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The global scattering-center model is a high-performance electromagnetic scattering parametric model for complex targets in an optical region. The traditional methods for constructing global scattering models are usually based on candidate-point screening and clustering and are prone to producing false scattering centers and ignoring actual scattering centers. To address this issue, this study proposes a novel modeling method based on the spectral peak analysis of the target electromagnetic scattering intensity field. First, the three-dimensional (3D) electromagnetic scattering intensity field of the target is estimated based on the multiperspective, one-dimensional scattering-center parameters of the target using the RANdom SAmple Consensus (RANSAC) and Parzen window methods. Next, the positions of the global 3D scattering centers are determined through spectral peak analysis, scattering-center association, and multivision measurement fusion. Finally, the scattering coefficients and type parameters of the global scattering centers are estimated after the visibility of the global scattering center is corrected through binary image morphological processing. Simulation results demonstrate that the global scattering center model extracted using this method, which is highly consistent with the geometrical structure of the target, achieves higher expression accuracy while using fewer scattering centers than those used in traditional methods.

Key Clinical Message Ultrasound‐guided core needle biopsy combined with immunohistochemistry and molecular testing could improve the diagnostic accuracy of bone metastases from follicular thyroid carcinoma, help to predict distant metastasis and prognosis. Abstract Metastatic thyroid follicular carcinoma presenting initially with bone lesion is uncommon, its prime symptom is gradual onset, localized pain. Patient with bone metastasis who were diagnosed before thyroidectomy had a higher rate of mortality, clinician should be cautious in eliciting the clinical history and this insidious symptom in middle age group, carry out further examination. We are presenting two case reports of a follicular thyroid carcinoma with bone metastasis, ultrasound‐guided core needle biopsy combined with immunohistochemistry (IHC) were carried out by our clinical team to determine the source and nature of the tumor, relevant literature was reviewed, molecular testing was discussed, we believe core needle biopsy combined with IHC and molecular testing improve the diagnostic accuracy of bone metastases from follicular thyroid carcinoma.

Background: Both CellSearch and CellCollector have been accepted as the proper devices to capture CTC by domestic approval department. However, there is little article about the comparison between these two devices around the world. Herein, we conducted the real-world study to compare with these two devices and to re-verify the efficacy of CTC counts. Methods: Patients who meet the following points should be included in the analysis. 1. Female, aged 18 years or older; 2. Eastern Cooperative Oncology Group (ECOG) score 0–2; 3. With at least one measurable tumor lesion; 4. Clear immunohistochemistry result; 5. Accept at least one CTC test. Patients were excluded in the analysis if they had a history of malignant tumors, incomplete follow-up information. Results: 536 metastatic breast cancer patients who had been detected for CTC at least once by CellSearch or CellCollector were included in the analysis. CellCollector in vivo CTC detection technology has a higher detection rate than the CellSearch system (69.2% vs 57.4%, P = 0.009). However, the proportion of CTC≥5 detected by CellSearch was higher than CellCollector (37.4% vs 16.3%, P

Synthetic aperture radar (SAR) automatic target recognition (ATR) technology has seen significant advancements. Despite these advancements, the majority of research still operates under the closed-set assumption, wherein all test samples belong to classes seen during the training phase. In real-world applications, however, it is common to encounter targets not previously seen during training, posing a significant challenge to the existing methods. Ideally, an ATR system should not only accurately identify known target classes but also effectively reject those belonging to unknown classes, giving rise to the concept of open set recognition (OSR). To address this challenge, we propose a novel approach that leverages the unique capabilities of the Capsule Network and the Kullback-Leibler divergence (KLD) to distinguish unknown classes. This method begins by deeply mining the features of SAR targets using the Capsule Network and enhancing the separability between different features through a specially designed loss function. Subsequently, the KLD of features between a testing sample and the center of each known class is calculated. If the testing sample exhibits a significantly larger KLD compared to all known classes, it is classified as an unknown target. The experimental results of the SAR-ACD dataset demonstrate that our method can maintain a correct identification rate of over 95% for known classes while effectively recognizing unknown classes. Compared to existing techniques, our method exhibits significant improvements.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

Background and objectives: The purpose of this research was to develop and validate the first prognostic nomograms for 3-, 5-, and 10-year cancer-specific survival (CSS) and overall survival (OS) in patients diagnosed with locally advanced thyroid cancer (LATC) by evaluating independent predictors of prognosis in a population of LATC patients. Methods: Demographics, clinicopathologic characteristics, treatment, and follow-up of 2396 LATC patients in the surveillance, epidemiology, and end results database from 2004 to 2015 were retrospectively analyzed and compared with patients with LATC according to staging. We randomized all LATC patients into training and validation groups in a 7:3 ratio. Cox regression analyses helped us to derive independent prognostic factors for LATC patients. According to these results, we established and validated the first prognostic nomograms and risk stratification. Results: In our research, the clinical information of LATC patients was compared and significant differences were found in the relevant variables including CSS and OS (P

Background: CDK4/6 inhibitors combined with endocrine therapy are standard first- or second-line treatment for patients with HR-positive and HER2-negative advanced breast cancer, however, there is currently no optimal recommendation for therapeutic strategies after progression on CDK4/6i. The aim of this study is to analyze the efficacy and safety of HDAC inhibitor Tucidinostat combined with endocrine therapy in patients after prior CDK4/6 inhibitor progression. Methods: The pathological and clinical data of 44 HR-positive and HER2-negative breast cancer patients treated with tucidinostat after progression on CDK4/6i at the Breast Oncology Department of the Fifth Medical Center of the PLA General Hospital from July 2019 to October 2021 were retrospectively analyzed. Observation indexes included progression-free survival (PFS), overall survival (OS), clinical benefit rate (CBR), objective response rate (ORR) and adverse events. At the same time, we attempted to identify potential genomic predictors using available next-generation sequencing (NGS). Results: A total of 44 patients were enrolled in this study. Median follow-up was 10 months (1–26 months) by the data cutoff date (February 2022). The CBR was 6.8% (3/44), the median PFS was 2.0 months (95% CI 1.9–2.1), and the median OS was 14 months (95% CI 6.3–21.7). The mPFS was 4.1 months (95%CI: 0–8.2) in patients with 1 metastatic site, and the mPFS was 4.5 months (95%CI: 4.2–4.8) in patients who received sequential tucidinostat after CDK4/6i failure. Multivariate analysis showed that patients with 1 metastatic site or sequential tucidinostat treatment after failure of CDK4/6i were more likely to benefit from tucidinostat combined with endocrine therapy. Preliminary data showed PIK3CA mutation may be associated with resistance of tucidinostat therapy. No grade 4 adverse events and no treatment-related deaths were recorded in the study. Dose reductions because of adverse events occurred in 4 (9.1%) patients. Conclusions: This study preliminarily shows that tucidinostat combined with endocrine therapy may be an optional sequential strategy for patients with HR+/HER2-advanced breast cancer that has progressed on CDK4/6 inhibitor, especially for these with lower tumor burden and fewer prior palliative treatment.

BackgroundNeoadjuvant therapy is a standard treatment for patients with large, nonmetastatic breast cancer and may allow breast-conserving surgery after tumor downsizing while decreasing the risk of subsequent relapse. Dynamic changes of circulation tumor cells (CTCs) have a role in predicting treatment efficacy of breast cancer. However, the relationship between CTC enumeration before neoadjuvant therapy and pathologic complete response rate is still uncertain.MethodsThe study was exploratory. A total of 50 breast cancer patients were enrolled in a phase II clinical study of neoadjuvant therapy for HER-2-positive early breast cancer. They were enrolled for blood draws before and after neoadjuvant therapy. We used two methods (CellSearch and TUMORFISH) to detect CTCs. We compared the sensitivity of the two systems and investigated the correlation of the enumeration on baseline CTCs with the diagnosis, prognosis, and efficacy of neoadjuvant therapy of the patients with HER-2-positive early breast cancer. We also explored the dynamic change of CTCs after neoadjuvant therapy.ResultsThe sensitivity of TUMORFISHER (27/50) method was significantly higher than that of the CellSearch system (15/50, p=0.008). The CTC numbers detected by the two detection systems were not significantly correlated with lymph node status, clinical stage, ki-67 level and hormone receptor status. Patients with ≥1 CTC before neoadjuvant therapy measured by the TUMORFISHER system had a significant high pCR rate (74.1% vs. 39.1%, p = 0.013); whereas, there was no predictive effect on pCR by CellSearch system (73.3% vs. 51.4%, p = 0.15). Patients with a decrease in CTCs enumeration after neoadjuvant therapy were more likely to achieve pCR than those with no change or increase in CTCs enumeration (87.5% vs 50.0%, p = 0.015) by the TUMORFISHER method. Unfortunately, there was no predictive value of CTCs enumeration for EFS before and after neoadjuvant therapy by two methods.ConclusionsOur study demonstrates that the new CTCs detection method TUMORFISHER system has a higher checkout rate in early breast cancer than the CellSearch system, and shows the opportunity of CTC enumeration as a novel assistant biomarker to predict the response of neoadjuvant therapy in patients with HER-2-positive early breast cancer.

In this paper, a dual-loop active disturbance rejection attitude controller design method is proposed for the attitude control of aircraft. Firstly, based on the nonlinear dynamic model of aircraft, the control system model with inner loop as angular velocity and outer loop as attitude angle is established. Secondly, the extended state observer for the inner loop is established to estimate and compensate the state of the system in real time. The controller of the compensated series integral system is designed by using the linear PD control method. Thirdly, the extended state observer and controller for the outer loop are designed by the same method. Finally, the stability proof of the dual-loop attitude control method is given based on the Lyapunov theorem of stability. Simulation results show that compared with the traditional PID attitude control method, the dual-loop ADRC attitude control method has better control quality in tracking speed, tracking error, and system anti-interference ability. The control parameters to be adjusted in this method have distinct physical significance. So this method has a wide range of engineering application prospects.