Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI

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.

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

“…However, because cancer cells are independently promoting local angiogenesis without regard for the rest of the tumor, a chaotic and leaky vasculature results. [143][144][145][146] Thus, these subregions, or habitats, are expected to contain cells with similar genotypes and phenotypes, including those that confer therapy resistance. 143 Spatial variation in environmental stressors drives a natural selection for cells that are most fit within their specific patterns of oxygen and nutrient availability, and the buildup of acidic waste products.…”

“…174 DCE-MRI can be analyzed quantitatively by fitting the data to ad hoc or pharmacokinetic models that measure the time course of the presence of Cancer December 15, 2018 gadolinium within the tissues. 145,179,180 However, relevant to habitat imaging is that the lack of perfusion would be expected to correlate to hypoxia only in areas with relatively high cell density. 160,174 These parameters have been related to tumor angiogenesis and hypoxia by histological coregistration.…”

Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures

“…These ROIs are usually delineated using the information provided by morphological MRI sequences such as T 1 weighted, T 2 weighted, FLAIR, and T 1 weighted post‐gadolinium ( T 1 c ) acquisitions. Recent approaches, however, focus on the definition of functional habitats based on tissue features at the voxel scale through the information provided by MRI sequences such as diffusion weighted imaging, perfusion weighted imaging (PWI), or MRS, among others …”

“…Recent approaches, however, focus on the definition of functional habitats based on tissue features at the voxel scale through the information provided by MRI sequences such as diffusion weighted imaging, perfusion weighted imaging (PWI), or MRS, among others. [15][16][17][18][19][20] These complex studies describing the heterogeneity of GBM at voxel level require the use of automated multiparametric image analysis methods. [21][22][23][24][25] In particular, the study of Juan-Albarracín et al 26 proposed a methodology to describe GBM vascular intrapatient heterogeneity based on a structured unsupervised multiparametric image analysis technology.…”

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

“…Current advances in the analysis of these clinical images include high-throughput quantification methods (radiomics) of a large number of imaging features from MRI, CT and PET/CT images to develop prognostic signatures that predict tumour phenotype, correlate with treatment outcomes [147] and distinguish between different predictive tumour genotypes [148]. These methods can identify tumour subregions, called habitats, with differential imaging characteristics (tissue perfusion and vascular permeability) that correlate with tumour aggressiveness [149]. Other methods include the use of diffusion magnetic resonance imaging (dMRI) to analyse brain fibre orientation and dispersion with parametric spherical deconvolution methods [150,151].…”