Objective To investigate the value of contrast-enhanced ultrasonography in detection and diagnosis of small primary liver cancer. Methods SonoVue-enhanced ultrasonography were performed on 353 patients with 378 primary liver cancer, less than 3 cm in diameter. Enhancement patterns and enhancement phases of hepatic lesions on contrast-enhanced ultrasonography were analyzed and compared with the results of histopathology. Results In all hepatic tumors, 96.6% （365/378） lesions enhanced in the arterial phase. Among them, 317 (83.9%) tumors enhanced earlier than liver parenchyma and 48 (12.7%) tumors enhanced synchronously with liver parenchyma, and 342 (90.5%) tumors showed early wash-out in the portal and late phases. With regard to the enhancement pattern, 329 (87.0%) tumors presented whole-lesion enhancement, 35 (9.3%) to be mosaic enhancement and 14 (3.7%) to be rim-like enhancement. If taking the whole-lesion enhancement and mosaic enhancement in arterial phase as diagnotic standard for primary liver cancer on contrast-enhanced ultrasonography, the sensitivity was 92.9%(351/378), and if the earlier or synchronous enhancement of the tumor compared with liver parenchyma in arterial phase and the wash-out in portal phase were regarded as the stardand, the sensitivity was 87.3%(330/378). Conclusion Contrast-enhanced ultrasonography could display real-time enhancement patterns as well as the wash-out processes both in hepatic tumors and the liver parenchyma. It might be of clinical value in diagnosis of primary liver cancer based on the hemodynamics of hepatic tumors on contrast-enhanced ultrasonography.
ObjectiveTo investigate the recommendations on imaging diagnosis in Chinese clinical practice guidelines (CPGs). MethodsWe electronically searched WanFang Data, VIP, CNKI and CBM databases from inception to December 31, 2014. Two reviewers independently screened literature and extracted data. The method of bibliometrics was used to analyze the data (including basic characteristics, strength of recommendation, quality of evidence, etc.). ResultsA total of 341 CPGs formulating the recommendations on diagnosis were included. 48.7% (166/341) guidelines developed the recommendations on imaging diagnosis (a total of 534). 25.7% (137/534) recommendations were with the symbols of quality of evidence and strength of recommendation, and 18.9% (101/534) with special words such as recommend, suggest. 22.3% (119/534) recommendations reported the strength of recommendation. Of which, 38.7% (46/119) were strong and 16.0% (19/119) were weak. However, 23.9% (11/46) strong recommendations were based on low quality of evidence. And 42.1% (8/19) weak recommendations were based on high quality of evidence. ConclusionAmong Chinese CPGs formulating the recommendations on diagnosis, the number of CPGs with recommendations on imaging is about 50%. And the quantity increases by years. The proportions of recommendations on imaging which report the strength of recommendation and/or quality of evidence are low. Meanwhile, the rating systems are uniform. Then the developers do not report the explanation for the strong recommendations based on low quality of evidence or the weak recommendations based on high quality of evidence in guideline.
ObjectiveTo investigate the citation status of systematic reviews on imaging diagnosis in clinical practice guidelines (CPGs) and provide reference for the development of Chinese imaging diagnosis guidelines. MethodsWe electronically searched PubMed databases to collect systematic reviews on imaging diagnosis. The date was limited from January 1st 2010 to December 31th 2012. Two reviewers independently screened literature and extracted data. The citation data of included systematic reviews were obtained on the Web of Science. Citation analysis method was used to analyze the citation frequency of systematic reviews on imaging diagnosis in CPGs. Results292 systematic reviews on imaging diagnosis were included, of which 94% (275/292) were indexed by Science Citation Index. The total citation frequency of these systematic reviews was 5413 (medium:20, range:0 to 131). 28% (78/275) were cited by CPGs. Of which, 7% (19/275) were used as the source of the evidence of recommendations in CPGs. ConclusionThe ratio of systematic reviews cited by CPGs is low, the ratio of being the source of evidence of recommendations of systematic reviews in CPGs is lower, and furthermore, the citation is time-delayed.
Pneumoconiosis ranks first among the newly-emerged occupational diseases reported annually in China, and imaging diagnosis is still one of the main clinical diagnostic methods. However, manual reading of films requires high level of doctors, and it is difficult to discriminate the staged diagnosis of pneumoconiosis imaging, and due to the influence of uneven distribution of medical resources and other factors, it is easy to lead to misdiagnosis and omission of diagnosis in primary healthcare institutions. Computer-aided diagnosis system can realize rapid screening of pneumoconiosis in order to assist clinicians in identification and diagnosis, and improve diagnostic efficacy. As an important branch of deep learning, convolutional neural network (CNN) is good at dealing with various visual tasks such as image segmentation, image classification, target detection and so on because of its characteristics of local association and weight sharing, and has been widely used in the field of computer-aided diagnosis of pneumoconiosis in recent years. This paper was categorized into three parts according to the main applications of CNNs (VGG, U-Net, ResNet, DenseNet, CheXNet, Inception-V3, and ShuffleNet) in the imaging diagnosis of pneumoconiosis, including CNNs in pneumoconiosis screening diagnosis, CNNs in staging diagnosis of pneumoconiosis, and CNNs in segmentation of pneumoconiosis foci to conduct a literature review. It aims to summarize the methods, advantages and disadvantages, and optimization ideas of CNN applied to the images of pneumoconiosis, and to provide a reference for the research direction of further development of computer-aided diagnosis of pneumoconiosis.