ABSTRACT
Background: Pathophysiology plays a significant role in the scientific study of ischemic stroke, and has attracted increasing interest from researchers in the field. However, a comprehensive bibliometric analysis is lacking in this field. The purpose of this study is to identify the current research status and hotspots of ischemic stroke pathophysiology from a bibliometric perspective. Methods: The Web of Science Core Collection database was searched for articles published from 1990 to 2022. CiteSpace, VOSviewer, and R package "bibliometrix" software were used to analyze countries/regions, institutions, journals, authors, papers, and keywords to predict the latest trends in ischemic stroke pathophysiology research. Results: This analysis collected 7578 records of ischemic stroke pathophysiology. China and America emerged as the leading countries in this field, with Harvard University being the most active institution. Among journals and authors in this field, journal Stroke and author Gregory YH Lip published the most papers, while Nature Medicine was the journal with the highest citation per article. Keywords and co-citation clusters were closely related to "central nervous system", "mechanisms", "biochemistry & molecular biology" and "radiology, nuclear medicine & medical imaging", while other related fields, such as peripheral organs damage induced by the central nervous system and rehabilitation after ischemic stroke, require further research efforts. Conclusion: This is the first bibliometric study that comprehensively mapped out the knowledge structure and development trends of ischemic stroke pathophysiology in recent 32 years, which may provide a reference for scholars to explore ischemic stroke pathophysiology.
ABSTRACT
Background: An accurate and noninvasive method to determine the preoperative clear-cell renal cell carcinoma (ccRCC) pathological grade is of great significance for surgical program selection and prognosis assessment. Previous studies have shown that diffusion-weighted imaging (DWI) has moderate value in grading ccRCC. But DWI cannot reflect the diffusion of tissue accurately because it is calculated using a monoexponential model. Intravoxel incoherent motion (IVIM) is the biexponential model of DWI. Only a few studies have examined the value of IVIM in grading ccRCC yet with inconsistent results. This study aimed to compare the value of DWI and IVIM in grading ccRCC. Methods: In this study, 96 patients with pathologically confirmed ccRCC were evaluated by DWI and IVIM on a 3-T scanner. According to the World Health Organization/International Society of Urological Pathology (WHO/ISUP) classification system, these patients were divided into two groups: low-grade (grade I and II) and high-grade (grade III and IV) ccRCC. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction of pseudodiffusion (f) values were calculated. The Mann-Whitney test, receiver-operating characteristic (ROC) analysis, and the Delong test were used for statistical evaluations. Results: (I) According to the WHO/ISUP nuclear grading system, 96 patients were divided into low-grade (grade I and II, 45 patients) and high-grade (grade III and IV, 51 patients) groups. (II) Compared with patients of low-grade ccRCC, the ADC and D values of those with high-grade ccRCC decreased while the D* and f values increased (P<0.05). (III) The cutoff value of the ADC, D, D*, and f in distinguishing low-grade from high-grade ccRCC was 1.50×10-3 mm2/s, 1.12×10-3 mm2/s, and 33.19×10-3 mm2/s, 0.31, respectively; the area under the curve (AUC) for the ADC, D, D*, and f values was 0.871, 0.942, 0.621, and 0.894, respectively, with the AUC of the D value being the highest; the sensitivity for the ADC, D, D*, and f values was 94.12%, 92.16%, 47.06%, and 92.16%, respectively; and the specificity for the ADC, D, D*, and f values was 66.67%, 91.11%, 77.78%, and 73.33%, respectively. (IV) Based on the Delong test, AUCD was significantly higher than AUCADC (P=0.02) and AUCD* (P<0.001), but there was no significant difference between AUCD and AUC f (P=0.18). Conclusions: Compared with the monoexponential model DWI, the biexponential model IVIM was more accurate in grading ccRCC.
ABSTRACT
Background: Diagnosis of mediastinal lesions on computed tomography (CT) images is challenging for radiologists, as numerous conditions can present as mass-like lesions at this site. This study aimed to develop a self-attention network-based algorithm to detect mediastinal lesions on CT images and to evaluate its efficacy in lesion detection. Methods: In this study, two separate large-scale open datasets [National Institutes of Health (NIH) DeepLesion and Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Mediastinal Lesion Analysis (MELA) Challenge] were collected to develop a self-attention network-based algorithm for mediastinal lesion detection. We enrolled 921 abnormal CT images from the NIH DeepLesion dataset into the pretraining stage and 880 abnormal CT images from the MELA Challenge dataset into the model training and validation stages in a ratio of 8:2 at the patient level. The average precision (AP) and confidence score on lesion detection were evaluated in the validation set. Sensitivity to lesion detection was compared between the faster region-based convolutional neural network (R-CNN) model and the proposed model. Results: The proposed model achieved an 89.3% AP score in mediastinal lesion detection and could identify comparably large lesions with a high confidence score >0.8. Moreover, the proposed model achieved a performance boost of almost 2% in the competition performance metric (CPM) compared to the faster R-CNN model. In addition, the proposed model can ensure an outstanding sensitivity with a relatively low false-positive rate by setting appropriate threshold values. Conclusions: The proposed model showed excellent performance in detecting mediastinal lesions on CT. Thus, it can drastically reduce radiologists' workload, improve their performance, and speed up the reporting time in everyday clinical practice.