

Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study.

COVID-19 has been spreading continuously since its outbreak, and the detection of its manifestations in the lung via chest computed tomography (CT) imaging is essential to investigate the diagnosis and prognosis of COVID-19 as an indispensable step. Automatic and accurate segmentation of infected lesions is highly required for fast and accurate diagnosis and further assessment of COVID-19 pneumonia. However, the two-dimensional methods generally neglect the intraslice context, while the three-dimensional methods usually have high GPU memory consumption and calculation cost. To address these limitations, we propose a two-stage hybrid UNet to automatically segment infected regions, which is evaluated on the multicenter data obtained from seven hospitals. Moreover, we train a 3D-ResNet for COVID-19 pneumonia screening. In segmentation tasks, the Dice coefficient reaches 97.23% for lung segmentation and 84.58% for lesion segmentation. In classification tasks, our model can identify COVID-19 pneumonia with an area under the receiver-operating characteristic curve value of 0.92, an accuracy of 92.44%, a sensitivity of 93.94%, and a specificity of 92.45%. In comparison with other state-of-the-art methods, the proposed approach could be implemented as an efficient assisting tool for radiologists in COVID-19 diagnosis from CT images.

Deep learning for improving the spatial resolution of magnetic particle imaging.

Objective.Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast.Approach.Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings.Main results.We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two.Significance.This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.

Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network.

Stripe artifacts can deteriorate the quality of light sheet fluorescence microscopy (LSFM) images. Owing to the inhomogeneous, high-absorption, or scattering objects located in the excitation light path, stripe artifacts are generated in LSFM images in various directions and types, such as horizontal, anisotropic, or multidirectional anisotropic. These artifacts severely degrade the quality of LSFM images. To address this issue, we proposed a new deep-learning-based approach for the elimination of stripe artifacts. This method utilizes an encoder-decoder structure of UNet integrated with residual blocks and attention modules between successive convolutional layers. Our attention module was implemented in the residual blocks to learn useful features and suppress the residual features. The proposed network was trained and validated by generating three different degradation datasets with different types of stripe artifacts in LSFM images. Our method can effectively remove different stripes in generated and actual LSFM images distorted by stripe artifacts. Besides, quantitative analysis and extensive comparison results demonstrated that our method performs the best compared with classical image-based processing algorithms and other powerful deep-learning-based destriping methods for all three generated datasets. Thus, our method has tremendous application prospects to LSFM, and its use can be easily extended to images reconstructed by other modalities affected by the presence of stripe artifacts.

Structure attention co-training neural network for neovascularization segmentation in intravascular optical coherence tomography.

PURPOSE: To development and validate a neovascularization (NV) segmentation model in intravascular optical coherence tomography (IVOCT) through deep learning methods. METHODS AND MATERIALS: A total of 1950 2D slices of 70 IVOCT pullbacks were used in our study. We randomly selected 1273 2D slices from 44 patients as the training set, 379 2D slices from 11 patients as the validation set, and 298 2D slices from the last 15 patients as the testing set. Automatic NV segmentation is quite challenging, as it must address issues of speckle noise, shadow artifacts, high distribution variation, etc. To meet these challenges, a new deep learning-based segmentation method is developed based on a co-training architecture with an integrated structural attention mechanism. Co-training is developed to exploit the features of three consecutive slices. The structural attention mechanism comprises spatial and channel attention modules and is integrated into the co-training architecture at each up-sampling step. A cascaded fixed network is further incorporated to achieve segmentation at the image level in a coarse-to-fine manner. RESULTS: Extensive experiments were performed involving a comparison with several state-of-the-art deep learning-based segmentation methods. Moreover, the consistency of the results with those of manual segmentation was also investigated. Our proposed NV automatic segmentation method achieved the highest correlation with the manual delineation by interventional cardiologists (the Pearson correlation coefficient is 0.825). CONCLUSION: In this work, we proposed a co-training architecture with an integrated structural attention mechanism to segment NV in IVOCT images. The good agreement between our segmentation results and manual segmentation indicates that the proposed method has great potential for application in the clinical investigation of NV-related plaque diagnosis and treatment.

Profiling the responsiveness of focal adhesions of human cardiomyocytes to extracellular dynamic nano-topography.

Focal adhesion complexes function as the mediators of cell-extracellular matrix interactions to sense and transmit the extracellular signals. Previous studies have demonstrated that cardiomyocyte focal adhesions can be modulated by surface topographic features. However, the response of focal adhesions to dynamic surface topographic changes remains underexplored. To study this dynamic responsiveness of focal adhesions, we utilized a shape memory polymer-based substrate that can produce a flat-to-wrinkle surface transition triggered by an increase of temperature. Using this dynamic culture system, we analyzed three proteins (paxillin, vinculin and zyxin) from different layers of the focal adhesion complex in response to dynamic extracellular topographic change. Hence, we quantified the dynamic profile of cardiomyocyte focal adhesion in a time-dependent manner, which provides new understanding of dynamic cardiac mechanobiology.

A Clinical Analysis of the Treatment of Chronic Coronary Artery Occlusion With Antegrade Dissection Reentry.

Objective: This study aimed to investigate the efficacy and safety of antegrade dissection re-entry (ADR) technique in the percutaneous coronary intervention (PCI) to open chronic total occlusion (CTO) lesions. Methods: The baseline, angiographic results, PCI success rate, and major adverse cardiac events (MACE) during the 12 months of follow-up were compared between 48 patients who did not use ADR in the treatment of CTO lesions (control group) and 50 patients who used ADR (treatment group). Results: The control group comprised 48 patients who had 52 CTO lesions, and the treatment group comprised 50 patients who had 58 CTO lesions. The success rate of PCI in the treatment group (89.7 vs. 71.2%, P = 0.047) was significantly higher than in the control group, where six patients had in-stent restenosis (ISR, ISR-CTO) that were all recanalized. The mean PCI time (71 ± 25 min vs. 95 ± 33 min, P = 0.041), X-ray exposure time (42 ± 17 min vs. 71 ± 22 min, P = 0.032), contrast agent dosage (98 ± 26 ml vs. 178 ± 63 ml, P = 0.029), MACE incidence during the 12 months of follow-up (22.0 vs. 41.7%, P = 0.046) and recurrent myocardial infarction incidence (10.0 vs. 27.1%, P = 0.047) were significantly lower in the treatment group than in the control group. The differences were all statistically significant. Conclusion: It is safe and effective to use the ADR technique in PCI for coronary artery CTO lesions. The technique shortens the operation time, reduces the radiation dose of doctors and patients and the use dose of contrast agents, and improves patients' prognoses.

Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study.

PURPOSE: The present study assessed the predictive value of peritumoral regions on three tumor tasks, and further explored the influence of peritumors with different sizes. METHODS: We retrospectively collected 333 samples of gastrointestinal stromal tumors from the Second Affiliated Hospital of Zhejiang University School of Medicine, and 183 samples of gastrointestinal stromal tumors from Tianjin Medical University Cancer Hospital. We also collected 211 samples of laryngeal carcinoma and 233 samples of nasopharyngeal carcinoma from the First Affiliated Hospital of Jinan University. The tasks of three tumor datasets were risk assessment (gastrointestinal stromal tumor), T3/T4 staging prediction (laryngeal carcinoma), and distant metastasis prediction (nasopharyngeal carcinoma), respectively. First, deep learning and radiomics were respectively used to construct peritumoral models, to study whether the peritumor had predictive value on three tumor datasets. Furthermore, we defined different sizes peritumors including fixed size (not considering tumor size) and adaptive size (according to average tumor radius) to explore the influence of peritumor of different sizes and types of tumors. Finally, we visualized the deep learning and radiomic models to observe the influence of the peritumor in three datasets. RESULTS: The performance of intra-peritumors are better than intratumors alone in three datasets. Specifically, the comparisons of area under receiver operating characteristic curve in the testing set between intra-peritumoral and intratumoral models are: 0.908 vs 0.873 (P value: 0.037) in gastrointestinal stromal tumor datasets, 0.796 vs 0.756 (P value: 0.188) in laryngeal carcinoma datasets and 0.660 vs 0.579 (P value: 0.431) in nasopharyngeal carcinoma datasets. Furthermore, for gastrointestinal stromal tumor datasets, deep learning is more stable to learn peritumors with both fixed and adaptive size than radiomics. For laryngeal carcinoma datasets, the intra-peritumoral radiomic model could make model performance more balanced. For nasopharyngeal carcinoma datasets, radiomics is also more suitable for modeling peritumors than deep learning. The size of the peritumor is critical in this task, and only the performance of 1.5 mm-4.5 mm peritumors is stable. CONCLUSIONS: Our results indicate that peritumors have additional predictive value in three tumor datasets through deep learning or radiomics. The definitions of the peritumoral region and artificial intelligence method also have great influence on the performance of the peritumor.

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC). PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups. STUDY TYPE: Retrospective. POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals. FIELD STRENGTH: 1.5T and 3T. SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images. ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively. STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance. RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05). DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 4.

Novel radiomics features from CCTA images for the functional evaluation of significant ischaemic lesions based on the coronary fractional flow reserve score.

To explore the superiority of radiomics analysis in the diagnostic performance of coronary computed tomography angiography (CCTA) for identifying myocardial ischaemia and predicting major adverse cardiovascular events (MACE). A total of 105 lesions from 88 patients who underwent CCTA and invasive fractional flow reserve measurement were collected as the training set, and another 31 patients with CCTA and clinical outcome information were used as the validation set. Conventional CCTA features included the stenosis diameter, length, Agatston score and high-risk plaque characteristics. After extracting and selecting radiomics features, the robustness of the radiomics features was examined, and then conventional and radiomics models were established using logistic regressions. The area under the receiver operating characteristic (ROC) curve (AUC) and Net Reclassification Index (NRI) were analysed to compare the discrimination and classification abilities between the two models in both the training and validation sets. A total of 1409 radiomics features were extracted, and three wavelet features were finally screened out. The robustness test showed good stability for the refined radiomics features. Compared with the conventional model, the radiomics model displayed a significantly improved diagnostic performance in the training set (AUC 0.762 vs. 0.631, 95% confidence interval [CI] 0.671-0.853 vs. 0.519-0.742, P = 0.058) but a slightly improved diagnostic performance in the validation set (AUC 0.671 vs. 0.592, 95% CI 0.466-0.875 vs. 0.519-0.742, P = 0.448). The NRI of the radiomics model was increased in both the training and validation sets (NRI 0.198 and 0.238, respectively). Quantitative radiomics analysis was feasible and might help to improve the diagnostic performance of CCTA but is still controversial for predicting MACE.

Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.

PURPOSE: To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images. METHODS: We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patients with COVID-19 using CT images with the maximum lung regions in axial, coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets. RESULTS: The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively. CONCLUSIONS: Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.

Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation.

PURPOSE: To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans. MATERIALS AND METHODS: We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model's prognostic performance. A Kaplan-Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression. RESULTS: Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic-clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688-0.920) and 0.756 (95% CI, 0.605-0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts. CONCLUSIONS: A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy. KEY POINTS: • Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer. • Imaging features extracted from CECT and NCCT images were independent predictors of PFS. • We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.