RESUMO
As medical treatments continue to advance rapidly, minimally invasive surgery (MIS) has found extensive applications across various clinical procedures. Accurate identification of medical instruments plays a vital role in comprehending surgical situations and facilitating endoscopic image-guided surgical procedures. However, the endoscopic instrument detection poses a great challenge owing to the narrow operating space, with various interfering factors (e.g. smoke, blood, body fluids) and inevitable issues (e.g. mirror reflection, visual obstruction, illumination variation) in the surgery. To promote surgical efficiency and safety in MIS, this paper proposes a cross-layer aggregated attention detection network (CLAD-Net) for accurate and real-time detection of endoscopic instruments in complex surgical scenarios. We propose a cross-layer aggregation attention module to enhance the fusion of features and raise the effectiveness of lateral propagation of feature information. We propose a composite attention mechanism (CAM) to extract contextual information at different scales and model the importance of each channel in the feature map, mitigate the information loss due to feature fusion, and effectively solve the problem of inconsistent target size and low contrast in complex contexts. Moreover, the proposed feature refinement module (RM) enhances the network's ability to extract target edge and detail information by adaptively adjusting the feature weights to fuse different layers of features. The performance of CLAD-Net was evaluated using a public laparoscopic dataset Cholec80 and another set of neuroendoscopic dataset from Sun Yat-sen University Cancer Center. From both datasets and comparisons, CLAD-Net achieves the AP0.5 of 98.9% and 98.6%, respectively, that is better than advanced detection networks. A video for the real-time detection is presented in the following link: https://github.com/A0268/video-demo.
RESUMO
BACKGROUND: Poorly differentiated gastric adenocarcinoma (PDGA) is a common adenocarcinoma with less glandular structure in gastric cancer. To date, the factors affecting its prognosis remain unclear. In this study, we establish a novel prognostic nomogram for PDGA. METHODS: We screened the Surveillance, Epidemiology, and End Results (SEER) database and downloaded data from PDGA patients who underwent surgery between 2010 and 2015. We explored their clinicopathological characteristics and important prognostic factors such as overall survival (OS), using univariate and multivariate Cox proportional hazards regression analyses, then constructed a prognostic nomogram using the resulting significant variables to predict the OS. We verified performance of the nomogram externally using a separate Chinese set, and further compared its ability as well as the 8th edition of the American Joint Committee on Cancer (AJCC) staging system to predict prognosis. RESULTS: A total of 3,887 patients in the SEER database met our inclusion criteria and were therefore included in the analysis. Multivariate analysis showed that age, sex, tumor size, prime site of tumor, T stage, N stage, and M stage were all independent prognostic factors for PDGA. These factors allowed successful establishment of a nomogram model with high predictive power, based on external verification using a Chinese set comprising 632 PDGA patients. The nomogram showed a better discrimination advantage than the 8th edition of the AJCC staging system in predicting OS (C-index of nomogram vs. AJCC staging for SEER set: 0.707 vs. 0.663; Chinese set: 0.788 vs. 0.713). CONCLUSIONS: The nomogram, established herein, was more accurate in predicting the 1-, 3-, and 5-year OS of PDGA patients than the traditional AJCC TNA staging system. Successful establishment of a PDGA prognostic nomogram is a further step towards individualized and precise treatment of gastric cancer.
RESUMO
BACKGROUND AND AIMS: The regular arrangement of collecting venules (RAC) refers to the appearance of multiple regular tiny veins in the body of the stomach and is considered to be very effective for identifying gastric mucosa with non-Helicobacter pylori infection. This meta-analysis was conducted to systematically evaluate the value of the sign in predicting a Helicobacter pylori-negative stomach and the relevant factors that may affect the performance of this prediction. METHODS: Two biomedical databases (PubMed and EMBASE) were systematically searched through April 20, 2020. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the SROC curve (AUC) were calculated. RESULTS: Fourteen articles with 4070 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR and AUC for the RAC in predicting non-Hp infection were 0.80 (0.67-0.89), 0.97 (0.93-0.98), 24.8 (12.2-50.8), 0.21 (0.12-0.36), 120 (47-301) and 0.97 (0.19-1.00), respectively. CONCLUSIONS: The RAC is a valuable endoscopic feature for the prediction of patients without Hp infection.