Dual-Branch Convolutional Neural Network Based on Ultrasound Imaging in the Early Prediction of Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Breast Cancer.

The early prediction of a patient's response to neoadjuvant chemotherapy (NAC) in breast cancer treatment is crucial for guiding therapy decisions. We aimed to develop a novel approach, named the dual-branch convolutional neural network (DBNN), based on deep learning that uses ultrasound (US) images for the early prediction of NAC response in patients with locally advanced breast cancer (LABC). This retrospective study included 114 women who were monitored with US during pretreatment (NAC pre) and after one cycle of NAC (NAC1). Pathologic complete response (pCR) was defined as no residual invasive carcinoma in the breast. For predicting pCR, the data were randomly split into a training set and test set (4:1). DBNN with US images was proposed to predict pCR early in breast cancer patients who received NAC. The connection between pretreatment data and data obtained after the first cycle of NAC was considered through the feature sharing of different branches. Moreover, the importance of data in various stages was emphasized by changing the weight of the two paths to classify those with pCR. The optimal model architecture of DBNN was determined by two ablation experiments. The diagnostic performance of DBNN for predicting pCR was compared with that of four methods from the latest research. To further validate the potential of DBNN in the early prediction of NAC response, the data from NAC pre and NAC1 were separately assessed. In the prediction of pCR, the highest diagnostic performance was obtained when combining the US image information of NAC pre and NAC1 (area under the receiver operating characteristic curve (AUC): 0.939; 95% confidence interval (CI): 0.907, 0.972; F1-score: 0.850; overall accuracy: 87.5%; sensitivity: 90.67%; and specificity: 85.67%), and the diagnostic performance with the combined data was superior to the performance when only NAC pre (AUC: 0.730; 95% CI: 0.657, 0.802; F1-score: 0.675; sensitivity: 76.00%; and specificity: 68.38%) or NAC1 (AUC: 0.739; 95% CI: 0.664, 0.813; F1-score: 0.611; sensitivity: 53.33%; and specificity: 86.32%) (p<0.01) was used. As a noninvasive prediction tool, DBNN can achieve outstanding results in the early prediction of NAC response in patients with LABC when combining the US data of NAC pre and NAC1.

A Physics-Guided Coordinated Distributed MPC Method for Shape Control of an Antenna Reflector.

Active shape control for an antenna reflector is a significant procedure used to compensate for the impacts of a complicated space environment. In this article, a physics-guided distributed model predictive control (DMPC) framework for reflector shape control with input saturation is proposed. First, guided by the actual physical characteristics, an overall structural system is decomposed into multilevel subsystems with the help of a so-called substructuring technique. For each subsystem, a prediction model with information interaction is discretized by an explicit Newmark- ß method. Then, to improve the system-wide control performance, a coordinator among all the subsystems is designed in an iterative fashion. The input saturation constraints are addressed by transforming the original problem into a linear complementarity problem (LCP). Finally, by solving the LCP, the input trajectory can be obtained. The performance of the proposed DMPC algorithm is validated through an experiment on the shape control of an antenna reflector structure.

A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors.

Breast cancer is one of the leading causes of female cancer deaths. Early diagnosis with prophylactic may improve the patients' prognosis. So far ultrasound (US) imaging has been a popular method in breast cancer diagnosis. However, its accuracy is bounded to traditional handcrafted feature methods and expertise. A novel method, named dual-sampling convolutional neural networks (DSCNNs), was proposed in this paper for the differential diagnosis of breast tumors based on US images. Combining traditional convolutional and residual networks, DSCNN prevented gradient disappearance and degradation. The prediction accuracy was increased by the parallel dual-sampling structure, which can effectively extract potential features from US images. Compared with other advanced deep learning methods and traditional handcrafted feature methods, DSCNN reached the best performance with an accuracy of 91.67% and an area under curve of 0.939. The robustness of the proposed method was also verified by using a public dataset. Moreover, DSCNN was compared with evaluation from three radiologists utilizing US-BI-RADS lexicon categories for overall breast tumors assessment. The result demonstrated that the prediction sensitivity, specificity and accuracy of the DSCNN were higher than those of the radiologist with 10 year experience, suggesting that the DSCNN has the potential to help doctors make judgements in clinic.