Publications by authors named "Victoria Noe-Kim"
Background Splenic biopsy is rarely performed because of the perceived risk of hemorrhagic complications. Purpose To evaluate the safety of large bore (≥18 gauge) image-guided splenic biopsy. Materials and Methods This retrospective study included consecutive adult patients who underwent US- or CT-guided splenic biopsy between March 2001 and March 2022 at eight academic institutions in the United States.
View Article and Find Full Text PDFSplenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds.
View Article and Find Full Text PDFBackground Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver.
View Article and Find Full Text PDFArticle Synopsis
- Understanding how breast cancers progress to invasive ductal carcinoma (IDC) could lead to better treatments, but it's tough to identify genetic markers and the influence of the tumor environment due to the many subtypes of breast cancer.
- A new in-vitro microfluidic assay was created to measure the invasion capabilities of different breast cancer subtypes within a 3D collagen matrix surrounding cancer cell structures.
- The study found that while two aggressive subtypes (MDA-MB-231 and SUM-159PT) invaded the matrix, the less aggressive luminal A subtype (MCF-7) did not, with SUM-159PT showing greater invasive behavior, suggesting the assay could help predict the invasive potential of various breast cancer types.
Successful drug delivery and overcoming drug resistance are the primary clinical challenges for management and treatment of cancer. The ability to rapidly screen drugs and delivery systems within physiologically relevant environments is critically important; yet is currently limited due to lack of appropriate tumor models. To address this problem, we developed the Tumor-microenvironment-on-chip (T-MOC), a new microfluidic tumor model simulating the interstitial flow, plasma clearance, and transport of the drug within the tumor.
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