Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma.

Background and purpose: The presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN. Materials and methods: In this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation. Results: Using the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods. Conclusion: Our modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction.

CPTV: Classification by tracking of carotid plaque in ultrasound videos.

The risk assessment of carotid plaque is strongly related to the plaque echo status in ultrasound. However, the echo classification of carotid plaques based on ultrasound remains challenging due to the changes in plaque shape and semantics, along with the complex vascular environment. This study proposed a framework for Classification of Plaque by Tracking Videos (CPTV). To the best of our knowledge, this is the first study on plaque classification by tracking ultrasound video rather than a sonographic view, which achieves accurate localization and stable echo classification. In the tracking task, Multi-scale Decoupling Tracking (MDTrack) module including Multi-scale Dilated Encoder (MDE) and Internal-Exterior Feature Decoupling (IEFD) was proposed to solve the problems caused by shape and semantic variations to achieve accurate plaque localization in ultrasound. In the classification task, the Tracking-assisted 3D Attention (T3D-Attention) module included recombination and 3D-Attention extracted plaque features and echo-related features in the vascular environment. The experiments demonstrated that the performance of CPTV is better than current mainstream tracking and classification methods, indicating that the tracking-assistance classification is a kind of enhancement method with high universality and stability in the plaque in ultrasound.

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.

Current status of group B Streptococcus infection in neonates: a multicenter prospective study. / 新生儿Bæ—é“¾çƒèŒæ„ŸæŸ“çŽ°çŠ¶çš„å¤šä¸­å¿ƒå‰çž»æ€§ç ”ç©¶.

OBJECTIVES: To investigate the incidence of maternal group B Streptococcus (GBS) colonization and neonatal early-onset GBS disease (GBS-EOD), and to study the factors associated with the development of GBS-EOD in the offspring of pregnant women with GBS colonization. METHODS: A total of 16 384 pregnant women and 16 634 neonates delivered by them were enrolled prospectively who had medical records in Xiamen Maternal and Child Care Hospital, Beijing Obstetrics and Gynecology Hospital of Capital Medical University, and Zhangzhou Zhengxing Hospital from May 1, 2019 to April 30, 2020. Unified GBS screening time, culture method, and indication for intrapartum antibiotic prophylaxis (IAP) were adopted in the three hospitals. The incidence rates of maternal GBS colonization and neonatal GBS-EOD were investigated. A multivariate logistic regression analysis was used to identify the factors associated with the development of GBS-EOD in the offspring of pregnant women with GBS colonization. RESULTS: In these three hospitals, the positive rate of GBS culture among the pregnant women in late pregnancy was 11.29% (1 850/16 384), and the incidence rate of neonatal GBS-EOD was 0.96‰ (16/16 634). The admission rate of live infants born to the GBS-positive pregnant women was higher than that of those born to the GBS-negative ones (P<0.05). The live infants born to the GBS-positive pregnant women had a higher incidence rate of GBS-EOD than those born to the GBS-negative ones [6.38‰ (12/1 881) vs 0.27‰ (4/14 725), P<0.05]. The multivariate logistic regression analysis showed that placental swabs positive for GBS and positive GBS in neonatal gastric juice at birth were independent predictive factors for the development of GBS-EOD (P<0.05), while adequate IAP was a protective factor (P<0.05) in the offspring of pregnant women with GBS colonization. CONCLUSIONS: GBS colonization of pregnant women in late pregnancy has adverse effects on their offspring. It is important to determine prenatal GBS colonization status of pregnant women and administer with adequate IAP based on the indications of IAP to reduce the incidence of neonatal GBS-EOD. Citation.