Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between<i>BRCA1/2</i>cases, unilateral cancer cases, and controls

Li, Hui; Giger, Maryellen L.; Li, Lan; Janardanan, Jyothi; Sennett, Charlene A.

doi:10.1117/1.jmi.1.3.031009

Cited by 32 publications

(39 citation statements)

References 34 publications

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“…However, Li et al were among the very first to apply texture analysis to discriminate between high-risk BRCA-mutated and low-risk wild-type patients [118]. AI methods were subsequently leveraged to improve the performance of these radiogenomic predictive models-first with Bayesian artificial neural networks, a framework for different machine learning algorithms [119,120], and then with convoluted neural networks (AUC = 0.86) [121].…”

Section: Breast and Ovariesmentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

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From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

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Section: Breast and Ovariesmentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

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“…This set of quantitative features was evaluated because the constituent features have demonstrated utility in previous studies involving clinical classifications based on parenchyma regions in FFDM images. [6][7][8]25…”

Section: B Radiomic Feature Calculationmentioning

confidence: 99%

“…Quantitative measures of parenchymal texture have been successfully applied to evaluate the risk of cancer in asymptomatic females . These studies use radiomic texture features including fractal dimension, power law spectral analysis, absolute gray level, gray‐level histogram analysis, neighborhood gray tone difference matrix (NGTDM), and gray‐level co‐occurrence matrix (GLCM) …”

Section: Introductionmentioning

confidence: 99%

“…Quantitative measures of parenchymal texture have been successfully applied to evaluate the risk of cancer in asymptomatic females. [6][7][8][9][10][11][12][13][14] These studies use radiomic texture features including fractal dimension, 6 power law spectral analysis, 7 absolute gray level, gray-level histogram analysis, neighborhood gray tone difference matrix (NGTDM), and gray-level co-occurrence matrix (GLCM). 15 Risk evaluation stands to be particularly impactful for patient care due to established high-risk screening recommendations that have been enacted by agencies such as the American Cancer Society.…”

Section: Introductionmentioning

confidence: 99%

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Radiomics robustness assessment and classification evaluation: A two‐stage method demonstrated on multivendor FFDM

Robinson

et al. 2019

Medical Physics

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Purpose Radiomic texture analysis is typically performed on images acquired under specific, homogeneous imaging conditions. These controlled conditions may not be representative of the range of imaging conditions implemented clinically. We aim to develop a two‐stage method of radiomic texture analysis that incorporates the reproducibility of individual texture features across imaging conditions to guide the development of texture signatures which are robust across mammography unit vendors. Methods Full‐field digital mammograms were retrospectively collected for women who underwent screening mammography on both a Hologic Lorad Selenia and GE Senographe 2000D system. Radiomic features were calculated on manually placed regions of interest in each image. In stage one (robustness assessment), we identified a set of nonredundant features that were reproducible across the two different vendors. This was achieved through hierarchical clustering and application of robustness metrics. In stage two (classification evaluation), we performed stepwise feature selection and leave‐one‐out quadratic discriminant analysis (QDA) to construct radiomic signatures. We refer to this two‐state method as robustness assessment, classification evaluation (RACE). These radiomic signatures were used to classify the risk of breast cancer through receiver operator characteristic (ROC) analysis, using the area under the ROC curve as a figure of merit in the task of distinguishing between women with and without high‐risk factors present. Generalizability was investigated by comparing the classification performance of a feature set on the images from which they were selected (intravendor) to the classification performance on images from the vendor on which it was not selected (intervendor). Intervendor and intravendor performances were also compared to the performance obtained by implementing ComBat, a feature‐level harmonization method and to the performance by implementing ComBat followed by RACE. Results Generalizability, defined as the difference between intervendor and intravendor classification performance, was shown to monotonically decrease as the number of clusters used in stage one increased (Mann–Kendall P < 0.001). Intravendor performance was not shown to be statistically different from ComBat harmonization while intervendor performance was significantly higher than ComBat. No significant difference was observed between either of the single methods and the use of ComBat followed by RACE. Conclusions A two‐stage method for robust radiomic signature construction is proposed and demonstrated in the task of breast cancer risk assessment. The proposed method was used to assess generalizability of radiomic texture signatures at varying levels of feature robustness criteria. The results suggest that generalizability of feature sets monotonically decreases as reproducibility of features decreases. This trend suggests that considerations of feature robustness in feature selection methodology could improve classifier generalizability in multif...

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“…2,3 An abundance of evidence exists demonstrating a positive correlation between mammographic percent density and breast cancer risk. [4][5][6][7][8][9][10] In addition to density, various studies have indicated a relationship between parenchymal texture patterns and risk of cancer development [11][12][13][14][15][16][17][18][19][20][21] or specific cancer risk factors such as BRCA1/BRCA2 gene mutation. 11,21 Percent density obtained from mammographic images refers to the ratio of the area of dense tissue present in a mammogram to the total area of the breast.…”

Section: Introductionmentioning

confidence: 99%

Breast density estimation from high spectral and spatial resolution MRI

Weiss

Medved

et al. 2016

J. Med. Imag

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Abstract. A three-dimensional breast density estimation method is presented for high spectral and spatial resolution (HiSS) MR imaging. Twenty-two patients were recruited (under an Institutional Review Board-approved Health Insurance Portability and Accountability Act-compliant protocol) for high-risk breast cancer screening. Each patient received standard-of-care clinical digital x-ray mammograms and MR scans, as well as HiSS scans. The algorithm for breast density estimation includes breast mask generating, breast skin removal, and breast percentage density calculation. The inter-and intra-user variabilities of the HiSS-based density estimation were determined using correlation analysis and limits of agreement. Correlation analysis was also performed between the HiSS-based density estimation and radiologists' breast imaging-reporting and data system (BI-RADS) density ratings. A correlation coefficient of 0.91 (p < 0.0001) was obtained between left and right breast density estimations. An interclass correlation coefficient of 0.99 (p < 0.0001) indicated high reliability for the inter-user variability of the HiSS-based breast density estimations. A moderate correlation coefficient of 0.55 (p ¼ 0.0076) was observed between HiSS-based breast density estimations and radiologists' BI-RADS. In summary, an objective density estimation method using HiSS spectral data from breast MRI was developed. The high reproducibility with low inter-and low intra-user variabilities shown in this preliminary study suggest that such a HiSS-based density metric may be potentially beneficial in programs requiring breast density such as in breast cancer risk assessment and monitoring effects of therapy. Keywords: high spatial and spectral resolution MRI; echo planar spectroscopic imaging; SENSE acceleration; breast cancer screening and diagnosis; breast density measurement.Paper 16187R

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Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing betweenBRCA1/2cases, unilateral cancer cases, and controls

Cited by 32 publications

References 34 publications

Radiogenomics: bridging imaging and genomics

Radiogenomics: bridging imaging and genomics

Radiomics robustness assessment and classification evaluation: A two‐stage method demonstrated on multivendor FFDM

Breast density estimation from high spectral and spatial resolution MRI

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