Abstract
Tumor heterogeneity can be elucidated by mapping subregions of the lesion with differential imaging characteristics, called habitats. Dynamic Contrast Enhanced (DCE-)MRI can depict the tumor microenvironments by identifying areas with variable perfusion and vascular permeability, since individual tumor habitats vary in the rate and magnitude of the contrast uptake and washout. Of particular interest is identifying areas of hypoxia, characterized by inadequate perfusion and hyper-permeable vasculature. An automatic procedure for delineation of tumor habitats from DCE-MRI was developed as a two-part process involving: (1) statistical testing in order to determine the number of the underlying habitats; and (2) an unsupervised pattern recognition technique to recover the temporal contrast patterns and locations of the associated habitats. The technique is examined on simulated data and DCE-MRI, obtained from prostate and brain pre-clinical cancer models, as well as clinical data from sarcoma and prostate cancer patients. The procedure successfully identified habitats previously associated with well-perfused, hypoxic and/or necrotic tumor compartments. Given the association of tumor hypoxia with more aggressive tumor phenotypes, the obtained in vivo information could impact management of cancer patients considerably.
Highlights
Hypoxia is a key determinant of tumor habitats as it favors molecular pathways towards tumor aggressiveness
In Stoyanova et al.[21], the number of independent signal-versus-time curves in Dynamic Contrast Enhanced (DCE-)Magnetic Resonance Imaging (MRI) data, k, was determined via visual inspection of the Principal Components (PCs), following Principal Component Analysis (PCA) of DCE-MRI data from the Volume of Interest (VOI). k was the numbers of signal-related PCs
An approach for identification of areas of hypoxia in solid tumors from DCE-MRI was previously developed, where the number of differential temporal contrast patterns was inferred via visual inspection of the principal components (PCs) of the dataset[21]
Summary
Hypoxia is a key determinant of tumor habitats as it favors molecular pathways towards tumor aggressiveness. Dynamic Contrast Enhanced (DCE-) MRI can characterize the microenvironment in solid tumors by determining areas of inadequate or heterogeneous perfusion with hyper-permeable vasculature[18]. Areas of tumor hypoxia can be detected using the signal-versus-time curves of DCE-MRI data as a surrogate marker[21]. The technique is based on an unsupervised pattern recognition (PR) technique that determines the differential signal-versus-time curve pattern associated with any given tumor habitat. The number of temporal contrast patterns were determined visually based on the Principal Components (PCs) of the DCE-MRI signal-versus-time curves[21]. A statistical approach for determining the number of significant PCs is presented This number is utilized in an unsupervised PR algorithm in order to delineate and quantitatively characterize tumor habitats. The technique is examined on simulated data and DCE-MRI data obtained from prostate and brain cancer pre-clinical models, as well as clinical data from patients with sarcoma and prostate cancer
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
