4aBA13 – In-vivo assessment of lymph nodes using quantitative ultrasound on a clinical scanner: a preliminary study
Cameron Hoerig, Ph.D., firstname.lastname@example.org
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.
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).
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.