Changes in Primary Breast Cancer Heterogeneity May Augment Midtreatment MR Imaging Assessment of Response to Neoadjuvant Chemotherapy

Parikh, Jyoti; Selmi, Mariyah; Charles‐Edwards, Geoffrey; Glendenning, Jennifer; Ganeshan, Balaji; Verma, Hema; Mansi, J; Harries, Matthew; Tutt, Andrew; Goh, Vicky

doi:10.1148/radiol.14130569

Cited by 117 publications

(105 citation statements)

References 52 publications

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“…Contrast-enhanced T1-weighted subtraction images reflect the vascular leakage and distribution of contrast agent after injection, whereas on T2-weighted MR images the signal intensity is predominantly derived from the tumor intracellular and extravascular extracellular space. 15 In our study, uniformity and entropy were influenced by various pathological findings on both T2- and contrast-enhanced T1-weighted subtraction images, although results were not always consistent according to various filter levels. Therefore, we conclude that heterogeneity measured by MRI texture analysis correlates with pathological heterogeneity and that these findings can be used as evidence for predicting patient prognosis using texture analysis.…”

Section: Discussionmentioning

confidence: 49%

“…15 Their hypothesis for improved performance of T2-weighted compared with contrast-enhanced T1-weighted images is based on the predominant underlying mechanism for signal intensity. Contrast-enhanced T1-weighted subtraction images reflect the vascular leakage and distribution of contrast agent after injection, whereas on T2-weighted MR images the signal intensity is predominantly derived from the tumor intracellular and extravascular extracellular space.…”

Section: Discussionmentioning

confidence: 99%

“…The effect of filtration, by highlighting larger pixels, has been hypothesized to accentuate the contribution of the vasculature to texture features (Figure 1). 15 …”

Section: Methodsmentioning

confidence: 99%

“…8 In breast imaging, texture analysis using magnetic resonance imaging (MRI) has been used to detect microcalcifications, 9 to differentiate between benign and malignant lesions 10–12 or between invasive ductal and invasive lobular carcinomas, 13 and to predict response to neoadjuvant chemotherapy. 14,15 …”

Section: Introductionmentioning

confidence: 99%

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Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis

Kim

Lim

et al. 2016

Medicine

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Section: Discussionmentioning

confidence: 49%

Section: Discussionmentioning

confidence: 99%

“…The effect of filtration, by highlighting larger pixels, has been hypothesized to accentuate the contribution of the vasculature to texture features (Figure 1). 15 …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis

Kim

Lim

et al. 2016

Medicine

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“…It is continuously being developed as it proved to be a useful tool for assessing prognosis, diagnosing patients with cancer, and differentiating between pathological and healthy tissue [5]. It was applied to identify heterogeneity of tumors across different imaging modalities, including computed tomography (CT) [6], magnetic resonance imaging (MRI) [7], position emission tomography (PET) [8], and more [9].…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis for Identifying Heterogeneity in Medical Images

Nalepa

Szymanek²,

Hayball³

et al. 2014

Computer Vision and Graphics

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Abstract. Heterogeneity is a well-recognized feature of malignancy associated with increased tumor aggression and treatment resistance. Texture analysis (TA) of images of various modalities, including, among others, CT, MRI or PET, can be applied to quantify the tumor heterogeneity and to extract useful information from images acquired in routine clinical practice without additional radiation or expense of further procedures. In this paper, we elaborate on the filtration-based approach to TA applied for extracting features from large sets of simulated images reflecting various clinical circumstances. The areas under receiver operating characteristic curves were used to assess the diagnostic performance of the derived biomarkers. We present and discuss their discriminative abilities in identifying heterogeneity and classifying images with simulated lesions of various characteristics and localized density variations.

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Role of texture analysis in breast MRI as a cancer biomarker: A review

Chitalia

Kontos

2018

Magnetic Resonance Imaging

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Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2019;49:927-938. View this article online at wileyonlinelibrary.com. Applications in Breast Computer-Aided DiagnosisComputer-aided diagnosis (CAD) of breast tissue was one of the earliest applications of texture analysis in the breast. 41 Gibbs and Turnbull 42 were one of the first to apply texture analysis toward classifying breast lesions as benign or malignant. The authors reported using 2D DCE-MR images from a cohort of 79 women, of which 45 were diagnosed with breast cancer. An ROI was selected to encompass the entire lesion, within which gray-level intensity values were discretized to 32 levels. Within each lesion ROI, a co-occurrence matrix was determined for adjoining pixels in 0 , 45 , 90 , and 135 directions. The co-occurrence matrices of each direction were averaged and 14 GLCM texture features were extracted. Texture features of variance, sum entropy, and 928

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Changes in Primary Breast Cancer Heterogeneity May Augment Midtreatment MR Imaging Assessment of Response to Neoadjuvant Chemotherapy

Abstract: Tumors become more homogeneous with treatment. An increase in T2-weighted MR imaging uniformity and a decrease in T2-weighted MR imaging entropy following NACT may provide an earlier indication of pCR than tumor size change.

Cited by 117 publications

References 52 publications

Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis

Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis

Texture Analysis for Identifying Heterogeneity in Medical Images

Role of texture analysis in breast MRI as a cancer biomarker: A review

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