Objectives To assess the diagnostic accuracy and lesion conspicuity of susceptibility-weighted angiography (SWAN) and T2* for the clot detection in acute cerebral venous thrombosis (CVT) by comparison with contrast-enhanced MR venography. Methods Venous thrombi detection and conspicuity were assessed by two readers for 18 venous segments on both T2*, SWAN source images, 2D SWAN reformats matching with T2*, and 3D SWAN images (SWAN-MinIP). Images obtained with the three reading techniques were systematically scored and compared to CE MRV findings, in a blinded fashion, per patient and per segment, and compared to each other. Results In 30 patients, 137 thrombosed venous segments were evaluated. The sensitivity of T2*, SWAN source images, 2D SWAN, and SWAN MinIP were, respectively, of 89.3%/82.1%, 82.1%, and 82.1% for dural sinus thrombosis and of 100%/100%/100%/96.6% for cortical venous thrombosis. There were significant differences in thrombus detection between T2* and SWAN: T2* versus SWAN source images and 2D SWAN ( p = 0.04) and versus SWAN MinIP ( p = 0.03). There were no significant differences between the three modalities of SWAN images. T2* was more sensitive than all SWAN images for both sigmoid sinus thrombosis and intracranial internal jugular vein thrombosis ( p = 0.04). Inter-observer agreement was slightly superior with T2* ( p < 0.05). Conclusion In this small cohort, SWAN sequence at 3T did not yield additional value for thrombus detection in acute CVT compared to T2*. This study highlights SWAN’s greatest weakness both for diagnostic accuracy and lesion conspicuity compared to T2* for acute venous clot detection near the skull base.
Background: Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC. Methods: We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form. Results: Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76–0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72–0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76–0.84). Conclusions: Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.
Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death worldwide in 2020, with approximately 906,000 new cases and 830,000 deaths. 1 Hepatocellular carcinoma (HCC) accounts for 75%-85% of cases, 1 and incidence rates continue to increase rapidly, by about 3% per year in women and 4% per year in men. 2 It is the fourth most common malignancy and the third leading cause of tumour-related death in China. 3 Owing to its highly aggressive nature, HCC is one of the deadliest primary cancers, with a 5-year survival rate of 10% or less. 4 Advances in sequencing technology have enabled a genome comprising more than 3 billion DNA base pairs, 5 to be sequenced within hours, 6 leading to widespread application for diagnosis and research. Cancer is a genetic disease resulting from an accumulation of mutations in a particular tissue causing uncontrolled cell division. Genomic medicine, sequencing DNA extracted from a tumour, constructs a picture of the mutational events and drivers for oncogenesis. 7 The poor prognosis of advanced HCC warrants this personalized.The objective of this article was to assess the value of genotyping for the prognostication or response to the treatment of HCC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.