It is a clinical problem to identify histological component in enlarged cervical lymph nodes, particularly in differentiation between lymph node metastasis and lymphoma involvement.To construct two kinds of deep learning (DL)-based computer-aided diagnosis (CAD) systems including DL-convolutional neural networks (DL-CNN) and DL-machine learning for pathological diagnosis of cervical lymph nodes by positron emission tomography (PET)/computed tomography (CT) images.We collected CT, PET, and PET/CT images series from 165 patients with enlarged cervical lymph nodes receiving examinations from January 2014 to June 2018. Six CNNs pretrained on ImageNet as DL architectures were used for two kinds of DL-based CAD models, including DL-CNN and DL-machine learning models. The DL-CNN models were constructed via transfer learning for classification of lymphomatous and metastatic lymph nodes. The DL-machine learning models were developed by DL-based features extractors and support vector machine (SVM) classifier. As for DL-SVM models, we also evaluate the effect of handcrafted radiomics features in combination of DL-based features.The DL-CNN model with ResNet50 architecture on PET/CT images had the best diagnostic performance among all six algorithms with an area under the receiver operating characteristic curve (AUC) of 0.845 and accuracy of 78.13% in the testing cohort. The DL-SVM model on ResNet50 extractor showed great performance for the testing cohort with an AUC of 0.901, accuracy of 86.96%, sensitivity of 76.09%, and specificity of 94.20%. The combination of DL-based and handcrafted features yielded the improvement of diagnostic performance.Our DL-based CAD systems on PET/CT images were developed for classifying metastatic and lymphomatous involvement with favorable diagnostic performance in enlarged cervical lymph nodes. Further clinical practice of our systems may improve quality of the following therapeutic interventions and optimize patients' outcomes.
GRB 211211A is a rare burst with a genuinely long duration, yet its prominent kilonova association provides compelling evidence that this peculiar burst was the result of a compact binary merger. However, the exact nature of the merging objects, whether they were neutron star pairs, neutron star--black hole systems, or neutron star--white dwarf systems, remains unsettled. This {\it Letter} delves into the rarity of this event and the possibility of using current and next-generation gravitational wave detectors to distinguish between the various types of binary systems. Our research reveals an event rate density of $\gtrsim 5.67^{+13.04}_{-4.69} \times 10^{-3}\ \rm Gpc^{-3}\ yr^{-1}$ for GRB 211211A-like gamma-ray bursts (GRBs), which, assuming GRB 211211A is the only example of such a burst, is significantly smaller than that of typical long- and short-GRB populations. We further calculated that if the origin of GRB 211211A is a result of a neutron star--black hole merger, it would be detectable with a significant signal-to-noise ratio (S/N), given the LIGO-Virgo-KAGRA designed sensitivity. On the other hand, a neutron star--white dwarf binary would also produce a considerable S/N during the inspiral phase at decihertz and is detectable by next-generation spaceborne detectors DECIGO and the Big Bang Observer. However, to detect this type of system with millihertz spaceborne detectors like LISA, Taiji, and TianQin, the event must be very close, approximately 3 Mpc in distance or smaller.
The Einstein Probe (EP) achieved its first detection and localization of a bright X-ray flare, EP240219a, on February 19, 2024, during its commissioning phase. Subsequent targeted searches triggered by the EP240219a alert identified a faint, untriggered gamma-ray burst (GRB) in the archived data of Fermi/GBM, Swift/BAT, Insight-HXMT/HE and INTEGRAL/SPI-ACS. The EP/WXT light curve reveals a long duration of approximately 160 seconds with a slow decay, whereas the Fermi/GBM light curve shows a total duration of approximately 70 seconds. The peak in the Fermi/GBM light curve occurs slightly later with respect to the peak seen in the EP/WXT light curve. Our spectral analysis shows that a single cutoff power-law model effectively describes the joint EP/WXT-Fermi/GBM spectra in general, indicating coherent broad emission typical of GRBs. The model yielded a photon index of $\sim -1.70 \pm 0.05$ and a peak energy of $\sim 257 \pm 134$ keV. After detection of GRB 240219A, long-term observations identified several candidates in optical and radio wavelengths, none of which was confirmed as the afterglow counterpart during subsequent optical and near-infrared follow-ups. The analysis of GRB 240219A classifies it as an X-ray rich GRB with a high peak energy, presenting both challenges and opportunities for studying the physical origins of X-ray flashes (XRFs), X-ray rich GRBs (XRRs), and classical GRBs (C-GRBs). Furthermore, linking the cutoff power-law component to non-thermal synchrotron radiation suggests that the burst is driven by a Poynting flux-dominated outflow.
MRI acts as a potential resource for exploration and interpretation to identify tumor characterization by advanced computer-aided diagnostic (CAD) methods.To evaluate and validate the performance of MRI-based CAD models for identifying low-grade and high-grade soft tissue sarcoma (STS) and for investigating survival prognostication.Retrospective.A total of 540 patients (295 male/female: 295/245, median age: 42 years) with STSs.5-T MRI with T1 WI sequence and fat-suppressed T2 -weighted (T2 FS) sequence.Manual regions of interests (ROIs) were delineated for generation of radiomic features. Automatic segmentation and pretrained convolutional neural networks (CNNs) were performed for deep learning (DL) analysis. The last fully connected layer at the top of CNNs was removed, and the global max pooling was added to transform feature maps to numeric values. Tumor grade was determined on histological specimens.The support vector machine was adopted as the classifier for all MRI-based models. The DL signature was derived from the DL-MRI model with the highest area under the curve (AUC). The significant clinical variables, tumor location and size, integrated with radiomics and DL signatures were ready for construction of clinical-MRI nomogram to identify tumor grading. The prognostic value of clinical variables and these MRI-based signatures for overall survival (OS) was evaluated via Cox proportional hazard.The clinical-MRI differentiation nomogram represented an AUC of 0.870 in the training cohort, and an AUC of 0.855, accuracy of 79.01%, sensitivity of 79.03%, and specificity of 78.95% in the validation cohort. The prognostic model showed good performance for OS with 3-year C-index of 0.681 and 0.642 and 5-year C-index of 0.722 and 0.676 in the training and validation cohorts.MRI-based CAD nomogram represents effective abilities in classification of low-grade and high-grade STSs. The MRI-based prognostic model yields favorable preoperative capacities to identify long-term survivals for STSs.3 TECHNICAL EFFICACY: Stage 4.
Purpose: To effectively control respiratory motion during radiotherapy of lung cancer without any side effects. Methods: A novel motion control scheme, hypnosis, has been introduced in lung cancer treatment. As we know, the uncertain position of lung tumor compromises the treatment effect. However, during hypnosis the respiration becomes uniform and stability compared to normal respiration. Therefore, hypnosis can be a helpful technique for respiratory control. Six volunteers are selected with a wide range of distribution of height, weight, and vital capacity. A set of experiments have been conducted for each volunteer, which is guided by a professional hypnotist. The amplitude of respiration has been recorded in the normal state and hypnosis state, respectively. Statistics approaches are used to analysis the data after experiments. All the experiments are repeated twice in the same condition. Results: The mean and root‐mean‐square (RMS) of the breathing amplitude are calculated for each experiment. The stability of the peaks and the similarity of the adjacent wave are also analyzed. The mean and the RMS of the amplitude are 16.2mm and 8.6mm during hypnosis state, while they are 37.4mm and 23.9mm during normal state. It can be seen that the mean and the RMS during hypnosis state are 56.6% and 64.2% smaller than during normal state, respectively. Moreover, the passing ratio of γ index between one of the 13 adjacent cycles and the others during hypnosis state is 16.4% higher than during normal state. Conclusion: The hypnosis intervention can be an alternative way for respiratory control, which can effectively reduce the respiratory amplitude and increase the stability of respiratory cycle. It will find useful application in image guided radiotherapy. This work is supported in part by grants from National Natural Science Foundation of China (NSFC: 81171402), NSFC Joint Research Fund for Overseas Research Chinese, Hong Kong and Macao Young Scholars (30928030), National Basic Research Program 973 (2010CB732606) from Ministry of Science and Technology of China, and Guangdong Innovative Research Team Program (No. 2011S013) of China.
It is generally believed that long-duration gamma-ray bursts (GRBs) are associated with massive star core-collapse, whereas short-duration GRBs are associated with mergers of compact star binaries. However, growing observations have suggested that oddball GRBs do exist, and multiple criteria (prompt emission properties, supernova/kilonova associations, and host galaxy properties) rather than burst duration only are needed to classify GRBs physically. A previously reported long-duration burst, GRB 060614, could be viewed as a short GRB with extended emission if it were observed at a larger distance and was associated with a kilonova-like feature. As a result, it belongs to the Type-I (compact star merger) GRB category and is likely of the binary neutron star merger origin. Here we report a peculiar long-duration gamma-ray burst, GRB 211211A, whose prompt emission properties in many aspects differ from all known Type-I GRBs, yet its multi-band observations suggest a non-massive-star origin. In particular, significant excess emission in both optical and near-infrared wavelengths has been discovered, which resembles kilonova emission as observed in some Type-I GRBs. These observations point towards a new progenitor type of GRBs. A scenario invoking a white dwarf-neutron star merger with a post-merger magnetar engine provides a self-consistent interpretation for all the observations, including prompt gamma-rays, early X-ray afterglow, as well as the engine-fed kilonova emission.
Due to the broadcast nature of wireless medium, wireless transmissions can be overheard by any undesired receivers with eavesdropping capability within source transmission range.A novel physical layer approach for secure wireless cooperative communications against eavesdropping is proposed in this paper.For an asynchronous cooperative communication network with a cluster of user nodes transmitting to a common destination, we propose an anti-eavesdropping space-time network coding (AE-STNC) scheme to prevent eavesdropping and overcome the problem of imperfect synchronization.In the proposed scheme, training symbols are first transmitted by the destination ().Owing to channel reciprocity, each user node can obtain the channel state information (CSI) between itself and , which is unavailable to the eavesdroppers.By exploiting such CSI, anti-eavesdropping encoding is designed for each user node to create high decoding error rate at the eavesdroppers and ensure successful decoding at .Furthermore, the AE-STNC is designed to achieve full diversity at .Power allocation subject to average power constraint is considered and the secure region against eavesdroppers is also investigated.Based on the proposed AE-STNC scheme, an anti-eavesdropping space-timefrequency coding (AE-STFNC) scheme is proposed for broadband asynchronous cooperative communications.Simulations are provided to verify the performance and security of the proposed transmission schemes.
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