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Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results.

ABSTRACT: Tumor hypoxia develops heterogeneously, affects radiation sensitivity and the development of metastases. Prognostic information derived from the in vivo characterization of the spatial distribution of hypoxic areas in solid tumors can be of value for radiation therapy planning and for monitoring the early treatment response. Tumor hypoxia is caused by an imbalance between the supply and consumption of oxygen. The tumor oxygen supply is inherently linked to its vasculature and perfusion which can be evaluated by dynamic contrast enhanced (DCE-) MRI using the contrast agent Gd-DTPA. Thus, we hypothesize that DCE-MRI data may provide surrogate information regarding tumor hypoxia. In this study, DCE-MRI data from a rat prostate tumor model were analysed with a Gaussian mixture model (GMM)-based classification to identify perfused, hypoxic and necrotic areas for a total of ten tumor slices from six rats, of which one slice was used as training data for GMM classifications. The results of pattern recognition analyzes were validated by comparison to corresponding Akep maps defining the perfused area (0.84 ± 0.09 overlap), hematoxylin and eosin (H&E)-stained tissue sections defining necrosis (0.64 ± 0.15 overlap) and pimonidazole-stained sections defining hypoxia (0.72 ± 0.17 overlap), respectively. Our preliminary data indicate the feasibility of a GMM-based classification to identify tumor hypoxia, necrosis and perfusion/permeability from non-invasively acquired, in vivo DCE-MRI data alone, possibly obviating the need for invasive procedures, such as biopsies, or exposure to radioactivity, such as positron emission tomography (PET) exams.

SUBMITTER: Han SH

PROVIDER: S-EPMC3706205 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results.

Han S H SH Ackerstaff E E Stoyanova R R Carlin S S Huang W W Koutcher J A JA Kim J K JK Cho G G Jang G G Cho H H

NMR in biomedicine 20130225 5

Tumor hypoxia develops heterogeneously, affects radiation sensitivity and the development of metastases. Prognostic information derived from the in vivo characterization of the spatial distribution of hypoxic areas in solid tumors can be of value for radiation therapy planning and for monitoring the early treatment response. Tumor hypoxia is caused by an imbalance between the supply and consumption of oxygen. The tumor oxygen supply is inherently linked to its vasculature and perfusion which can ...[more]

PMID: 23440683

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Project description:The aim of the study was to investigate dynamic contrast enhanced MRI (DCE-MRI) as a potential marker to assess the therapeutic responses of fecal microbiota transplantation (FMT) in patients with Crohn's disease (CD) and to determine the parameter or combination of parameters most strongly associated with changes in clinical indicators after treatment.In 22 CD patients, DCE-MRI was performed with a 3.0T scanner. Parameters of DCE-MRI (vascular transfer constant [K] and blood volume [BV]) in the terminal ileum were compared between before and day 90 after FMT treatment. The differences of clinical indicators (C-reactive protein [CRP], Harvey-Bradshaw index [HBI]) and DCE-MRI parameters (K, BV) between pre- and post-treatment was calculated by Student's 2-tailed, paired t-test. The correlations between percent change of clinical indicators (ΔCRP, ΔHBI) with DCE-MRI parameters (ΔK, ΔBV) were analyzed by Pearson's correlation coefficients. A logistic regression model was used to identify the changes of DCE-MRI parameters related to the treatment outcomes. Receiver operating characteristic curves (ROCs) were generated to assess which DCE-MRI parameter showed the best accuracy for evaluation of therapeutic response.After treatment, mean values of clinical indicators decreased significantly (CRP: 62.68 ± 31.86 vs 43.55 ± 29.63 mg/L, P = .008; HBI: 7.18 ± 2.10 vs 5.73 ± 2.33, P = 0.012). Both DCE-MRI parameters showed prominent differences before and after treatment: K (1.86 ± 0.87 vs 1.39 ± 0.83 min, P = .017), BV (61.02 ± 28.49 vs 41.96 ± 22.75 mL/100 g, P = .005). There were significant correlations between ΔCRP or ΔHBI and percent change of CDE-MRI parameters (ΔK to ΔCRP: 0.659; ΔK to ΔHBI: 0.496; ΔBV to ΔCRP: 0.442; ΔBV to ΔHBI: 0.476). Compared to ΔK and ΔBV individually, the combination of both parameters performed best in assessment of therapeutic response with an area under the ROCs (AUC) of 0.948.K and BV parameters derived from DCE-MRI have the potential to assess for therapeutic response after FMT treatment for CD. The combination of K and BV measurements improved the predictive capability compared to the individual parameters.

Project description:Background and purposeNo report has been published on the use of DSC MR imaging, DCE MR imaging, and DWI parameters in combination to create a prognostic prediction model in glioblastoma patients. The aim of this study was to develop a machine learning-based model to find preoperative multiparametric MR imaging parameters associated with prognosis in patients with glioblastoma. Normalized CBV, volume transfer constant, and ADC of the nonenhancing T2 high-signal-intensity lesions were evaluated using K-means clustering.Materials and methodsA total of 142 patients with glioblastoma who underwent preoperative MR imaging and total resection were included in this retrospective study. From the normalized CBV, volume transfer constant, and ADC maps, the parametric data were sorted using the K-means clustering method. Patients were divided into training and test sets (ratio, 1:1), and the optimal number of clusters was determined using receiver operating characteristic analysis. Kaplan-Meier survival analysis and log-rank tests were performed to identify potential parametric predictors. A multivariate Cox proportional hazard model was conducted to adjust for clinical predictors.ResultsThe nonenhancing T2 high-signal-intensity lesions were divided into 6 clusters. The cluster (class 4) with the relatively low normalized CBV and volume transfer constant value and the lowest ADC values was most associated with predicting glioblastoma prognosis. The optimal cutoff of the class 4 volume fraction of nonenhancing T2 high-signal-intensity lesions predicting 1-year progression-free survival was 9.70%, below which the cutoff was associated with longer progression-free survival. Two Kaplan-Meier curves based on the cutoff value showed a statistically significant difference (P = .037). When we adjusted for all clinical predictors, the cluster with the relatively low normalized CBV and volume transfer constant values and the lowest ADC value was an independent prognostic marker (hazard ratio, 3.04; P = .048). The multivariate Cox proportional hazard model showed a concordance index of 0.699 for progression-free survival.ConclusionsOur model showed that nonenhancing T2 high-signal-intensity lesions with the relatively low normalized CBV, low volume transfer constant values, and the lowest ADC values could serve as useful prognostic imaging markers for predicting survival outcomes in patients with glioblastoma.

Project description:ObjectivesTo determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" breast cancers.Materials and methodsIn this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.ResultsImaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.ConclusionsConsidering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.

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Dataset Information

Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results.

Publications

Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results.

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