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Vascular profile characterization of liver tumors by magnetic resonance imaging using hemodynamic response imaging in mice.

ABSTRACT: Recently, we have demonstrated the feasibility of using hemodynamic response imaging (HRI), a functional magnetic resonance imaging (MRI) method combined with hypercapnia and hyperoxia, for monitoring vascular changes during liver pathologies without the need of contrast material. In this study, we evaluated HRI ability to assess changes in liver tumor vasculature during tumor establishment, progression, and antiangiogenic therapy. Colorectal adenocarcinoma cells were injected intrasplenically to model colorectal liver metastasis (CRLM) and the Mdr2 knockout mice were used to model primary hepatic tumors. Hepatic perfusion parameters were evaluated using the HRI protocol and were compared with contrast-enhanced (CE) MRI. The hypovascularity and the increased arterial blood supply in well-defined CRLM were demonstrated by HRI. In CRLM-bearing mice, the entire liver perfusion was attenuated as the HRI maps were significantly reduced by 35%. This study demonstrates that the HRI method showed enhanced sensitivity for small CRLM (1-2 mm) detection compared with CE-MRI (82% versus 38%, respectively). In addition, HRI could demonstrate the vasculature alteration during CRLM progression (arborized vessels), which was further confirmed by histology. Moreover, HRI revealed the vascular changes induced by rapamycin treatment. Finally, HRI facilitates primary hepatic tumor characterization with good correlation to the pathologic differentiation. The HRI method is highly sensitive to subtle hemodynamic changes induced by CRLM and, hence, can function as an imaging tool for understanding the hemodynamic changes occurring during CRLM establishment, progression, and antiangiogenic treatment. In addition, this method facilitated the differentiation between different types of hepatic lesions based on their vascular profile noninvasively.

SUBMITTER: Edrei Y

PROVIDER: S-EPMC3050867 | biostudies-other | 2011 Mar

REPOSITORIES: biostudies-other

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Vascular profile characterization of liver tumors by magnetic resonance imaging using hemodynamic response imaging in mice.

Edrei Yifat Y Gross Eitan E Corchia Nathalie N Tsarfaty Galia G Galun Eithan E Pappo Orit O Abramovitch Rinat R

Neoplasia (New York, N.Y.) 20110301 3

Recently, we have demonstrated the feasibility of using hemodynamic response imaging (HRI), a functional magnetic resonance imaging (MRI) method combined with hypercapnia and hyperoxia, for monitoring vascular changes during liver pathologies without the need of contrast material. In this study, we evaluated HRI ability to assess changes in liver tumor vasculature during tumor establishment, progression, and antiangiogenic therapy. Colorectal adenocarcinoma cells were injected intrasplenically t ...[more]

PMID: 21390187

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Project description:The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.

Project description:ObjectiveRadiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations.Methods105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time.ResultsThe average DSC of the liver in the validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively.ConclusionUNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.

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Vascular profile characterization of liver tumors by magnetic resonance imaging using hemodynamic response imaging in mice.

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Vascular profile characterization of liver tumors by magnetic resonance imaging using hemodynamic response imaging in mice.

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