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Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems.

Fiuzy M, Haddadnia J, Mollania N, Hashemian M, Hassanpour K - Iran J Cancer Prev (2012)

Bottom Line: In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples.Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure.It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Bioengineering, Faculty of Electrical and Computer, Hakim Sabzevari University, Sabzevar, Iran.

ABSTRACT

Background: Accurate Diagnosis of Breast Cancer is of prime importance. Fine Needle Aspiration test or "FNA", which has been used for several years in Europe, is a simple, inexpensive, noninvasive and accurate technique for detecting breast cancer. Expending the suitable features of the Fine Needle Aspiration results is the most important diagnostic problem in early stages of breast cancer. In this study, we introduced a new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration (FNA).

Methods: We studied the Features of Wisconsin Data Base Cancer which contained about 569 FNA test samples (212 patient samples (malignant) and 357 healthy samples (benign)). In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples. Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure. This research proposed a new algorithm for an accurate diagnosis of breast cancer.

Results: According to Wisconsin Data Base Cancer (WDBC) data base, 62.75% of samples were benign, and 37.25% were malignant. After applying the proposed algorithm, we achieved high detection accuracy of about "96.579%" on 205 patients who were diagnosed as having breast cancer. It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively. If done by experts, Fine Needle Aspiration (FNA) can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. FNA can be the first line of diagnosis in women with breast masses, at least in deprived regions, and may increase health standards and clinical supervision of patients.

Conclusion: Such a smart, economical, non-invasive, rapid and accurate system can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer. Another advantage of this method is the possibility of diagnosing breast abnormalities. If done by experts, FNA can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples.

No MeSH data available.


Related in: MedlinePlus

FNA Test and Samples, Figure1 a. Biopsy, Figure 1 b By Ultrasound, Figure1 c. Benign Sample, Figure1 d. Malignant Sample
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f3-IJCP-05-169: FNA Test and Samples, Figure1 a. Biopsy, Figure 1 b By Ultrasound, Figure1 c. Benign Sample, Figure1 d. Malignant Sample

Mentions: The FNA test involves extracting fluid from the breast tissue, and a visual examination of this sample under a microscope [8]. In smart detection, at first, this image turns to a gray level (it does not require color info). Then, the related software defines the cell core boundary based on the image processing techniques, and calculates the features for each core such as radius, texture, perimeter, area, compaction (square perimeter divided by area), flat (mean difference in length of lines radially adjacent), concavity, symmetry and fractal (border kernel of approximation coastline [25- 27]). Finally, it calculates the mean square error and the mean of the three largest achieved values. Respectively, 30 features by factual values are gained for each sample. WDBC database is the data set involving a few FNA tests provided in the above description, which have been performed on 569 patients in Wisconsin hospitals and identified 212 malignant and 357 benign cases [28]. One sample of FNA test is shown in Figure 1.


Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems.

Fiuzy M, Haddadnia J, Mollania N, Hashemian M, Hassanpour K - Iran J Cancer Prev (2012)

FNA Test and Samples, Figure1 a. Biopsy, Figure 1 b By Ultrasound, Figure1 c. Benign Sample, Figure1 d. Malignant Sample
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4209568&req=5

f3-IJCP-05-169: FNA Test and Samples, Figure1 a. Biopsy, Figure 1 b By Ultrasound, Figure1 c. Benign Sample, Figure1 d. Malignant Sample
Mentions: The FNA test involves extracting fluid from the breast tissue, and a visual examination of this sample under a microscope [8]. In smart detection, at first, this image turns to a gray level (it does not require color info). Then, the related software defines the cell core boundary based on the image processing techniques, and calculates the features for each core such as radius, texture, perimeter, area, compaction (square perimeter divided by area), flat (mean difference in length of lines radially adjacent), concavity, symmetry and fractal (border kernel of approximation coastline [25- 27]). Finally, it calculates the mean square error and the mean of the three largest achieved values. Respectively, 30 features by factual values are gained for each sample. WDBC database is the data set involving a few FNA tests provided in the above description, which have been performed on 569 patients in Wisconsin hospitals and identified 212 malignant and 357 benign cases [28]. One sample of FNA test is shown in Figure 1.

Bottom Line: In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples.Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure.It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Bioengineering, Faculty of Electrical and Computer, Hakim Sabzevari University, Sabzevar, Iran.

ABSTRACT

Background: Accurate Diagnosis of Breast Cancer is of prime importance. Fine Needle Aspiration test or "FNA", which has been used for several years in Europe, is a simple, inexpensive, noninvasive and accurate technique for detecting breast cancer. Expending the suitable features of the Fine Needle Aspiration results is the most important diagnostic problem in early stages of breast cancer. In this study, we introduced a new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration (FNA).

Methods: We studied the Features of Wisconsin Data Base Cancer which contained about 569 FNA test samples (212 patient samples (malignant) and 357 healthy samples (benign)). In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples. Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure. This research proposed a new algorithm for an accurate diagnosis of breast cancer.

Results: According to Wisconsin Data Base Cancer (WDBC) data base, 62.75% of samples were benign, and 37.25% were malignant. After applying the proposed algorithm, we achieved high detection accuracy of about "96.579%" on 205 patients who were diagnosed as having breast cancer. It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively. If done by experts, Fine Needle Aspiration (FNA) can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. FNA can be the first line of diagnosis in women with breast masses, at least in deprived regions, and may increase health standards and clinical supervision of patients.

Conclusion: Such a smart, economical, non-invasive, rapid and accurate system can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer. Another advantage of this method is the possibility of diagnosing breast abnormalities. If done by experts, FNA can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples.

No MeSH data available.


Related in: MedlinePlus