Abstract PS3-29: Pixel-level tissue classification from ultrasound images of breast cancer and direct comparison to matched histological measurements

Abstract

Abstract Introduction: The use of noninvasive ultrasound (US) for quantitative tissue characterization has been an exciting research prospect for several decades now. Herein the challenge is to find hidden patterns in the US data to reveal more information about tissue function and pathology that cannot be seen in the more conventional US images. A new pixel-level analysis technique has been developed by our group for tissue classification. Termed H-scan US (H stands for hue or Hermite), this imaging approach links a special class of nth-ordered Gaussian-weighted Hermite functions (GHn) to the physics of US scattering and reflection from different tissue structures. The sensitivity of in vivo H-scan US imaging to subtle changes in scatterer size is a question that has not been fully answered. To that end, the purpose of this study was to compare local H-scan US image intensity to direct (co-registered) histological measures made at the cellular level. Methodology Female nude athymic mice were implanted into mammary fatty pad with breast cancer cells (N = 8). Implanted tumors were allowed to grow for about four weeks before study enrollment. Image data was acquired using a programmable US scanner (Vantage 256, Verasonics Inc) equipped with a 256-element L22-8v CMUT linear array transducer (Kolo Medical). Plane wave imaging with 5 angles was performed at a center frequency of 15 MHz. To generate the H-scan US image, three parallel convolution filters (GH2, GH6, and GH10) were applied to the radiofrequency (RF) data sequences to measure the relative strength of the received signals. After envelope detection, the relative strength of the filter outputs is color coded whereby the lower frequency (GH2 = 9 MHz) backscattered US signal components are assigned to the R channel, moderate frequency (GH6 = 15 MHz) signals are assigned to the G channel, and the higher frequency (GH10 = 21 MHz) signals to the B channel. After performing H-scan US, the imaging cross-section was marked, and the tumors were excised and sliced along the same plane for histologic processing. After nuclear staining, tissue sections were scanned and digitized using confocal microscopy and fluorescent filters for DAPI. Automated segmentation of each cancer cell nucleus in the histologic sections was performed using an active contour technique. US images were interpolated to the same number of pixels as the histology image and spatial alignment between the two was performed. Lastly, nucleus size and density from histologic sections was compared to local H-scan US image features. Results: Custom software was developed to compare local US image features at the pixel level to that of co-registered histologic images and measurements of nuclear size and density. In an attempt to properly match the histological and the H-scan US images, three different sized kernels of 0.16 ✗ 0.16 mm, 0.32 ✗ 0.32 mm or 0.5 ✗ 0.5 mm were used to partition both the H-scan US and histology images into 1888, 448 or 62 distinct region-of-interests (ROIs), respectively. Mean nuclear size and density measurements from the histology ROIs was compared to the mean H-scan US image intensity from the many spatially matched ROI locations. A statistically significant linear relationship was found between local H-scan US image intensity and nuclear size (R2 > 0.4, < 0.001) and density (R2 > 0.6, p < 0.001). Conclusions: Preliminary results from use of an animal model of breast cancer reveals that in vivo H-scan US images positively correlated with physical measures of nucleus size and density as quantified from co-registered histologic images. Citation Format: Mawia Khairalseed, Shreya Reddy, Jane Song, Girdhari Rijal, Kenneeth Hoyt. Pixel-level tissue classification from ultrasound images of breast cancer and direct comparison to matched histological measurements [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-29.