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

Mohammad Fiuzy; Javad Haddadnia; Nasrin Mollania; Maryam Hashemian; Kazem Hassanpour

International Journal of Cancer Management

The Official Journal of Cancer Research Center (CRC), Shahid Beheshti University of Medical Sciences

Home

Instructions

APC

Authors Guide Submit Manuscript

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

Author(s):

Mohammad Fiuzy¹,

Javad Haddadnia^1,*,

Nasrin Mollania²,

Maryam Hashemian³,

Kazem Hassanpour³

1Dept. of Bioengineering, Faculty of Electrical and Computer, Hakim Sabzevari University, Sabzevar, Iran

2Dept. of Biology, Faculty of Basic Sciences, Hakim Sabzevari University, Sabzevar, Iran

3Dept. of Clinical Sciences, Sabzevar University of Medical Sciences, Sabzevar, Iran

International Journal of Cancer Management:Vol. 5, issue 4; e80827

Published online:Dec 31, 2012

Article type:Research Article

Received:May 14, 2012

Accepted:Aug 22, 2012

How to Cite:Fiuzy M, Haddadnia J, Mollania N, Hashemian M, Hassanpour K. Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems. Int J Cancer Manag. 2012;5(4):e80827. doi:

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.

Keywords

Breast cancer

Fine needle aspiration

Artificial intelligent

Fulltext

Full text is available in PDF.

Automatic Classification of Cancerous Masses in Digital Mammograms Using Curvelet Analysis and Hybrid Genetic Algorithm-Artificial Neural Network Model

Fereshteh Torabi Jafroudi,

Azadeh Kiani-Sarkaleh,

Mahyar Nirouei

Torabi Jafroudi F, Kiani-Sarkaleh A, Nirouei M. Automatic Classification of Cancerous Masses in Digital Mammograms Using Curvelet Analysis and Hybrid Genetic Algorithm-Artificial Neural Network Model. I J Radiol. 2024;21(2):e146102. doi: https://doi.org/10.5812/iranjradiol-146102

15

Oct

2024

The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

Parsa Yazdanpanahi,

Farnaz Atighi,

Alireza Keshtkar,

Reza Hamidi,

Mohamadali Rezaeimanesh,

Alireza Karimi

,et al.

Yazdanpanahi P, Atighi F, Keshtkar A, Hamidi R, Rezaeimanesh M, et al. The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review. Shiraz E-Med J. 2024;25(11):e148493. doi: https://doi.org/10.5812/semj-148493

27

Nov

2023

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

Mohammad Jafar Dehghan,

Amirabbas Azizi

Dehghan MJ, Azizi A. A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm. Jundishapur J Nat Pharm Prod. 2023;18(4):e142058. doi: https://doi.org/10.5812/jjnpp-142058

2

Oct

2024

Clinical Implication of the New AI-TIRADS Classification of Thyroid Nodules; Our Real Clinical Experience

Mohammad Hadi Gharib,

Reza Afghani,

Siamak Rajaei,

Gholamreza Roshandel,

Ahmad Alijani,

Zolaykha Karamollahi

,et al.

Gharib MH, Afghani R, Rajaei S, Roshandel G, Alijani A, et al. Clinical Implication of the New AI-TIRADS Classification of Thyroid Nodules; Our Real Clinical Experience. Shiraz E-Med J. 2024;25(11):e147642. doi: https://doi.org/10.5812/semj-147642

30

Apr

2015

Application of Texture Analysis Method for Classification of Benign and Malignant Thyroid Nodules in Ultrasound Images

Ali Abbasian Ardakani,

Akbar Gharbali,

Afshin Mohammadi

Ardakani AA, Gharbali A, Mohammadi A. Application of Texture Analysis Method for Classification of Benign and Malignant Thyroid Nodules in Ultrasound Images. Int J Cancer Manag. 2015;8(2):e80600. doi:

Import into EndNote Import into BibTex

Share on

Comments

Number of Comments:0

Cited by

Scopus by DOI: 0
Last Update: 1 week ago
Scopus by Title: 7
Last Update: 1 week ago
Scopus by Title (Ref): 7
Last Update: 1 week ago
CrossRef: 0
Last Update: 3 days ago

Metrics

Ordering Reprints

Articles are published under the Creative Commons license stated on each article. No permission or royalty fee is required for uses permitted by that license. CCC handles optional bulk and customized reprint orders. Any quotation covers production and delivery services only, not copyright permission. > Request Reprints from CCC

Search Relations

Author(s):

Mohammad Fiuzy:[PubMed][Scholar]
Javad Haddadnia:[PubMed][Scholar]
Nasrin Mollania:[PubMed][Scholar]
Maryam Hashemian:[PubMed][Scholar]
Kazem Hassanpour:[PubMed][Scholar]

The Official Journal of Cancer Research Center (CRC), Shahid Beheshti University of Medical Sciences

Outlines

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

Abstract

Fulltext

Similar Articles

Automatic Classification of Cancerous Masses in Digital Mammograms Using Curvelet Analysis and Hybrid Genetic Algorithm-Artificial Neural Network Model

The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

Clinical Implication of the New AI-TIRADS Classification of Thyroid Nodules; Our Real Clinical Experience

Application of Texture Analysis Method for Classification of Benign and Malignant Thyroid Nodules in Ultrasound Images

Last Update: 1 week ago

Last Update: 1 week ago

Last Update: 1 week ago

Last Update: 3 days ago