Poster Session B   |   7:00am Expo - Hall A & C   |   Poster ID #112

Quantitative assessment of the cancer mechanical microenvironment in vivo using novel and non-invasive multi-modal imaging methods

Academic Research
Prevention, Early Detection, Implementation, and Dissemination
FDA Status:
Not Applicable
CPRIT Grant:
Cancer Site(s):
All Cancers
Raffaella Righetti
Texas A&M University
Hadiur Khan
Texas A&M University
Sharmin Majumder
Texas A&M University
Tauhid Islam
Texas A&M University
Robin Vander Pol
Houston Methodist
Anthony Wood
Houston Methodist
Corrine Chua
Houston Methodist


The mechanical microenvironment in cancers significantly affects malignant progression, response to treatment, and the distribution of drugs and imaging agents. Mechanopathological parameters such as interstitial fluid pressure and solid stress can carry significant information on patient response evaluation and outcome risk stratification prediction. They can affect gene expressions, cell signaling activities, and metastatic potential of cancer cells. As of today, however, non-invasive and quantitative assessment of the tumor mechanical microenvironment in vivo remains very challenging.

Our group has pioneered the development of novel ultrasound-based imaging technologies to assess the behavior of tissues that can be modelled as biphasic materials. This behavior has been shown to be directly linked to and modulated by underlying transport phenomena in cancers. In the past few years, we have demonstrated that it is possible to generate high resolution images of multiple mechanopathological parameters of great clinical significance. These include Young’s modulus (YM), Poisson’s ratio (PR), vascular permeability (VP), interstitial permeability (IP), extracellular volume fraction (EVF), interstitial fluid pressure (IFP) and solid stress (SS). All these cancer parameters can be reconstructed, simultaneously and independently, from a single set of ultrasonic data (~ 1 minute of acquisition). These technologies have the potential to provide a set of truly personalized imaging markers to assess disease progression and treatment effectiveness.


In vivo experiments were performed on 24 mice with triple negative breast cancer (MDA-MB-231) injected in the mammary fat pad. Ultrasound data were acquired from untreated (13) and treated (11) mice in isoflurane-anesthetized conditions. The same cancers were scanned for three consecutive weeks to evaluate the evolution of the various cancer parameters during tumor progression or after treatment. Under a standard creep compression protocol, a small uniaxial load (3 kPa ± 1 kPa) was applied using a compressor plate attached to the transducer, while RF datasets were acquired for approximately 60 seconds. RF data were acquired using a standard ultrasound diagnostic system with a 38-mm linear array transducer (center frequency: 6.6 MHz, bandwidth: 5–14 MHz). The applied force was monitored using a dedicated pressure sensor. The in vivo animal study was approved by the Houston Methodist Research Institute, Institutional Animal Care and Use Committee.

From the acquired time-sequenced RF data, the local tissue displacements and volumetric strains were estimated using dedicated algorithms. Using these imaging data as input to previously developed cancer models, high resolution maps of the underlying cancer mechanical parameters (i.e., YM and PR) and transport parameters (VP, IP, EVF, IFP and SS) were reconstructed. Selected parameters (IFP and SS) were independently validated using an invasive established method. Histological analysis was performed on all cancers at the end time point.


The results of this study show that the cancer mechanopathological parameters estimated using the proposed novel ultrasound imaging technologies evolve during cancer progression and decrease after treatment. The spatial distribution of these parameters was found to be highly heterogenous in untreated tumors. Good statistical agreement was observed between the ultrasound-estimated parameters and corresponding independent validations.


Emerging quantitative imaging techniques may be key to the achievement of truly personalized and optimized diagnosis and treatment of cancers. This study shows that it is possible to image and quantify multiple cancer parameters using non-invasive diagnostic imaging systems, commonly employed in the clinics. These parameters have the potential to be used as markers of cancer growth and as quantitative tools to assess treatment effectiveness.