RESUMEN
The present study aimed to retrospectively assess the effects of human epidermal growth factor receptor 2 (HER2) expression on the diagnosis of patients with hormone receptor (HR)+/HER2- late-stage breast cancer undergoing advanced first-line endocrine-based treatment. A total of 72 late-stage breast tumor cases from June 2017 to June 2019 were selected from the Department of Surgical Oncology, Shaanxi Provincial People's Hospital (Xi'an, China) and included in the present study. The expression of estrogen receptor, progesterone receptor and HER2 was detected by immunohistochemistry. The subjects were divided into two groups: the HER2-negative (0) cohort (n=31) and the HER2 low expression cohort (n=41). The age, BMI, Karnofsky Performance Status (KPS) score, tumor size, lymph node metastasis, pathological type, Ki-67 expression and menopausal status of the patients were obtained through the electronic medical record system of Shaanxi Provincial People's Hospital. Progression-free survival (PFS) and overall survival (OS) were evaluated for all patients. The median PFS and OS of the HER2(0) cohort were longer than those of the HER2 low expression cohort (all P<0.05). It was shown that age (hazard ratio, 6.000 and 5.465), KPS score (hazard ratio, 4.000 and 3.865), lymph node metastasis (hazard ratio, 3.143; 2.983) and HER2 status (hazard ratio, 3.167 and 2.996) were independent influencing factors of the prognosis of patients with HR+/HER2- advanced breast cancer (ABC) (all P<0.05). Three models (model 1, no parameters adjusted; model 2, BMI, tumor size, pathological type, Ki-67 and menopausal status adjusted; and model 3, age, KPS functional status score and lymph node metastasis adjusted based on model 2) were established within the HER2(0) cohort as the reference for statistical analysis using the multivariate Cox's regression test. In models 2 and 3, the risk of poor prognosis of ABC within the HER2 low expression cohort was significantly higher compared with that in the HER2(0) cohort (hazard ratio, 3.558 and 4.477; 95% CI, 1.349-9.996 and 1.933-11.586; P=0.003 and P<0.001). The HER2 expression status of patients with HR+/HER2- ABC receiving advanced first-line endocrine therapy may affect PFS and OS.
RESUMEN
This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance matrix with the persymmetric structure covariance estimator, symmetric structure covariance estimator, and Toeplitz structure covariance estimator, respectively, to derive three knowledge-aided structured covariance estimators. At the analysis stage, the authors assess the performance of the proposed estimators in estimation accuracy and detection probability. The analysis is conducted both on the simulated data and real sea clutter data collected by the IPIX radar sensor system. The results show that the knowledge-aided Toeplitz structure covariance estimator (KA-T) has the best performance both in estimation and detection, and the knowledge-aided persymmetric structure covariance estimator (KA-P) has similar performance with the knowledge-aided symmetric structure covariance estimator (KA-S). Moreover, compared with existing knowledge-aided estimator, the proposed estimators can obtain better performance when secondary data are insufficient.
RESUMEN
In infrared and visible image fusion, existing methods typically have a prerequisite that the source images share the same resolution. However, due to limitations of hardware devices and application environments, infrared images constantly suffer from markedly lower resolution compared with the corresponding visible images. In this case, current fusion methods inevitably cause texture information loss in visible images or blur thermal radiation information in infrared images. Moreover, the principle of existing fusion rules typically focuses on preserving texture details in source images, which may be inappropriate for fusing infrared thermal radiation information because it is characterized by pixel intensities, possibly neglecting the prominence of targets in fused images. Faced with such difficulties and challenges, we propose a novel method to fuse infrared and visible images of different resolutions and generate high-resolution resulting images to obtain clear and accurate fused images. Specifically, the fusion problem is formulated as a total variation (TV) minimization problem. The data fidelity term constrains the pixel intensity similarity of the downsampled fused image with respect to the infrared image, and the regularization term compels the gradient similarity of the fused image with respect to the visible image. The fast iterative shrinkage-thresholding algorithm (FISTA) framework is applied to improve the convergence rate. Our resulting fused images are similar to super-resolved infrared images, which are sharpened by the texture information from visible images. Advantages and innovations of our method are demonstrated by the qualitative and quantitative comparisons with six state-of-the-art methods on publicly available datasets.
