Does vascular imaging with MRI predict response to neoadjuvant chemotherapy in primary breast cancer?

Ah-See, Mei-Lin; Μakris, Alexandros; Taylor, N. Jane; Burcombe, R.; Harrison, M.; Stirling, JJ; Richman, P. I.; Leach, MO; Padhani, AR

doi:10.1200/jco.2004.22.90140.582

Cited by 6 publications

(9 citation statements)

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“…DCE‐MRI is able to demonstrate intrinsic differences in the behavior of the distribution of contrast medium that exist between different tissue types, and thus has potential to play multiple roles in cancer management (1–3). A number of studies in various tumor sites (including the breast, rectum, and brain) have shown that changes in tumor microvasculature as demonstrated by DCE‐MRI are useful for predicting and assessing the extent of response to a variety of treatments (4–10). The advantage of DCE‐MRI in predicting response to therapy is that it can quantitatively assess vascular function and does not rely on changes in tumor size to assess the efficacy of treatment.…”

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confidence: 99%

Inter‐ and intraobserver variability in the evaluation of dynamic breast cancer MRI

Beresford

Padhani

Taylor

et al. 2006

Magnetic Resonance Imaging

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Purpose: To quantify variations within and between observers ascribable to manual region of interest (ROI) placement in patients with breast cancer undergoing dynamic MRI. Materials and Methods:Expert and nonexpert observers independently outlined tumor ROIs on 30 dynamic T 1 -weighted (T 1 W) MRI scans on five occasions over two months. Lesion size (number of pixels) and kinetic parameter estimates, including the transfer constant (K trans ), were calculated for each ROI placement. Inter-and intraobserver variability was assessed with respect to the interval between drawings, lesion morphology, and observer experience.Results: For the nonexpert, the variability reduced with decreasing time intervals between ROI drawings (the coefficient of variance (wCV) values at two months, two weeks, one day, and same-day time intervals were respectively 11.6%, 10.7%, 4.8%, and 2.6% for lesion size, and 8.9%, 9.7%, 6.7%, and 3.2% for K trans ). For the expert observer, the variability was smaller overall and more constant, but improved for same-day ROI placements (region size wCV: 7.5%, 6.2%, 7.1%, and 3.7%; K trans wCV: 5.4%, 5.3%, 5.6%, and 4.5%). Conclusion:Significant observer variability in manual ROI placement occurs in dynamic MRI of breast cancer. For serial patient studies, ROI placements should be outlined at the same sitting to minimize observer error.

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confidence: 99%

Inter‐ and intraobserver variability in the evaluation of dynamic breast cancer MRI

Beresford

Padhani

Taylor

et al. 2006

Magnetic Resonance Imaging

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“…One slice from the pre-treatment scan of each patient was used for analysis. Data from this study have been previously reported [13,3].…”

Section: In Vivo Validationmentioning

confidence: 94%

Semi-parametric Analysis of Dynamic Contrast-Enhanced MRI Using Bayesian P-Splines

Schmid¹,

Whitcher

Yang³

2006

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

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Current approaches to quantitative analysis of DCE-MRI with non-linear models involve the convolution of an arterial input function (AIF) with the contrast agent concentration at a voxel or regional level. Full quantification provides meaningful biological parameters but is complicated by the issues related to convergence, (de-)convolution of the AIF, and goodness of fit. To overcome these problems, this paper presents a penalized spline smoothing approach to model the data in a semi-parametric way. With this method, the AIF is convolved with a set of B-splines to produce the design matrix, and modeling of the resulting deconvolved biological parameters is obtained in a way that is similar to the parametric models. Further kinetic parameters are obtained by fitting a non-linear model to the estimated response function and detailed validation of the method, both with simulated and in vivo data is provided.

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“…For the clinical evaluation of our approach we used data of six breast cancer patients who have participated in a breast cancer study which has previously been reported on and which has already been analyzed [44,45,26,10]. Per breast cancer patient, we used two scans recorded by a 1.5 T Siemens MAGNETOM Symphony scanner (Repetition time (TR) = 11 ms, Echo Time (TE) = 4.7 ms).…”

Section: Application To Dce-mri Data 41 Description Of the Datamentioning

confidence: 99%

Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data

Feilke¹,

Bischl²,

Schmid

et al. 2016

Methods Inf Med

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SummaryBackground: For the statistical analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data, compartment models are a commonly used tool. By these models, the observed uptake of contrast agent in some tissue over time is linked to physiologic properties like capillary permeability and blood flow. Up to now, models of different complexity have been used, and it is still unclear which model should be used in which situation. In previous studies, it has been found that for DCE-MRI data, the number of compartments differs for different types of tissue, and that in cancerous tissue, it might actually differ over a region of voxels of one DCE-MR image. Objectives: To find the appropriate number of compartments and estimate the parameters of a regression model for each voxel in an DCE-MR image. With that, tumors in an DCE-MR image can be located, and for example therapy success can be assessed. Methods:The observed uptake of contrast agent in a voxel of an image of some tissue is described by a concentration time curve. This curve can be modeled using a nonlinear regression model. We present a boosting approach with nonlinear regression as base procedure, which allows us to estimate the number of compartments and the related parameters for each voxel of an DCE-MR image. In addition, a spatially regularized version of this approach is proposed. Results: With the proposed approach, the number of compartments -and with that the complexity of the model -per voxel is not fixed but data-driven, which allows us to fit models of adequate complexity to the concentration time curves of all voxels. The parameters of the model remain nevertheless interpretable because of the underlying compartment model. Conclusions:The proposed boosting approaches outperform all competing methods considered in this paper regarding the correct localization of tumors in DCE-MR images as well as the spatial homogeneity of the estimated number of compartments across the image, and the definition of the tumor edge.

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Does vascular imaging with MRI predict response to neoadjuvant chemotherapy in primary breast cancer?

Cited by 6 publications

References 0 publications

Inter‐ and intraobserver variability in the evaluation of dynamic breast cancer MRI

Inter‐ and intraobserver variability in the evaluation of dynamic breast cancer MRI

Semi-parametric Analysis of Dynamic Contrast-Enhanced MRI Using Bayesian P-Splines

Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data

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