Automation of pattern recognition analysis of dynamic contrast‐enhanced MRI data to characterize intratumoral vascular heterogeneity

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The tumor microenvironment is heterogeneous, exhibiting severe functional vascular abnormalities 1. Dynamic contrast‐enhanced MRI (DCE‐MRI) is used to assess tumor blood flow and permeability clinically and preclinically, after the administration of the contrast agent (CA) gadopentetate dimeglumine (Gd‐DTPA), with < 30 min (clinically typically 5  min) scan times and high spatial resolution (<200 µm preclinically and 1–2 mm clinically) 4. Parameters from tracer‐kinetic modeling of signal‐versus‐time DCE‐MRI curves 4 have been used to differentiate tumor microenvironments 5 and to longitudinally monitor vascular changes in response to treatments 6. Various pattern analysis approaches, including machine learning, have been used to extract features to improve tumor classification and, to a lesser extent, assess intratumoral heterogeneity to guide treatment or gauge prognosis 16.
Using preclinical in vivo imaging modalities coregistered with pathology, we have shown previously that well‐vascularized (i.e., well‐perfused) tumor areas are characterized by rapid Gd‐DTPA uptake/washout, that hypoxic areas exhibit reduced vascular function associated with delayed Gd‐DTPA uptake/washout, and that necrotic areas exhibit slow or no CA uptake and no discernible washout over the experimental observation 12. We categorized these tumor microenvironments based on their representative DCE‐MRI signal‐versus‐time curves by pattern recognition (PR), using the Gaussian mixture model or constrained nonnegative matrix factorization (cNMF) 24. The semiautomatic PR approach required manual input of the number of patterns (NP) in the DCE‐MRI data. The variable (subjective) application of a fixed NP for all tumor slices may lead to overfitting (or underfitting) in tumors or tumor slices that are characterized by more (or fewer) patterns than predefined, thus disregarding intratumoral heterogeneity represented by disparate DCE‐MRI curves and physiological environments across tumor slices (Fig. 1).
The goal of this study was to optimize and automate DCE‐MRI data analysis using our previously described unsupervised PR approach 24 to accurately and fully automate the identification of vascularity‐driven intratumoral heterogeneity using cNMF. This model involves novel automatic approaches to determine the NP for each DCE‐MRI slice, to spatially map intratumoral heterogeneities, and to incorporate the computerized determination of the precontrast signal. A stepwise scheme of the analysis process is shown in Figure 2. All analysis steps were coded in MATLAB (MathWorks, Inc. Natick, Massachusetts, USA).

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