Potential use of ultrasound for the detection of cell changes in cancer treatment

Assessing engineered tissues and biomaterials using ultrasound imaging: In vitro and in vivo applications

Quantitative assessment of the structural, functional, and mechanical properties of engineered tissues and biomaterials is fundamental to their development for regenerative medicine applications. Ultrasound (US) imaging is a non-invasive, non-destructive, and cost-effective technique capable of longitudinal and quantitative monitoring of tissue structure and function across centimeter to sub-micron length scales. Here we present the fundamentals of US to contextualize its application for the assessment of biomaterials and engineered tissues, both in vivo and in vitro. We review key studies that demonstrate the versatility and broad capabilities of US for clinical and pre-clinical biomaterials research. Finally, we highlight emerging techniques that further extend the applications of US, including for ultrafast imaging of biomaterials and engineered tissues in vivo and functional monitoring of stem cells, organoids, and organ-on-a-chip systems in vitro.

Quantitative Ultrasound Spectroscopy for Differentiation of Hepatocellular Carcinoma from At-Risk and Normal Liver Parenchyma

PURPOSE

Quantitative ultrasound approaches can capture tissue morphologic properties to augment clinical diagnostics. This study aims to clinically assess whether quantitative ultrasound spectroscopy (QUS) parameters measured in hepatocellular carcinoma (HCC) tissues can be differentiated from those measured in at-risk or healthy liver parenchyma.

EXPERIMENTAL DESIGN

This prospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the Institutional Review Board. Fifteen patients with HCC, 15 non-HCC patients with chronic liver disease, and 15 healthy volunteers were included (31.1% women; 68.9% men). Ultrasound radiofrequency data were acquired in each patient in both liver lobes at two focal depths (3/9 cm). Region of interests (ROIs) were drawn on HCC and liver parenchyma. The average normalized power spectrum for each ROI was extracted, and a linear regression was fit within the -6 dB bandwidth, from which the midband fit (MBF), spectral intercept (SI), and spectral slope (SS) were extracted. Differences in QUS parameters between the ROIs were tested by a mixed-effects regression.

RESULTS

There was a significant intraindividual difference in MBF, SS, and SI between HCC and adjacent liver parenchyma (P < 0.001), and a significant interindividual difference between HCC and at-risk and healthy non-HCC parenchyma (P < 0.001). In patients with HCC, cirrhosis (n = 13) did not significantly change any of the three parameters (P > 0.8) in differentiating HCC from non-HCC parenchyma. MBF (P = 0.12), SI (P = 0.33), and SS (P = 0.57) were not significantly different in non-HCC tissue among the groups.

CONCLUSIONS

The QUS parameters are significantly different in HCC versus non-HCC liver parenchyma, independent of underlying cirrhosis. This could be leveraged for improved HCC detection with ultrasound in the future.

Photoacoustic simulations of microvascular bleeding: spectral analysis and its application for monitoring vascular-targeted treatments

Solid tumors are typically supplied nutrients by a network of irregular blood vessels. By targeting these vascular networks, it might be possible to hinder cancer growth and metastasis. Vascular disrupting agents induce intertumoral hemorrhaging, making photoacoustic (PA) imaging well positioned to detect bleeding due to its sensitivity to hemoglobin and its various states. We introduce a fractal-based numerical model of intertumoral hemorrhaging to simulate the PA signals from disrupted tumor blood vessels. The fractal model uses bifurcated cylinders to represent vascular trees. To mimic bleeding from blood vessels, hemoglobin diffusion from microvessels was simulated. In the simulations, the PA signals were detected by a linear array transducer (30 MHz center frequency) of four different vascular trees. The power spectrum of each beamformed PA signal was computed and fitted to a straight line within the −6-dB bandwidth of the receiving transducer. The spectral slope and midband fit (MBF) based on the fit decreased by 0.11  dB  /  MHz and 2.12 dB, respectively, 1 h post bleeding, while the y-intercept increased by 1.21 dB. The results suggest that spectral PA analysis can be used to measure changes in the concentration and spatial distribution of hemoglobin in tissue without the need to resolve individual vessels. The simulations support the feasibility of using PA imaging and spectral analysis in cancer treatment monitoring by detecting microvessel disruption.

Tumour Vascular Shutdown and Cell Death Following Ultrasound-Microbubble Enhanced Radiation Therapy

High-dose radiotherapy effects are regulated by acute tumour endothelial cell death followed by rapid tumour cell death instead of canonical DNA break damage. Pre-treatment with ultrasound-stimulated microbubbles (USMB) has enabled higher-dose radiation effects with conventional radiation doses. This study aimed to confirm acute and longitudinal relationships between vascular shutdown and tumour cell death following radiation and USMB in a wild type murine fibrosarcoma model using in vivo imaging.

Methods: Tumour xenografts were treated with single radiation doses of 2 or 8 Gy alone, or in combination with low-/high-concentration USMB. Vascular changes and tumour cell death were evaluated at 3, 24 and 72 h following therapy, using high-frequency 3D power Doppler and quantitative ultrasound spectroscopy (QUS) methods, respectively. Staining using in situ end labelling (ISEL) and cluster of differentiation 31 (CD31) of tumour sections were used to assess cell death and vascular distributions, respectively, as gold standard histological methods.

Results: Results indicated a decrease in the power Doppler signal of up to 50%, and an increase of more than 5 dBr in cell-death linked QUS parameters at 24 h for tumours treated with combined USMB and radiotherapy. Power Doppler and quantitative ultrasound results were significantly correlated with CD31 and ISEL staining results (p < 0.05), respectively. Moreover, a relationship was found between ultrasound power Doppler and QUS results, as well as between micro-vascular densities (CD31) and the percentage of cell death (ISEL) (R² 0.5-0.9).

Conclusions: This study demonstrated, for the first time, the link between acute vascular shutdown and acute tumour cell death using in vivo longitudinal imaging, contributing to the development of theoretical models that incorporate vascular effects in radiation therapy. Overall, this study paves the way for theranostic use of ultrasound in radiation oncology as a diagnostic modality to characterize vascular and tumour response effects simultaneously, as well as a therapeutic modality to complement radiation therapy.

Macrophages as a potential tumor-microenvironment target for noninvasive imaging of early response to anticancer therapy

As a result of therapy-induced apoptosis, peripheral blood monocytes are recruited to tumors, where they become tumor-associated macrophages (TAMs). To date, few studies have investigated noninvasive molecular imaging for assessment of macrophage infiltration in response to therapy-induced apoptosis. Here, noninvasive assessment of changes in tumor accumulation of TAMs was proposed as a new way to measure early tumor response to anticancer therapy. Three different nanoparticles, QD710-Dendron quantum dots (QD710-D), Ferumoxytol, and PG-Gd-NIR813, were used for near-infrared fluorescence imaging, T2-weighted magnetic resonance imaging, and dual optical/T1-weighted MR imaging, respectively, in the MDA-MB-435 tumor model. Treatment with Abraxane induced tumor apoptosis and infiltrating macrophages. In spite of markedly different physicochemical properties among the nanoparticles, in vivo imaging revealed increased uptake of all three nanoparticles in Abraxane-treated tumors compared with untreated tumors. Moreover, imaging visualized increased uptake of QD710-D in MDA-MB-435 tumors but not in drug-resistant MDA-MB-435R tumors grown in the mice treated with Abraxane. Our results suggest that infiltration of macrophages due to chemotherapy-induced apoptosis was partially responsible for increased nanoparticle uptake in treated tumors. Noninvasive imaging techniques in conjunction with systemic administration of imageable nanoparticles that are taken up by macrophages are a potentially useful tool for assessing early treatment response.

In Vivo Multiparametric Ultrasound Imaging of Structural and Functional Tumor Modifications during Therapy

Longitudinal imaging techniques are needed that can meaningfully probe the tumor microenvironment and its spatial heterogeneity. Contrast-enhanced ultrasound, shear wave elastography and quantitative ultrasound are ultrasound-based techniques that provide information on the vascular function and micro-/macroscopic tissue structure. Modifications of the tumor microenvironment induced by cytotoxic and anti-angiogenic molecules in ectopic murine Lewis lung carcinoma tumors were monitored. The most heterogenous structures were found in tumors treated with anti-angiogenic drug that simultaneously accumulated the highest levels of necrosis and fibrosis. The anti-angiogenic group presented the highest number of correlations between parameters related to vascular function and those related to the micro-/macrostructure of the tumor microenvironment. Results suggest how patterns of multiparametric ultrasound modifications can be related to provide a more insightful marker of changes occurring within tumors during therapy.

Computer aided prognosis for cell death categorization and prediction in vivo using quantitative ultrasound and machine learning techniques

PURPOSE

At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based clinical standard for the early assessment and monitoring of cancer treatment response. Here, the authors have developed, for the first time, a complete computer-aided-prognosis (CAP) system based on multiparametric quantitative ultrasound (QUS) spectroscopy methods in association with texture descriptors and advanced machine learning techniques. This system was used to noninvasively categorize and predict cell death levels in fibrosarcoma mouse tumors treated using ultrasound-stimulated microbubbles as novel endothelial-cell radiosensitizers.

METHODS

Sarcoma xenograft tumor-bearing mice were treated using ultrasound-stimulated microbubbles, alone or in combination with x-ray radiation therapy, as a new antivascular treatment. Therapy effects were assessed at 2-3, 24, and 72 h after treatment using a high-frequency ultrasound. Two-dimensional spectral parametric maps were generated using the power spectra of the raw radiofrequency echo signal. Subsequently, the distances between "pretreatment" and "post-treatment" scans were computed as an indication of treatment efficacy, using a kernel-based metric on textural features extracted from 2D parametric maps. A supervised learning paradigm was used to either categorize cell death levels as low, medium, or high using a classifier, or to "continuously" predict the levels of cell death using a regressor.

RESULTS

The developed CAP system performed at a high level for the classification of cell death levels. The area under curve of the receiver operating characteristic was 0.87 for the classification of cell death levels to both low/medium and medium/high levels. Moreover, the prediction of cell death levels using the proposed CAP system achieved a good correlation (r = 0.68,  p < 0.001) with histological cell death levels as the ground truth. A statistical test of significance between individual treatment groups with the corresponding control group demonstrated that the predicted levels indicated the same significant changes in cell death as those indicated by the ground-truth levels.

CONCLUSIONS

The technology developed in this study addresses a gap in the current standard of care by introducing a quality control step that generates potentially actionable metrics needed to enhance treatment decision-making. The study establishes a noninvasive framework for quantifying levels of cancer treatment response developed preclinically in tumors using QUS imaging in conjunction with machine learning techniques. The framework can potentially facilitate the detection of refractory responses in patients to a certain cancer treatment early on in the course of therapy to enable switching to more efficacious treatments.

Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer

A noninvasive computer-aided-theragnosis (CAT) system was developed for the early therapeutic cancer response assessment in patients with locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy. The proposed CAT system was based on multi-parametric quantitative ultrasound (QUS) spectroscopic methods in conjunction with advanced machine learning techniques. Specifically, a kernel-based metric named maximum mean discrepancy (MMD), a technique for learning from imbalanced data based on random undersampling, and supervised learning were investigated with response-monitoring data from LABC patients. The CAT system was tested on 56 patients using statistical significance tests and leave-one-subject-out classification techniques. Textural features using state-of-the-art local binary patterns (LBP), and gray-scale intensity features were extracted from the spectral parametric maps in the proposed CAT system. The system indicated significant differences in changes between the responding and non-responding patient populations as well as high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. The proposed CAT system achieved an accuracy of 85%, 87%, and 90% on weeks 1, 4 and 8, respectively. The sensitivity and specificity of developed CAT system for the same times was 85%, 95%, 90% and 85%, 85%, 91%, respectively. The proposed CAT system thus establishes a noninvasive framework for monitoring cancer treatment response in tumors using clinical ultrasound imaging in conjunction with machine learning techniques. Such a framework can potentially facilitate the detection of refractory responses in patients to treatment early on during a course of therapy to enable possibly switching to more efficacious treatments.

Quantitative evaluation of cell death response in vitro and in vivo using conventional-frequency ultrasound

Previous studies using high-frequency ultrasound have suggested that radiofrequency (RF) spectral analysis can be used to quantify changes in cell morphology to detect cell death response to therapy non-invasively. The study here investigated this at conventional-frequencies, frequently used in clinical settings.

Spectral analysis was performed using ultrasound RF data collected with a clinical ultrasound platform. Acute myeloid leukemia (AML-5) cells were exposed to cisplatinum for 0–72 hours in vitro and prepared for ultrasound data collection. Preclinical in vivo experiments were also performed on AML-5 tumour-bearing mice receiving chemotherapy.

The mid-band fit (MBF) spectral parameter demonstrated an increase of 4.4 ± 1.5 dBr for in vitro samples assessed 48 hours after treatment, a statistically significant change (p < 0.05) compared to control. Further, in vitro concentration-based analysis of a mixture of apoptotic and untreated cells indicated a mean change of 10.9 ± 2.4 dBr in MBF between 0% and 40% apoptotic cell mixtures. Similar effects were reproduced in vivo with an increase of 4.6 ± 0.3 dBr in MBF compared to control, for tumours with considerable apoptotic areas within histological samples. The alterations in the size of cells and nuclei corresponded well with changes measured in the quantitative ultrasound (QUS) parameters.

Categorizing extent of tumor cell death response to cancer therapy using quantitative ultrasound spectroscopy and maximum mean discrepancy

Quantitative ultrasound (QUS) spectroscopic techniques in conjunction with maximum mean discrepancy (MMD) have been proposed to detect, and to classify noninvasively the levels of cell death in response to cancer therapy administration in tumor models. Evaluation of xenograft tumor responses to cancer treatments were carried out using conventional-frequency ultrasound at different times after chemotherapy exposure. Ultrasound data were analyzed using spectroscopic techniques and multi-parametric QUS spectral maps were generated. MMD was applied as a distance criterion, measuring alterations in each tumor in response to chemotherapy, and the extent of cell death was classified into less/more than 20% and 40% categories. Statistically significant differences were observed between "pre-" and "post-treatment" groups at different times after chemotherapy exposure, suggesting a high capability of proposed framework for detecting tumor response noninvasively. Promising results were also obtained for categorizing the extent of cell death response in each tumor using the proposed framework, with gold standard histological quantification of cell death as ground truth. The best classification results were obtained using MMD when applied on histograms of QUS parametric maps. In this case, classification accuracies of 84.7% and 88.2% were achieved for categorizing extent of tumor cell death into less/more than 20% and 40%, respectively.

Quantitative ultrasound visualization of cell death: emerging clinical applications for detection of cancer treatment response

Differentiable echogeneities exhibited by living and dead cells enables the monitoring of cell death response via quantitative ultrasound techniques at high-frequencies and recently at clinical range frequencies. Such capability can be potentially employed to provide rapid and quantitative functional information in real time, and at the patient bedside for evaluating therapy response early following treatment. This paper summarizes backgrounds on quantitative ultrasound visualization of cell death and highlights its potential capabilities for monitoring cancer treatment response, where favorable results have been reported, according to a recent pilot clinical study.