Classification of breast tissue by electrical impedance spectroscopy

Silva, Jorge Estrela da; Sá, Joaquim P. Marques de; Jossinet, J.

doi:10.1007/bf02344684

Cited by 155 publications

(72 citation statements)

References 17 publications

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“…The dataset is obtained from electrical impedance measurements of freshly excised tissue samples from the breast [23]. The first tree groups with 54 samples in total refer to breast cancer and the remaining 52 samples in three groups refer to normal breast tissue cases.…”

Section: Datasets and Information On Selected Featuresmentioning

confidence: 99%

Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm

Özçift

2011

J Med Syst

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Accurate classifiers are vital to design precise computer aided diagnosis (CADx) systems. Classification performances of machine learning algorithms are sensitive to the characteristics of data. In this aspect, determining the relevant and discriminative features is a key step to improve performance of CADx. There are various feature extraction methods in the literature. However, there is no universal variable selection algorithm that performs well in every data analysis scheme. Random Forests (RF), an ensemble of trees, is used in classification studies successfully. The success of RF algorithm makes it eligible to be used as kernel of a wrapper feature subset evaluator. We used best first search RF wrapper algorithm to select optimal features of four medical datasets: colon cancer, leukemia cancer, breast cancer and lung cancer. We compared accuracies of 15 widely used classifiers trained with all features versus to extracted features of each dataset. The experimental results demonstrated the efficiency of proposed feature extraction strategy with the increase in most of the classification accuracies of the algorithms.

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Section: Datasets and Information On Selected Featuresmentioning

confidence: 99%

Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm

Özçift

2011

J Med Syst

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“…Our results were compared mostly with those found in [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. We did not aim to make an exhaustive literature review, but to sample referenced works and reach comparable results to demonstrate the utility of the MLC exploration method and its underlying tool (BiomedTK).…”

Section: A Methods To Build Ensemble Classifiersmentioning

confidence: 93%

A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources

2011

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This paper describes the BiomedTK software framework, created to perform massive explorations of machine learning classifiers configurations for biomedical data analysis over distributed Grid computing resources. BiomedTK integrates ROC analysis throughout the complete classifier construction process and enables explorations of large parameter sweeps for training third party classifiers such as artificial neural networks and support vector machines, offering the capability to harness the vast amount of computing power serviced by Grid infrastructures. In addition, it includes classifiers modified by the authors for ROC optimization and functionality to build ensemble classifiers and manipulate datasets (import/export, extract and transform data, etc.). BiomedTK was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature. The comprehensive method herewith presented represents an improvement to biomedical data analysis in both methodology and potential reach of machine learning based experimentation.

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“…Breast cancer is diagnosed by changes in tissue stiffness detected by mammography. Microenvironmental electrical alteration between healthy and tumorous tissue is used in diagnosis procedures such as electrical impedance spectroscopy (EIS) [42, 43]. Clinical impedance measurements have detected an altered electrical environment in lung cancer patients, which demonstrated cancer patients to have a reduced electrical reactance [44].…”

Section: Physical and Chemical Alterations In Cancermentioning

confidence: 99%

Improving cancer therapies by targeting the physical and chemical hallmarks of the tumor microenvironment

et al. 2016

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Tumors are highly heterogeneous at the patient, tissue, cellular, and molecular levels. This multi-scale heterogeneity poses significant challenges for effective therapies, which ideally must not only distinguish between tumorous and healthy tissue, but also fully address the wide variety of tumorous sub-clones. Commonly used therapies either leverage a biological phenotype of cancer cells (e.g. high rate of proliferation) or indiscriminately kill all the cells present in a targeted volume. Tumor microenvironment (TME) targeting represents a promising therapeutic direction, because a number of TME hallmarks are conserved across different tumor types, despite the underlying genetic heterogeneity. Historically, TME targeting has largely focused on the cells that support tumor growth (e.g. vascular endothelial cells). However, by viewing the intrinsic physical and chemical alterations in the TME as additional therapeutic opportunities rather than barriers, a new class of TME-inspired treatments has great promise to complement or replace existing therapeutic strategies. In this review we summarize the physical and chemical hallmarks of the TME, and discuss how these tumor characteristics either currently are, or may ultimately be targeted to improve cancer therapies.

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Classification of breast tissue by electrical impedance spectroscopy

Cited by 155 publications

References 17 publications

Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm

Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm

A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources

Improving cancer therapies by targeting the physical and chemical hallmarks of the tumor microenvironment

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