The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms

Waugh, Shelley A.; Lerski, R A; Bidaut, Luc; Thompson, Alastair

doi:10.1118/1.3622605

Cited by 74 publications

(60 citation statements)

References 35 publications

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“…Although data was acquired under two different imaging conditions, which may introduce an additional variable, it has been previously reported that robust texture analysis can be performed across different magnets and clinical protocols, providing the spatial resolution remains constant, as it does in this study [23,38,39].…”

Section: Discussionmentioning

confidence: 94%

“…Image data was re-binned to 6 bits/pixel according to previous experience, the literature, and recommendations from the software developers [22,23]. Grey-level normalisation, which is known to minimise the effect of brightness and contrast variations on the outcome of TA [24,25], was carried out within MaZda by rescaling the histogram data to fit within μ± 3σ (μ-grey-level mean, σ-grey-level standard deviation).…”

Section: Texture Analysismentioning

confidence: 99%

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Magnetic resonance imaging texture analysis classification of primary breast cancer

et al. 2015

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• MR-derived entropy features, representing heterogeneity, provide important information on tissue composition. • Entropy features can differentiate between histological and immunohistochemical subtypes of breast cancer. • Differing entropy features between breast cancer subtypes implies differences in lesion heterogeneity. • Texture analysis of breast cancer potentially provides added information for decision making.

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Section: Discussionmentioning

confidence: 94%

Section: Texture Analysismentioning

confidence: 99%

Magnetic resonance imaging texture analysis classification of primary breast cancer

et al. 2015

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“…The influence of the acquisition protocol on textural measures has been controversial in the literature and may depend on the specific parameter, tumors studied, etc [13, 14, 15, 16, 17]. Mayerhoefer et al [13] and Waugh et al [14] studied the influence of different clinical breast MRI protocols and parameters on the results of several textural features.…”

Section: Introductionmentioning

confidence: 99%

“…Mayerhoefer et al [13] and Waugh et al [14] studied the influence of different clinical breast MRI protocols and parameters on the results of several textural features. They showed that spatial resolution is the most important factor influencing textural measures, while changes to other protocol parameters did not change the outcome of texture analyses so significantly.…”

Section: Introductionmentioning

confidence: 99%

Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

et al. 2017

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PurposeTextural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols.Materials and methodsTwenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size.ResultsNo textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases).ConclusionTextural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images.

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“…Although slightly different MRI protocols were used (1.5 T and 3 T), it is well known that changes to MRI sequence parameters are less critical for the outcome of texture analysis than spatial resolution, which may be the most important factor to consider. 17,18 In this work, we selected a set of classical textural features of local (CM) and regional (RLM) nature. Histograms are also classical tools for heterogeneity computation.…”

Section: Correlationsmentioning

confidence: 99%

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

Melodelima¹,

Pérez-Beteta²,

Luque³

et al. 2016

BJR

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Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T 1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient.Results: Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T 1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour. INTRODUCTIONGlioblastoma (GBM) is the most frequent malignant brain tumour in adults and the most lethal type, with a median survival of 14.6 months for patients receiving the standard of care, i.e. maximal safe surgery plus radiotherapy and chemotherapy.1 Pre-operative MRI is routinely used for diagnosis, treatment planning, response evaluation and follow-up.The typical GBM appearance upon diagnosis on MRI consists of an enhanced ring with a central non-enhanced core of necrosis observed mainly on contrast-enhanced T 1 weighted images. Recently, there has been an increased use of advanced imaging techniques alone or in combination with conventional MRI modalities to characterize the connection of the so-called radio phenotype with the tumour genotype, the so-called radiogenomics (e.g. recent reviews for GBM 2,3 ). However, the use of those techniques requires further research and validation to achieve a broad clinical applicability. Thus, in clinical practice and trials, T 1 contrast-enhanced and T 2 /fluid-attenuated inversionrecovery images are still the gold standard for diagnosis and treatment planning. 4 One of the most important characteristics of GBM is its marked intratumour heterogeneity. In fact, these tumours are usually referred to as "glioblastoma multiforme", in order to highlight their heterogeneous nature. GBM tumours are formed by tumour cells which differ in their morphology, genetics and biological behaviour and may underlie the inability of conventional therapies to significantly impact patient outcomes.5 It is well known that ...

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The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms

Cited by 74 publications

References 35 publications

Magnetic resonance imaging texture analysis classification of primary breast cancer

Magnetic resonance imaging texture analysis classification of primary breast cancer

Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

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