Main content

Home

Menu

Loading wiki pages...

View
Wiki Version:
This project is being conducted in collaboration with the Liver Imaging Reporting and Data System (LI-RADS) [Steering Committee][1] and Research Working Group. We are investigating the performance of LI-RADS diagnostic algorithms to combine imaging features to assign a LI-RADS category to individual liver observations in high-risk patients. Each LI-RADS category corresponds to a level of risk for hepatocellular carcinoma (HCC). For CT/MRI, this includes five major and 21 ancillary features whereas for contrast-enhanced ultrasound (CEUS), there are three major and five ancillary features. Prior systematic reviews have explored the association of individual LI-RADS categories and individual LI-RADS imaging features with a diagnosis of HCC. We have so far completed the following meta-analyses using our current LI-RADS individual patient data (IPD) cohort: • [CT/MRI and CEUS LI-RADS Major Features Association with Hepatocellular Carcinoma: Individual Patient Data Meta-Analysis.][2] • [Impact of Reference Standard on CT, MRI, and Contrast-enhanced US LI-RADS Diagnosis of Hepatocellular Carcinoma: A Meta-Analysis.][3] • [LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis][4] • [Individual Participant Data Meta-Analysis of LR-5 in LI-RADS Version 2018 versus Revised LI-RADS for Hepatocellular Carcinoma Diagnosis][5] • [Comparative Performance of 2018 LI-RADS versus Modified LIRADS (mLI-RADS): An Individual Participant Data Meta-Analysis][6] • [Is concurrent LR‑5 associated with a higher rate of hepatocellular carcinoma in LR‑3 or LR‑4 observations? An individual participant data meta‑analysis][7] Multiple important questions remain that we believe can be addressed using IPD meta-analysis. An IPD meta-analysis involves collecting and pooling de-identified primary study data from prior publications to increase study sample size permitting higher-level subgroup analysis. Increasingly, IPD meta-analyses are seen as the standard for evidence in many fields. To learn more about the project, see the full [Methods Overview][8]. To see the members of the LI-RADS IPD Group, see [Who are we][9]. **Funding**: Joan Sealy Trust for Cancer Research, Canadian Institute for Health Research (CIHR) Operating Grant, Radiological Society of North America (RSNA) Research Scholar Grant [1]: https://www.acr.org/-/media/ACR/Files/RADS/LI-RADS/LIRADS-Steering-Committee-and-charter.pdf [2]: https://pubmed.ncbi.nlm.nih.gov/34783596/ [3]: https://pubs.rsna.org/doi/10.1148/radiol.212340 [4]: https://pubs.rsna.org/doi/10.1148/radiol.231501 [5]: https://pubs.rsna.org/doi/10.1148/radiol.231656 [6]: https://onlinelibrary.wiley.com/doi/10.1002/jmri.29167 [7]: https://doi.org/10.1007/s00261-024-04580-6 [8]: https://osf.io/tdv7j/wiki/Methods%20Overview/ [9]: https://osf.io/tdv7j/wiki/Who%20are%20we/
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.