Literature DB >> 21958601

A margin sharpness measurement for the diagnosis of breast cancer from magnetic resonance imaging examinations.

Jacob E D Levman1, Anne L Martel.   

Abstract

RATIONALE AND
OBJECTIVES: Cancer screening by magnetic resonance imaging (MRI) has been shown to be one of the most sensitive methods available for the early detection of breast cancer. There is high variability in the diagnostic accuracy of radiologists analyzing the large amounts of data acquired in a breast MRI examination, and this has motivated substantial research toward the development of computer-aided detection and diagnosis systems. Most computer-aided diagnosis systems for breast MRI focus on dynamic information (how a lesion's brightness changes over the course of an examination after the injection of a contrast agent). The inclusion of lesion margin measurements is much less common. One characteristic of malignant tumors is that they grow into neighboring tissues. This growth creates tumor margins that are variably fuzzy or diffuse (ie, they are not sharp).
MATERIALS AND METHODS: In this short report, the authors present a new method for measuring a tumor's margin from breast MRI examinations and compare it with an existing mathematical technique for margin measurements.
RESULTS: The proposed method can yield a test with sensitivity of 77% (specificity, 65%) on screening data, outperforming existing mathematical lesion margin measurement methods. Furthermore, when the presented margin measurement is combined with existing dynamic features, there is a statistically significant improvement in computer-aided diagnosis test performance (P < .0014).
CONCLUSIONS: The proposed method for measuring a tumor's margin outperforms existing mathematical methods on an extremely challenging data set containing many small lesions. The technique presented may be useful in discriminating between malignant and benign lesions in the context of the computer-aided diagnosis of breast cancer from MRI. Copyright Â
© 2011 AUR. Published by Elsevier Inc. All rights reserved.

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Mesh:

Year:  2011        PMID: 21958601     DOI: 10.1016/j.acra.2011.08.004

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  9 in total

1.  A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.

Authors:  Jacob E D Levman; Ellen Warner; Petrina Causer; Anne L Martel
Journal:  J Digit Imaging       Date:  2014-02       Impact factor: 4.056

2.  Semi-automatic region-of-interest segmentation based computer-aided diagnosis of mass lesions from dynamic contrast-enhanced magnetic resonance imaging based breast cancer screening.

Authors:  Jacob Levman; Ellen Warner; Petrina Causer; Anne Martel
Journal:  J Digit Imaging       Date:  2014-10       Impact factor: 4.056

Review 3.  Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture.

Authors:  José Raniery Ferreira; Marcelo Costa Oliveira; Paulo Mazzoncini de Azevedo-Marques
Journal:  J Digit Imaging       Date:  2018-08       Impact factor: 4.056

4.  CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

Authors:  José Raniery Ferreira-Junior; Marcel Koenigkam-Santos; Ariane Priscilla Magalhães Tenório; Matheus Calil Faleiros; Federico Enrique Garcia Cipriano; Alexandre Todorovic Fabro; Janne Näppi; Hiroyuki Yoshida; Paulo Mazzoncini de Azevedo-Marques
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-11-13       Impact factor: 2.924

5.  Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

Authors:  José Raniery Ferreira; Paulo Mazzoncini de Azevedo-Marques; Marcelo Costa Oliveira
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-08-23       Impact factor: 2.924

6.  Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval.

Authors:  Jiajing Xu; Sandy Napel; Hayit Greenspan; Christopher F Beaulieu; Neeraj Agrawal; Daniel Rubin
Journal:  Med Phys       Date:  2012-09       Impact factor: 4.071

7.  Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study.

Authors:  Luyu Huang; Weihuan Lin; Daipeng Xie; Yunfang Yu; Hanbo Cao; Guoqing Liao; Shaowei Wu; Lintong Yao; Zhaoyu Wang; Mei Wang; Siyun Wang; Guangyi Wang; Dongkun Zhang; Su Yao; Zifan He; William Chi-Shing Cho; Duo Chen; Zhengjie Zhang; Wanshan Li; Guibin Qiao; Lawrence Wing-Chi Chan; Haiyu Zhou
Journal:  Eur Radiol       Date:  2021-10-16       Impact factor: 7.034

8.  Automatic weighing attribute to retrieve similar lung cancer nodules.

Authors:  David Jones Ferreira de Lucena; José Raniery Ferreira Junior; Aydano Pamponet Machado; Marcelo Costa Oliveira
Journal:  BMC Med Inform Decis Mak       Date:  2016-07-21       Impact factor: 2.796

9.  Using quantitative features extracted from T2-weighted MRI to improve breast MRI computer-aided diagnosis (CAD).

Authors:  Cristina Gallego-Ortiz; Anne L Martel
Journal:  PLoS One       Date:  2017-11-07       Impact factor: 3.240

  9 in total

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