Background: Patients with breast cancer (BC) may develop locoregional recurrence alone or with distant metastases. Results of previous studies discussing the benefit of local surgery among patients with chest wall disease were controversial. Whether surgical reduction for chest wall disease could influence survival outcome is still a question. The objective of this study was to compare overall survival (OS) in patients with recurrence involving the chest wall who did or did not undergo surgical reduction after previous treatment of the primary BC to explore the role of surgical reduction. Methods:We retrospectively reviewed BC patients with chest wall as the first recurrent/metastatic site selected between January 2012 and December 2018 to explore whether surgical reduction for chest wall disease could influence OS. Clinicopathological data, including age at initial diagnosis, TNM stage, the pathological parameters, and treatment were recorded and analyzed. OS was primarily described using the Kaplan-Meier estimator for each group, with the statistical significance between groups being tested by the log-rank test.Results: A total of 198 patients with a median age of 48 years (range, 22-73 years) were analyzed. Chest wall as the only site of recurrence occurred in 139 patients (70.2%), and the other 59 (29.8%) patients had other metastatic sites. There were 88 patients who underwent surgical reduction for chest wall recurrence.The median OS was significantly longer for the patients who had chest wall disease reduction than for those who did not {194.2 months [95% confidence interval (CI): 140.4-247.9 months] vs. 102.7 months (95% CI: 79.7-125.7 months), respectively, P=0.001}. From multivariate analysis, surgical reduction was an independent factor significantly influenced OS (HR =0.52, 95% CI: 0.33-0.81, P=0.004). Subgroup analyses showed that OS was statistically longer in the chest wall disease surgical reduction group than in the no reduction group with respect to hormone receptor (HR) negative (−), human epidermal growth factor receptor 2 (HER2) negative (−), triple-negative breast cancer (TNBC), disease-free survival (DFS) >24 months, and chest wall disease only.Conclusions: BC patients with chest wall recurrence could benefit from surgical reduction with a prolonged OS. In a certain selected group, surgical reduction may be warranted.
Abstract. Accurate location of pedestrians plays a crucial role in emergency relief, traffic control, crowd behavior analysis and other aspects. Especially in the context of the COVID-19 pandemic in recent years, pedestrian location technology can help relevant departments to complete target screening more quickly.However, the Pedestrian Dead Reckoning algorithm can only calculate the target trajectory through the sensor return value, but can not carry out real-time trajectory correction and location.With the rapid development of deep learning, object detection and tracking technology based on computer vision has been applied to pedestrian location, but there are two challenges in the application process.Firstly, in the pedestrian gathering scene, the target number base is large, so the accuracy of the current detection algorithm needs to be improved and the model drift index of the tracking algorithm needs to be reduced. Secondly, there is a certain distortion between the real three-dimensional coordinate space of pedestrians and the two-dimensional image captured by the camera, and the transformation of the spatial coordinate of the target point is a technical difficulty.In this regard, first of all, to improve the accuracy of pedestrian target detection in crowded scenes, this paper adopts the method of improving the generalization of network to pedestrian target, and uses k-means algorithm to find the best prior frame of pedestrian, and sets the width to height ratio suitable for the target.Secondly, to solve the problem of model drift in the above tracking process, this paper proposes a binary classification model based on target appearance difference, which introduces target context information as a new target distinguishing feature when two or more targets are similar.Finally, in order to obtain more accurate coordinate position information, this paper combines the inverse perspective algorithm to calculate the target coordinates into the coordinates in the world coordinate system, and calculates the exact position of the target in the aerial view, as well as the distance between the targets or the current flow of people.In order to evaluate the effectiveness of the proposed algorithm, experiments on target detection, tracking and precise positioning were carried out in different intensity scenarios to verify the feasibility of the proposed method.
Hot rolled steel strip is usually detected by image processing for defects, but it is often affected by light and conventional image processing methods cannot effectively detect defects with small areas. In this paper, an image processing method is proposed to overcome the effect of fill light for the detection of steel strip surface damage by convolutional neural network multiscale detection method.In the target detection part, K-means++ clustering was performed on the anchor size, while the S-SPP module for multi-scale detection was introduced to further improve the detection of small-area damage. After training the model with the NEU-CLS dataset, the accuracy reached 94.6% and the detection speed was 27.3 ms. Through comparison experiments, the experimental results show that the proposed method of image processing can detect steel strip damage more accurately and faster, which provides a feasible method for practical application in factory scenes.
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