Cameron Hoerig, Ph.D., cah4016@med.cornell.edu
Weill Cornell Medicine
Department of Radiology
416 E 55th St., MR-007
New York, NY 10022

Popular version of 4aBA13 – In vivo assessment of lymph nodes using quantitative ultrasound on a clinical scanner: A preliminary study
Presented Thursday morning, May 26, 2022
182nd ASA Meeting, Denver
Click here to read the abstract

Cancer can spread through the body via the lymphatic system. When a primary tumor is found in a patient, biopsies may be performed on one or more nearby lymph nodes (LNs) to look for evidence of cancerous cells and aid in disease staging and treatment planning. LN biopsies typically involve first removing the node, slicing it into very thin sections (thinner than a human hair), and staining the sections. Next, a pathologist views these sections under a microscope to look for abnormal cells. Because the tissue sections are so thin and the node is comparatively large, it is infeasible for a pathologist to look at every slide for each LN. Consequently, small clumps of cancerous cells may be missed. Similarly, biopsies performed via fine needle aspiration (FNA) – wherein a very thin needle is used to extract very small tissue samples throughout a LN while it is still in the body – also comes with the risk of missing cancerous cells. As an example, the false-negative rate for biopsies on axillary lymph nodes is as high as 10%!

In this work, we are using an ultrasonography technique called quantitative ultrasound (QUS) to assess LNs in vivo and determine if metastatic cells are present without the need for biopsy. Different tissue types scatter the ultrasound wave in different ways. However, the processing that typically occurs in clinical scanners strips this information away before displaying conventional B-mode images. Examples of B-mode images from benign and metastatic lymph nodes are displayed in Fig. 1 along with optical microscopy pictures of corresponding FNA results. The microscopy images show a clear contrast in the microstructure between normal and cancerous cells that is not invisible in the ultrasound B-mode images.

(Left column) B-mode images of metastatic and benign lymph nodes. (Right column) The corresponding optical microscopy images of stained tissue samples from FNA biopsy show the difference in tissue microstructure between benign and metastatic lymph nodes.

QUS methods extract information from the ultrasonic signal before the typical image processing steps to make inferences about tissue microstructure. Theoretically, these methods are independent of the scanner and operator, meaning the same information can be obtained by any sonographer using any scanner and the information obtained depends only on the underlying tissue microstructure. QUS methods used in this study glean information about the scatterer diameter, effective acoustic concentration, and scatterer organization (randomly positioned vs organized).

Left and middle columns are representative color overlays of scatterer diameter and acoustic concentration from QUS processing. The right column is the resulting classification from the trained LDA.

We have thus far collected data on 16 LNs from 15 cancer patients with a known primary tumor. The same clinical GE Logiq E9 scanner was used to collect ultrasound echo data for QUS processing and for ultrasound-guided FNA. Metastatic status of the LNs was determined from the FNA results. QUS methods were applied to the US images to obtain a total of 9 parameters. From these, we determined scatterer diameter and effective acoustic concentration were most effective at differentiating benign and metastatic nodes. Using these two parameters as input to a linear discriminant analysis (LDA) – a type of machine learning algorithm – we correctly classified 95% of US images as containing a benign or metastatic LN. Examples of QUS parameter maps overlayed on B-mode images, and the resulting classification by LDA, are provided in Fig. 2. The associated ROC plot had an area under the ROC curve of 0.90, showing excellent ability of LDA to identify metastatic nodes from only two QUS parameters. These preliminary results demonstrate the feasibility of characterizing LNs in vivo at conventional frequencies using a clinical scanner, potentially offering a means to complement US-FNA practice and reduce unnecessary LN biopsies.

Richard KP Benninger – richard.benninger@cuanschutz.edu
University of Colorado Anschutz medical campus
1775 Aurora Ct
Aurora, CO. 80045

Popular version of 2aBAb1 – Applying ultrasound phase-change contrast agents to guide therapeutic intervention in type 1 diabetes
Presented Tuesday morning, May 24th, 2022
182^nd ASA Meeting
Click here to read the abstract

Type1 diabetes is an autoimmune disease in which the insulin-producing cells in the pancreas are destroyed. As a result people with type1 diabetes have to take insulin for the rest of their life. This is not a cure, and as well as the significant patient burden there are still risks for complications of diabetes that include eye, kidney and heart damage, as well as potentially falling into a coma from insulin overdose and low blood sugar. Strategies have been developed to prevent type1 diabetes through immune therapies that stop the destruction of insulin producing cells. Treatment early in the disease process, before significant destruction of insulin producing cells will be needed. However it is challenging to predict if an individual will get type1 diabetes and when, limiting the ability to intervene early.

Imaging approaches have been explored to detect the presence of autoimmune disease and concurrent inflammation in the pancreas, and loss of the insulin-producing cells. However there have been limited successes. A potential approach is based on the blood vessels become leaky during the autoimmune disease and inflammation in the pancreas. Thus small particles below 1um diameter can leak and accumulate in the diseased tissue. We have proposed to leverage the inherent advantages of ultrasound imaging that include deployability, cost-effectiveness and safety profile. Ultrasound contrast agents consist of gas filled bubbles (microbubbles). However the size of thee microbubbles means that they cannot access diseased tissue and are restricted to blood vessels. We have utilized a novel phase-change ultrasound contrast agent that consists of a condensed liquid droplet that is stable at body temperature and in circulation. However the acoustic beam from an ultrasound transducer can vaporize these droplets into microbubbles that provide ultrasound contrast. Thus these phase-change agents serve as circulating microbubble precursors that can access diseased tissue.

We tested whether these ultrasound phase change agents can access the injured tissue in the pancreas resulting from autoimmune disease, and whether accumulation of the contrast agents could be detected in ultrasound imaging. We found in pre-clinical models of type1 diabetes that significant accumulation of ultrasound phase change agents were observed in the pancreas, which was measurable by ultrasound (Figure 1). This accumulation correlated with the presence of autoimmune disease and decline in insulin-producing cells. Importantly the accumulation and ultrasound contrast was only present in the pancreas in models of diabetes: no accumulation was observed in non-diseased tissues. Further the accumulation of ultrasound phase change agents and ultrasound contrast correlated with the development of diabetes: models that developed diabetes rapidly or lacked therapeutic prevention showed a much higher contrast than those models that  developed diabetes slowly or showed therapeutic prevention of diabetes. Most importantly elevated contrast was measured very early in the disease process, earlier than the current gold standard measurement of circulating insulin autoantibodies.

Figure 1: Conventional B-mode and contrast mode images before and after infusion and activation of phase-change ultrasound contrast agent. P=Pancreas, K=Kidney, S=Spleen.

As such the use of phase-change ultrasound contrast agents shows significant promise for detecting and tracking the presence of autoimmune disease and inflammation in the pancreas that heralds the development of type1 diabetes (Figure 2). Such a measurement would guide therapeutic intervention to prevent type1 diabetes, as well as assess the efficacy of such a treatment. Successful disease prevention will avoid the need for lifelong insulin therapy and complications of diabetes.

Figure 2: Schematic illustrating use of phase-change ultrasound contrast agents to detect autoimmune disease in the pancreas.