Axial-Shear Strain Imaging for Differentiating Benign and Malignant Breast Masses

Xu, Haiyan; Rao, Min; Varghese, Tomy; Sommer, Amy M.; Baker, Sara; Hall, Timothy J.; Sisney, Gale A.; Burnside, Elizabeth S.

doi:10.1016/j.ultrasmedbio.2010.07.008

Cited by 41 publications

(55 citation statements)

References 54 publications

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“…However, the length scale of this approach does not allow for a reliable distinction of benign and malignant breast masses 36,37 . This challenge is uniquely met by IT-AFM, which is able to differentiate between cancer and benign lesions by correlating stiffness measurements resolved at the nanometre scale across the entire length of a biopsy.…”

Section: A Soft Hypoxic Cell Phenotype Is Conducive To Metastasismentioning

confidence: 99%

The nanomechanical signature of breast cancer

et al. 2012

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Cancer initiation and progression follow complex molecular and structural changes in the extracellular matrix and cellular architecture of living tissue. However, it remains poorly understood how the transformation from health to malignancy alters the mechanical properties of cells within the tumour microenvironment. Here, we show using an indentation-type atomic force microscope (IT-AFM) that unadulterated human breast biopsies display distinct stiffness profiles. Correlative stiffness maps obtained on normal and benign tissues show uniform stiffness profiles that are characterized by a single distinct peak. In contrast, malignant tissues have a broad distribution resulting from tissue heterogeneity, with a prominent low-stiffness peak representative of cancer cells. Similar findings are seen in specific stages of breast cancer in MMTV-PyMT transgenic mice. Further evidence obtained from the lungs of mice with late-stage tumours shows that migration and metastatic spreading is correlated to the low stiffness of hypoxia-associated cancer cells. Overall, nanomechanical profiling by IT-AFM provides quantitative indicators in the clinical diagnostics of breast cancer with translational significance.

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Section: A Soft Hypoxic Cell Phenotype Is Conducive To Metastasismentioning

confidence: 99%

The nanomechanical signature of breast cancer

et al. 2012

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“…The increased stiffness surrounding the tumor may be caused by desmoplastic reaction and is useful to distinguish malignant from benign tumors [19]. In previous studies [16][17][18][19][20][21]24], the radiologists used the size difference between B-mode and strain images in tumor diagnosis such as size ratio. Itoh, et al assigned each elasticity image an elasticity score on a five-point scale to describe strain ratio [33].…”

Section: Strain Featuresmentioning

confidence: 99%

“…A tumor with an elastographic images/B-mode size ratio greater than 1.0 had high likelihood being malignant. The stiffness contrast and normalized shear strain area were also used to diagnose breast tumors [14,19,[21][22][23][24]. The stiffness contrast is the ratio between the mean strains estimated in the background to that estimated within the breast tumor.…”

Section: Introductionmentioning

confidence: 99%

Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography

Chang

Yang

et al. 2015

Computers in Biology and Medicine

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“…Consequently, features from ASSE have been shown to have potential in non-invasive breast lesion classification into benign versus malignant. Specifically, the following two features were reported as promising – The normalized axial–shear strain area (NASSA) present at and outside the inclusion boundary (Thitaikumar et al 2007, 2008, Xu et al 2010, Varghese 2011);Presence or absence of non-zero axial-shear strain values inside the inclusion (referred to as a “ fill-in ”) along with contrasting margin at the boundary (Thittai et al 2010, Galaz et al 2009, Thittai et al 2012). …”

Section: Introductionmentioning

confidence: 99%

“…The normalized axial–shear strain area (NASSA) present at and outside the inclusion boundary (Thitaikumar et al 2007, 2008, Xu et al 2010, Varghese 2011);…”

Section: Introductionmentioning

confidence: 99%

An analysis of the segmentation threshold used in axial–shear strain elastography

Thittai¹,

Xia²

2015

Ultrasonics

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Axial - shear strain elastography was introduced recently to image the tumor-host tissue boundary bonding characteristics. The image depicting the axial-shear strain distribution in a tissue under axial compression was termed as an axial-shear strain elastogram (ASSE). It has been demonstrated through simulation, tissue-mimicking phantom experiments, and retrospective analysis of in vivo breast lesion data that metrics quantifying the pattern of axial-shear strain distribution on ASSE can be used as features for identifying the lesion boundary condition as loosely-bonded or firmly-bonded. Consequently, features from ASSE have been shown to have potential in non-invasive breast lesion classification into benign versus malignant. Although there appears to be a broad concurrence in the results reported by different groups, important details pertaining to the appropriate segmentation threshold needed for – 1) displaying the ASSE as a color-overlay on top of corresponding Axial Strain Elastogram (ASE) and/or sonogram for feature visualization and 2) ASSE feature extraction are not yet fully addressed. In this study, we utilize ASSE from tissue mimicking phantom (with loosely-bonded & firmly-bonded inclusions) experiments and freehand –acquired in vivo breast lesion data (7 benign & 9 malignant) to analyze the effect of segmentation threshold on ASSE feature value, specifically, the “fill-in” feature that was introduced recently. We varied the segmentation threshold from 20% to 70% (of the maximum ASSE value) for each frame of the acquisition cine-loop of every data and computed the number of ASSE pixels within the lesion that was greater than or equal to this threshold value. If at least 40% of the pixels within the lesion area crossed this segmentation threshold, the ASSE frame was considered to demonstrate a “fill-in” that would indicate a loosely-bonded lesion boundary condition (suggestive of a benign lesion). Otherwise, the ASSE frame was considered not to demonstrate a “fill-in” indicating a firmly-bonded lesion boundary condition (suggestive of a malignant lesion). The results demonstrate that in the case of in vivo breast lesion data the appropriate range for the segmentation threshold value seems to be 40% to 60%. It was noted that for a segmentation threshold within this range ( for example, at 50%) all of the analyzed breast lesion cases can be correctly classified into benign and malignant, based on the percentage number of frames within the acquisition cine-loop that demonstrate a “fill-in”.

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Axial-Shear Strain Imaging for Differentiating Benign and Malignant Breast Masses

Cited by 41 publications

References 54 publications

The nanomechanical signature of breast cancer

The nanomechanical signature of breast cancer

Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography

An analysis of the segmentation threshold used in axial–shear strain elastography

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