scholarly journals PENERAPAN METODE RBPNN UNTUK KLASIFIKASI KANKER PAYUDARA

2016 ◽  
Vol 3 (2) ◽  
pp. 135
Author(s):  
Fairudz Shahura ◽  
Oni Soesanto ◽  
Fatma Indriani
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Fractal Dimension ◽  
Fine Needle Aspiration ◽  
Probabilistic Neural Network ◽  
Needle Aspiration ◽  
Training Data ◽  
Fine Needle ◽  
Radial Basis ◽  
Malignant Breast

<p><em>Breast cancer is the most commonly diagnosed cancer in women. Breast cancer cases are increasing each year. Therefore, early detection of breast cancer plays an important role in anticipating the spread of cancer. Fine-needle aspiration (FNA) biopsy is one way to detect breast cancer. FNA is a method of taking the majority of tissue with a syringe that is intended to aid in the diagnosis of various tumor diseases. The FNA samples that have been studied generate ten characteristics, namely radius, texture, perimeter, area, compactness, smoothness, concavity, concave points, symmetry, and fractal dimension. These characteristics are used to classify benign and malignant breast cancer. To classify breast cancer, Radial Basis Probabilistic Neural Network (RBPNN) required. This study aims to determine how the performance of the method of Radial Basis Probabilistic Neural Network for classifying breast cancer. The accuracy was found to be equal 93.19% for training data, and 90.35% for testing data.</em><em></em></p><p><strong><em>Keywords:</em></strong><em> Radial Basis Probabilistic Neural Network, Classification, Breast Cancer.</em></p><p><em>Kanker payudara merupakan penyakit yang paling banyak menyerang kaum wanita. Penderita penyakit kanker payudara semakin meningkat pada tiap tahunnya. Oleh karena itu deteksi dini kanker payudara memegang peranan penting dalam mengantisipasi penyebaran kanker. Salah satu cara untuk mendeteksi kanker payudara adalah  dengan fine-needle aspiration (FNA) biopsy. FNA merupakan suatu metode pengambilan sebagian jaringan tubuh manusia dengan jarum suntik yang bertujuan untuk membantu diagnosis berbagai penyakit tumor. Sampel FNA yang telah diteliti menghasilkan sepuluh karakteristik, yaitu radius, texture, perimeter, area, compactness, smoothness, concavity, concave points, symmetry, dan fractal dimension. Kesepuluh karakteristik tersebut digunakan untuk mengklasifikasikan kanker payudara jinak dan ganas. Untuk mengklasifikasi tingkat keganasan dari kanker payudara dapat dilakukan dengan metode Radial Basis Probabilistic Neural Network (RBPNN). Penelitian ini bertujuan untuk mengetahui bagaimana performansi metode Radial Basis Probabilistic Neural Network untuk mengklasifikasikan kanker payudara. Dari hasil penelitian didapat akurasi 93.19% untuk data training, serta 90.35% untuk data testing.</em><em></em></p><strong><em>Kata kunci :</em></strong><em> Radial Basis Probabilistic Neural Network, Klasifikasi, Breast Cancer.</em>

scholarly journals Fine-needle Aspiration Cytology to Identify a Rare Mimicker of Breast Cancer: Plasma Cell Mastitis

2018 ◽  
Vol 40 (08) ◽  
pp. 491-493
Author(s):  
Carlos Ortiz-Mendoza ◽  
Norma Sánchez ◽  
Arturo Dircio
Keyword(s):  
Breast Cancer ◽  
Breast Neoplasms ◽  
Plasma Cell ◽  
Fine Needle Aspiration ◽  
Fine Needle Aspiration Cytology ◽  
Needle Aspiration ◽  
Aspiration Cytology ◽  
Fine Needle ◽  
Malignant Breast ◽  
Needle Aspiration Cytology

AbstractThere are rare benign diseases that can mimic malignant breast neoplasms in the clinical exam and in mammography. We evaluated the contribution of an accessible procedure to most clinicians, the fine-needle aspiration cytology, to identify a rare mimicker of malignant breast neoplasms. A type 2 diabetic 85-year-old female presented with a 6-month history of a left breast lump. The physical exam and mammography were compatible with breast cancer. Nevertheless, after fine-needle aspiration cytology, the diagnosis was plasma cell mastitis. Once this rare diagnosis was established, the tumor was extirpated, and the final histologic diagnosis corroborated chronic plasma cell mastitis. The patient's postoperative evolution was uneventful, and no other treatment was needed. Fine-needle aspiration cytology could be a valuable tool to identify rare mimickers of malignant breast neoplasms.

scholarly journals Correlation Between Ultrasound BI-RADS 4 Breast Lesions and Fine Needle Cytology Categories in a Sample of Iraqi Female Patients

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hiba Mohammed Abdulwahid ◽  
Zahraa Yahya Mohammed ◽  
Furat Nidhal ◽  
Farah A.J. AL Zahwi ◽  
Muna Jumaa Ali
Keyword(s):  
Breast Cancer ◽  
Fine Needle Aspiration ◽  
Breast Lesion ◽  
Malignant Lesion ◽  
Needle Aspiration ◽  
Accurate Estimation ◽  
Breast Lesions ◽  
Fine Needle ◽  
Case Series Study ◽  
Malignant Breast

Abstract Background: Breast cancer is the most common malignancy in female and the most registered cause of women’s mortality worldwide. BI-RADS 4 breast lesions are associated with an exceptionally high rate of benign breast pathology and breast cancer, so BI-RADS 4 is subdivided into 4A, 4B and 4C to standardize the risk estimation of breast lesions. The aim of the study: to evaluate the correlation between BI-RADS 4 subdivisions 4A, 4B & 4C and the categories of reporting FNA cytology results. Patients and Methods: A case series study was conducted in the Oncology Teaching Hospital in Baghdad from September 2018 to September 2019. Included patients had suspicious breast findings and given BI-RADS 4 (4A, 4B, or 4C) in the radiological report accordingly. Fine needle aspiration was performed under the ultrasound guide and the results were classified into five categories. The biopsy was performed for suspicious, malignant or equivocal FNA findings. Results: This study included 158 women with BIRADS 4 breast lesions with the mean age of (44.6 years); There was a highly significant association between BI-RADS 4 breast lesion and FNA results (p<0.001); 51.9% of BI-RADS IV-C had C5 FNA results. There was a highly significant association between BI-RADS 4 lesion and the final diagnosis (p<0.001); 41.2% of BI-RADS 4 B had a malignant breast lesion, while 37.3% of BIRADS 4 C had a malignant lesion. Conclusion: A clear relationship was observed between BI-RADS 4 subcategories and the fine needle aspiration cytology subgroups. BI-RADS 4-B is helpful in the discrimination between benign and malignant breast lesions; furthermore BI-RADS 4C has more acceptable validity in the diagnosis of breast malignancy. Therefore, BI-RADS subcategories are encouraged to be included and mentioned in the ultrasound report for more accurate estimation of the lesion nature.

Flow cytometry in breast cancer: cell yield using two different fine needle aspiration devices

2005 ◽  
Vol 59 (9) ◽  
pp. 1034-1038 ◽  
Author(s):  
R. Freitas Júnior ◽  
R. Camplejohn ◽  
I. S. Fentiman ◽  
S. A. Souza
Keyword(s):  
Breast Cancer ◽  
Flow Cytometry ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Fine Needle Aspiration ◽  
Needle Aspiration ◽  
Cell Yield ◽  
Fine Needle
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