Quantitative Ultrasound Comparison of MAT and 4T1 Mammary Tumors in Mice and Rats Across Multiple Imaging Systems

Abstract

Objectives Quantitative ultrasound estimates such as the frequency-dependent backscatter coefficient (BSC) have the potential to enhance noninvasive tissue characterization and to identify tumors better than traditional B-mode imaging. Thus, investigating system independence of BSC estimates from multiple imaging platforms is important for assessing their capabilities to detect tissue differences. Methods Mouse and rat mammary tumor models, 4T1 and MAT, respectively, were used in a comparative experiment using 3 imaging systems (Siemens, Ultrasonix, and VisualSonics) with 5 different transducers covering a range of ultrasonic frequencies. Results Functional analysis of variance of the MAT and 4T1 BSC-versus-frequency curves revealed statistically significant differences between the two tumor types. Variations also were found among results from different transducers, attributable to frequency range effects. At 3 to 8 MHz, tumor BSC functions using different systems showed no differences between tumor type, but at 10 to 20 MHz, there were differences between 4T1 and MAT tumors. Fitting an average spline model to the combined BSC estimates (3–22 MHz) demonstrated that the BSC differences between tumors increased with increasing frequency, with the greatest separation above 15 MHz. Confining the analysis to larger tumors resulted in better discrimination over a wider bandwidth. Conclusions Confining the comparison to higher ultrasonic frequencies or larger tumor sizes allowed for separation of BSC-versus-frequency curves from 4T1 and MAT tumors. These constraints ensure that a greater fraction of the backscattered signals originated from within the tumor, thus demonstrating that statistically significant tumor differences were detected.

MRI reveals increased tumorigenesis following high fat feeding in a mouse model of triple‐negative breast cancer

“…Due to their inability to provide precise soft tissue imaging, computed tomography and ultrasound have not been adequate to monitor neoplastic changes during early stage mammary cancer progression in mice . On the other hand, MRI provides excellent soft tissue contrast, can detect early cancer in vivo much more reliably than other imaging modalities, allows evaluation of the surrounding parenchyma, and allows accurate tumor volume measurements over time.…”

“…Due to their inability to provide precise soft tissue imaging, computed tomography and ultrasound have not been adequate to monitor neoplastic changes during early stage mammary cancer progression in mice. 15,16 On the other hand, MRI provides excellent soft tissue contrast, can detect early cancer in vivo much more reliably than other imaging modalities, allows evaluation of the surrounding parenchyma, and allows accurate tumor volume measurements over time. Previous work from our laboratory demonstrated that in vivo T 2 -weighted MRI reliably detects very early in situ mammary tumors in SV40TAg mice with high sensitivity and specificity, and differentiates in situ from invasive cancer.…”

Robust probabilistic classification applicable to irregularly sampled functional data

“…In practice, medical or biological data are often collected with repetition from distinct subjects (or clusters) which induces correlation between curves. For example, in QUS, it is common to obtain multiple scans from each region of interest (Wirtzfeld et al, 2015). To account for dependency structure among curves, we extend (3) to include cluster-level random function with coefficients assumed to follow t-distribution with the same df vin (3).…”

“…Furthermore, noninvasive scanning raises the potential for outlying measurements due to unexpected contamination, e.g., heterogeneity due to the inclusion of neighboring tissue in the scanned region. The statistically distinct behaviors of BSC on different types of the tumor were demonstrated by Wirtzfeld et al (2015). A subsequent goal is to develop a classifier to predict the class for a new measured BSC curve or set of curves.…”

Structure Function Estimated From Histological Tissue Sections

“…Earlier QUS successes for tissue characterization involved various organs such as the eye [1], [2], prostate [3], kidney [4], heart [5], [6], blood [7], [8], breast [9]–[12], liver [13], cancerous lymph nodes [14], and apoptotic cells [15], [16]. Recent development in QUS has expanded to clinical settings.…”