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

Han, So Hyun; Stoyanova, Radka; Lee, Hansol; Carlin, Sean D.; Koutcher, Jason A.; Cho, Hyung Joon; Ackerstaff, Ellen

doi:10.1002/mrm.26822

Cited by 7 publications

(8 citation statements)

References 37 publications

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“…To automatically segment the tissue region of intact BBB, cNMF was applied to Δ R 2 *(t) curves generated from DSC‐MRI of rat stroke models. Detailed cNMF algorithms were described in a previous study, and were largely followed here, except for signal normalization. Briefly, to find nonnegative matrices S and W, representing the basic temporal curves and corresponding weights, respectively, that satisfy modeling of the data matrix V ≈ WS , multiplicative update algorithms were used.…”

Section: Methodsmentioning

confidence: 99%

“…Nonnegative matrix factorization (NMF), which is an unsupervised pattern recognition (PR) algorithm, is fast and can efficiently classify dynamic signals without user intervention and training data . Constrained NMF (cNMF) analysis can iteratively search nonnegative matrices of the basic temporal patterns and corresponding weights, which satisfies the data matrix based on multiplicative update algorithms with certain constraints .…”

mentioning

confidence: 99%

“…Constrained NMF (cNMF) analysis can iteratively search nonnegative matrices of the basic temporal patterns and corresponding weights, which satisfies the data matrix based on multiplicative update algorithms with certain constraints . Previous constrained NMF‐based classification of heterogeneous tumor perfusion from DCE‐MRI signals was reportedly effective . Thus, PR analysis may enable segmentation of BBB‐damaged areas, large blood vessels, ventricles, and normal regions in the stroke brain from DSC‐MRI signal–time‐curves alone, without additional information.…”

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confidence: 99%

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Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study

Jin

Han

Stoyanova

et al. 2019

Magnetic Resonance Imaging

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Background: The manual segmentation of intact blood-brain barrier (BBB) regions in the stroke brain is cumbersome, due to the coexistence of infarction, large blood vessels, ventricles, and intact BBB regions, specifically in areas with weak signal enhancement following contrast agent injection. Hypothesis: That from dynamic susceptibility contrast (DSC)-MRI alone, without user intervention, regions of weak BBB damage can be segmented based on the leakage-related parameter K 2 and the extent of intact BBB regions, needed to estimate K 2 values, determined. Study Type: Feasibility. Animal Model: Ten female Sprague-Dawley rats (SD, 200-250g) underwent 1-hour middle carotid artery occlusion (MCAO) and 1-day reperfusion. Two SD rats underwent 1-hour MCAO with 3-day and 5-day reperfusion. Field Strength/Sequence: 7T; ADC and T 1 maps using diffusion-weighted echo planar imaging (EPI) and relaxation enhancement (RARE) with variable repetition time (TR), respectively. dynamic contrast-enhanced (DCE)-MRI using FLASH. DSC-MRI using gradient-echo EPI. Assessment: Constrained nonnegative matrix factorization (cNMF) was applied to the dynamic ΔR * 2 -curves of DSC-MRI (<4 min) in a BBB-disrupted rat model. Areas of voxels with intact BBB, classified by automated cNMF analyses, were then used in estimating K 1 and K 2 values, and compared with corresponding values from manually-derived areas. Statistical Tests: Mean AE standard deviation of ΔT 1 -differences between ischemic and healthy areas were displayed with unpaired Student's t-tests. Scatterplots were displayed with slopes and intercepts and Pearson's r values were evaluated between K 2 maps obtained with automatic (cNMF)-and manually-derived regions of interest (ROIs) of the intact BBB region. Results: Mildly BBB-damaged areas (indistinguishable from DCE-MRI (10 min) parameters) were automatically segmented. Areas of voxels with intact BBB, classified by automated cNMF, matched closely the corresponding, manually-derived areas when respective areas were used in estimating K 2 maps (Pearson's r = 0.97, 12 slices). Data Conclusion: Automatic segmentation of short DSC-MRI data alone successfully identified areas with intact and compromised BBB in the stroke brain and compared favorably with manual segmentation.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study

Jin

Han

Stoyanova

et al. 2019

Magnetic Resonance Imaging

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“…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.…”

Section: Habitat Imagingmentioning

confidence: 99%

“…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. [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. [147][148][149][150] In fact, intratumoral spatial heterogeneity has been associated with resistance to therapy, 151 tumor growth, 152 and poor prognosis, 153 and therefore has direct clinical consequences.…”

Section: Habitat Imagingmentioning

confidence: 99%

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

Napel

Jardim‐Perassi

et al. 2018

Cancer

148

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Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

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Distinguishing metastatic triple‐negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

Bendau

Smith

Zhang

et al. 2020

Journal of Biophotonics

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Triple‐negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African‐American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time‐consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label‐free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole‐tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.

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

Abstract: The PR-based DCE-MRI analysis was successfully automated to spatially map intratumoral vascular heterogeneity. Magn Reson Med 79:1736-1744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Cited by 7 publications

References 37 publications

Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study

Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study

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

Distinguishing metastatic triple‐negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

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