Visual analysis on clinical randomized controlled trial of fire needle based on VOSviewer and CiteSpace. / 基于VOSviewer与CiteSpaceçš„ç«é’ˆä¸´åºŠéšæœºå¯¹ç §è¯•éªŒç ”ç©¶å¯è§†åŒ–åˆ†æž.

To analyze the research hotspots, frontiers and trends of fire needle clinical randomized controlled trial (RCT) literature in the past 10 years by using bibliometrics and knowledge mapping methods. Six Chinese and English databases including CNKI, Wanfang, VIP, SinoMed, PubMed and Web of Science ( WOS ) were searched for RCT research literature on fire needle. CiteSpace V6.1.R6 and VOSviewer V1.6.18 software were used to analyze the cooperation network, keyword co-occurrence, keyword clustering, keyword timeline, keyword emergence, etc., and to draw a visual knowledge map. A total of 1 973 Chinese articles and 3 English articles were included. The top three institutions that publish articles were Guangzhou University of CM, Heilongjiang University of CM and Beijing Hospital of TCM Affiliated to Capital Medical University. The fire needle was often combined with acupuncture, cupping and bloodletting therapy in the treatment of acne, vitiligo, lumbar disc herniation, herpes zoster, stroke sequelae, facial paralysis, knee osteoarthritis and so on. The research frontiers included the combined application of fire needle and other therapies, clinical mechanism research and efficacy evaluation index research. In the future, we should expand the dominant diseases, optimize the research design, strengthen the cooperation between the teams, and carry out high-level clinical research.

Performance of abbreviated protocols versus unenhanced MRI in detecting occult breast lesions of mammography in patients with dense breasts.

To assess the diagnostic ability of abbreviated protocols of MRI (AP-MRI) compared with unenhanced MRI (UE-MRI) in mammographically occult cancers in patients with dense breast tissue. The retrospective analysis consisted of 102 patients without positive findings on mammography who received preoperative MRI full diagnostic protocols (FDP) between January 2015 and December 2018. Two breast radiologists read the UE, AP, and FDP. The interpretation times were recorded. The comparisons of the sensitivity, specificity and area under the curve of each MRI protocol, and the sensitivity of these protocols in each subgroup of different size tumors used the Chi-square test. The paired sample t-test was used for evaluating the difference of reading time of the three protocols. Among 102 women, there were 68 cancers and two benign lesions in 64 patients and 38 patients had benign or negative findings. Both readers found the sensitivity and specificity of AP and UE-MRI were similar (p > 0.05), whereas compared with FDP, UE had lower sensitivity (Reader 1/Reader 2: p = 0.023, 0.004). For different lesion size groups, one of the readers found that AP and FDP had higher sensitivities than UE-MRI for detecting the lesions ≤ 10 mm in diameter (p = 0.041, p = 0.023). Compared with FDP, the average reading time of UE-MRI and AP was remarkably reduced (p < 0.001). AP-MRI had more advantages than UE-MRI to detect mammographically occult cancers, especially for breast tumors ≤ 10 mm in diameter.

A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules.

This study was to develop a radiomics nomogram mainly using wavelet features for identifying malignant and benign early-stage lung nodules for high-risk screening. A total of 116 patients with early-stage solitary pulmonary nodules (SPNs) (≤ 3 cm) were divided into a training set (N = 70) and a validation set (N = 46). Radiomics features were extracted from plain LDCT images of each patient. A radiomics signature was then constructed with the LASSO with the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. This radiomics signature, consisting of one original and nine wavelet features, achieved favorable predictive efficacy than Mayo Clinic Model. The radiomics nomogram with radiomics signature and age also showed good calibration and discrimination in the training set (AUC 0.9406; 95% CI 0.8831-0.9982) and the validation set (AUC 0.8454; 95% CI 0.7196-0.9712). The decision curve indicated the clinical usefulness of our nomogram. The presented radiomics nomogram shows favorable predictive accuracy for identifying malignant and benign lung nodules in early-stage patients and is much better than the Mayo Clinic Model.