Lesion size ratio for differentiating breast masses

Hall, Timothy J.; Svensson, W. E.; Behren, P. Von; Zhu, Yanning; Malin, Jennifer L.; Spalding, C.S.; Connors, A M; Chopra, Deepti; Lowery, Carol

doi:10.1109/ultsym.2003.1293128

Cited by 10 publications

(10 citation statements)

References 6 publications

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“…Also, when the threshold of 1.2 for area ratio was applied to our own image ratio data, our group sensitivities and specificities were 94% and 29%, respectively. Our best performer (observer A) obtained a sensitivity of 96% and a specificity of 61% by using an area ratio threshold of 1.01, which is closer to the sensitivity and specificity values obtained by Hall et al (22).…”

Section: Discussionsupporting

confidence: 86%

“…Finally, a comparison was made between the results presented by Hall et al (22) and those obtained in the current study by using the same US strain imaging algorithm and analysis approach. A P value of less than .05 was considered to indicate a statistically significant difference for all analyses.…”

Section: Data and Statistical Analysismentioning

confidence: 78%

“…In the previous presentation by Hall et al (22), an optimal area ratio threshold of 1.2, a sensitivity of 100%, and a specificity of 75.4% in characterizing breast lesions were obtained in a series of 169 lesions. This threshold is similar to our group area threshold of 1.13, but our group sensitivity was 96% and specificity was only 24%.…”

Section: Discussionmentioning

confidence: 97%

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Breast Lesions: Evaluation with US Strain Imaging–Clinical Experience of Multiple Observers

Regner¹,

Hesley²,

et al. 2006

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Results suggest that US strain imaging has the potential to aid diagnosis of breast lesions. However, manually tracing lesion boundaries for size ratio differentiation in a busy clinical setting did not match the diagnostic performance levels previously reported. Focusing on measurements of lesion width, along with additional observer training or automated processes, may yield a suitable method for routine clinical application.

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Section: Discussionsupporting

confidence: 86%

Section: Data and Statistical Analysismentioning

confidence: 78%

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Breast Lesions: Evaluation with US Strain Imaging–Clinical Experience of Multiple Observers

Regner¹,

Hesley²,

et al. 2006

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“…This is certainly true of tumors found in the breast and the prostate. Motivated by this observation several clinical trials have been conducted (some are on-going) with the aim of assessing the utility of elasticity imaging in the detection and diagnosis of breast and prostate cancer (Garra et al 1997, Hiltawsky et al 2001, Hall et al 2003, Itoh et al 2006, Regner et al 2006, Burnside et al 2007). Most of these, as indeed most elasticity imaging studies to date, have focused on the linear elastic properties of tissue.…”

Section: Introductionmentioning

confidence: 99%

Linear and nonlinear elasticity imaging of soft tissuein vivo: demonstration of feasibility

et al. 2009

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We establish the feasibility of imaging the linear and nonlinear elastic properties of soft tissue using ultrasound. We report results for breast tissue where it is conjectured that these properties may be used to discern malignant tumors from benign tumors. We consider and compare three different quantities that describe nonlinear behavior, including the variation of strain distribution with overall strain, the variation of the secant modulus with overall applied strain and finally the distribution of the nonlinear parameter in a fully nonlinear hyperelastic model of the breast tissue.

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“…That frame rate provides sufficient feedback to the eye-brain system to control the boundary conditions of the deformation to always obtain high-quality elasticity imaging data with freehand scanning. The preliminary results of clinical trials in breast imaging with this system [5,6], which currently include over 300 subjects, suggest that elasticity imaging provides diagnostically-useful information that is not otherwise available. Specifically, the ratio of the lesion size in a strain image to the lesion size in the corresponding Bmode image is a sensitive criterion for differentiating benign from malignant lesions.…”

Section: Introductionmentioning

confidence: 99%

Motion tracking for palpation imaging

Hall

Jiang

2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)

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We are developing a method for imaging the elastic properties of tissue using unmodified clinical equipment and techniques that are similar to standard clinical exams. Our work with in vivo data from human subjects suggests that elasticity imaging provides new diagnostically significant information. For example, we can observe a nonlinear stressstrain relationship among tissues. Both the accuracy and variance of the displacement estimates must be understood to verify that observation. A significant body of work in algorithm development, computer simulation and phantom experiments precedes this effort. Much of that work addressed the variance in 1-D displacement estimates. The displacement estimate variance for a two-dimensional (2-D) search with a 2-D data kernel can be adequately studied using simulated echo data. The accuracy of displacement estimates when the true displacement is unknown, as with biological tissue experiments, is more difficult to mimic and is studied using data acquired from in vivo breast imaging. Methods to reduce displacement estimate variance and verify displacement estimate accuracy are presented.

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Lesion size ratio for differentiating breast masses

Cited by 10 publications

References 6 publications

Breast Lesions: Evaluation with US Strain Imaging–Clinical Experience of Multiple Observers

Breast Lesions: Evaluation with US Strain Imaging–Clinical Experience of Multiple Observers

Linear and nonlinear elasticity imaging of soft tissuein vivo: demonstration of feasibility

Motion tracking for palpation imaging

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