Regional Admittivity Spectra With Tomosynthesis Images for Breast Cancer Detection: Preliminary Patient Study

Kao, Tzu-Jen; Boverman, Gregory; Kim, Bong Seok; Isaacson, David; Saulnier, G.J.; Newell, J.C.; Choi, Myoung Hwan; Moore, Richard H.; Kopans, Daniel B.

doi:10.1109/tmi.2008.926049

Cited by 27 publications

(9 citation statements)

References 25 publications

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“…One research group has investigated the combination of DBT with electrical impedance tomography for characterization of suspicious lesions, showing promising results with a small number of patient cases. [142][143][144] Combining DBT with ultrasound has also been investigated, with the development of a prototype system that acquires the tomosynthesis image and immediately afterwards positions and scans an ultrasound probe across the compressed breast, obtaining a co-registered 3D ultrasound image. 145,146 The combination of morphological information with functional information is also being developed by a group that is investigating combining DBT with limited angle Tc-99m sestamibi-based single photon emission computed tomography (SPECT).…”

Section: Ivd Multimodality Imagingmentioning

confidence: 99%

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

2013

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Many important post-acquisition aspects of breast tomosynthesis imaging can impact its clinical performance. Chief among them is the reconstruction algorithm that generates the representation of the three-dimensional breast volume from the acquired projections. But even after reconstruction, additional processes, such as artifact reduction algorithms, computer aided detection and diagnosis, among others, can also impact the performance of breast tomosynthesis in the clinical realm. In this two part paper, a review of breast tomosynthesis research is performed, with an emphasis on its medical physics aspects. In the companion paper, the first part of this review, the research performed relevant to the image acquisition process is examined. This second part will review the research on the post-acquisition aspects, including reconstruction, image processing, and analysis, as well as the advanced applications being investigated for breast tomosynthesis.

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Section: Ivd Multimodality Imagingmentioning

confidence: 99%

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

2013

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“…Twelve articles described studies into breast cancer on a total of 2460 patients. Six of the 12 studies looked at in vivo tissue by measuring the electrical impedance and electrical properties of the overlying skin or lesion itself intra-operatively [12][13][14][15][16][17], whilst the other six studies directly measured impedance of fresh ex vivo specimens [18][19][20][21][22][23]. Three studies looked at intra-ductal carcinoma (IDC) alone [12,13,20], whilst six studies looked at various different carcinoma subtypes [14-17, 21, 22], and the remaining three studies did not report the exact histopathology of the cancer specimens tested [14,19,21].…”

Section: Breast Cancermentioning

confidence: 99%

“…One study was able to integrate the various parameters (R o /R ∝ and f c ) to create a new parameter that was able to distinguish malignant tissue with an AUROC of 0.93 [24]. Four of the breast studies used EIS technology either integrated or in combination with mammographic technology in order to produce images as well as quantitative measurements of the tissue [13,[15][16][17]. One study was able to show that impedance was affected by age and hormonal state of the patient and was most sensitive at identifying cancers in patients under 40 without any palpable lesions [22].…”

Section: Breast Cancermentioning

confidence: 99%

The clinical application of electrical impedance technology in the detection of malignant neoplasms: a systematic review

et al. 2020

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Background Electrical impedance technology has been well established for the last 20 years. Recently research has begun to emerge into its potential uses in the detection and diagnosis of pre-malignant and malignant conditions. The aim of this study was to systematically review the clinical application of electrical impedance technology in the detection of malignant neoplasms. Methods A search of Embase Classic, Embase and Medline databases was conducted from 1980 to 22/02/2018 to identify studies reporting on the use of bioimpedance technology in the detection of pre-malignant and malignant conditions. The ability to distinguish between tissue types was defined as the primary endpoint, and other points of interest were also reported. Results 731 articles were identified, of which 51 reported sufficient data for analysis. These studies covered 16 different cancer subtypes in a total of 7035 patients. As the studies took various formats, a qualitative analysis of each cancer subtype’s data was undertaken. All the studies were able to show differences in electrical impedance and/or related metrics between malignant and normal tissue. Conclusions Electrical impedance technology provides a novel method for the detection of malignant tissue, with large studies of cervical, prostate, skin and breast cancers showing encouraging results. Whilst these studies provide promising insights into the potential of this technology as an adjunct in screening, diagnosis and intra-operative margin assessment, customised development as well as multi-centre clinical trials need to be conducted before it can be reliably employed in the clinical detection of malignant tissue.

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“…The similarities of the tumor's symptoms are almost same as the inflammation of the skin problem. Breast pain, swelling, and reddish skin are very common symptoms of cancer but people ignore it as they take it as normal skin inflammatory problem [9][10][11]. Fig.…”

Section: B Problem Statementmentioning

confidence: 99%

“…(x-a)(x-b) = x 2 -(a+b)x + ab (10) Optimization issue of the weight values can be resolved by using the following equations foe SVMs:…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

Talha¹,

Kushsairy²,

Shahzad³

et al. 2020

IJACSA

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Breast cancer and heart disease can be acknowledged as very dangerous and common disease in many countries including Pakistan. In this paper classifiers comparative study has been performed for the tumor and heart disease classification. Around one lac women are diagnosed annually with this life-threatening disease having no family history of the disease. If it is not treated on time it may grow and spread to the other parts of human body. Mammograms are the X-rays of the breast which can be used for the screening of cancer tumor. Prior identification of breast cancer may increase the chance of survival up to 70 percent. Tumors which causes cancer can be categorized into two types: a) Benign and b) Malignant. Benign tumor can be explained as the tumor which are not attached to neighbor tissues or spread in the other parts of the body. In Malignant tumor, other parts may be affected by it as it can grow and spread in the other parts of the body. To classify the tumor as Malignant or Benign is very complex as the similarities of cancer tumor and tumor caused by the skin inflammation are almost same. The early identification of Malignant is mandatory to protect the patient life. Diversified medical methods based on deep learning and machine learning have been developed to treat the patients as cancer is a very serious and crucial issue in this era. In this research paper machine learning algorithms like logistic regression, K-NN and tree have been applied to the breast cancer data set which has been taken from UCI Machine learning repository. Comparative study of classifiers has been performed to determine the better classifier for the robust prediction of breast tumors. Simulated results proved that using Logistic regression, ninety-one percent accuracy was achieved. The research showed that logistic regression can be applied for the accurate and precise early prediction of breast cancer. Cardiovascular disease is very common throughout the world. It has been noticed that health in cardiac patients that there are so many factors which causes heart disease or heart attack. The factors leading to the heart failure includes varying blood pressure, high sugar, cardiac pain, and heart rate, high cholesterol level (LDL), artery blockage and irregular ECG signals. Many researchers proved that stress in patients can also be the reason for the heart disease. Higher numbers of cardiac surgeries like angioplasty and heart bypass are performed on annual basis. Actually, people don't care about their lifestyle and diet and fully ignore the symbols. It can be early predicted and cured if proper testing and medication for heart is done. Sometimes there is a false pain which has the same feeling like angina pain depicting cardiovascular disease. To reduce the false alarm and robustly classify the heart disease, several machine learning approaches have been adopted. In proposed research for the accurate classification of heart disease comparison has been performed among support vector machine (SVM), K-nearest neighbors K-NN and lin...

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Regional Admittivity Spectra With Tomosynthesis Images for Breast Cancer Detection: Preliminary Patient Study

Cited by 27 publications

References 25 publications

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

The clinical application of electrical impedance technology in the detection of malignant neoplasms: a systematic review

Proficiency Assessment of Machine Learning Classifiers: An Implementation for the Prognosis of Breast Tumor and Heart Disease Classification

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