Background: Fine Needle Aspiration Cytology (FNAC) is a simple, minimally invasive, cost effective, outpatient based and a rapid diagnostic method for breast lesions. The aim of the present study was to correlate cytological findings with histopathological findings and to determine the accuracy of FNAC in the diagnosis of breast lesions.
Material And Methods: A total of 222 breast aspirates were studied. Histo-cytopathological correlations were obtained in 91 cases. All the aspirates were stained with Haematoxylin and Eosin (H andE) stain.
Results: Among 222 patients, 217 were females and 5 were males. Benign breast lesions were found in 144 cases (64.87%); among which fibroadenoma (30.18%) was the commonest lesion which was observed. Malignancy was observed in 69 cases (31.08%); among them, ductal carcinoma was the predominant lesion (29.28%) which was seen. Histopathological confirmations were obtained in 90 cases out of 91 cases in which histo-cytopathological corrections were possible. All 45 malignant aspirates were confirmed by histopathology. Benign reports were confirmed in 45 out of 46 cases by doing histological examinations; except one case which was diagnosed as malignant by studying its histopathology. Sensitivity and specificity of FNAC in breast lesions were reported to be 97.82% and 100% respectively, with 100% positive predictive value and 97.85% negative predictive value. Diagnostic accuracy of FNAC in the present study was found to be 98.90%.
Conclusion: It is important to remember that a negative FNAC of a breast lesion does not preclude the diagnosis of a carcinoma, particularly in presence of a clinical suspicion of malignancy and/or an abnormal mammogram.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3919414 | PMC |
| http://dx.doi.org/10.7860/JCDR/2013/6507.3756 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A rare case of hydatid cyst presenting as a breast cancer. A case report.
Int J Surg Case Rep
December 2024
Debre Markos University, Surgery Department, Ethiopia. Electronic address:
Introduction And Importance: Hydatid disease, caused by the Echinococcus parasite, is a significant health concern in endemic regions. While commonly found in the liver and lungs, breast involvement is rare. We present a case of a hydatid cyst in the breast of a 34-year-old woman from Ethiopia, initially suspected to be breast cancer.
View Article and Find Full Text PDFPredicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.
Front Immunol
December 2024
Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Objective: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance for developing scientifically individualized treatment plans, assessing prognosis, and planning preoperative interventions.
Methods: A retrospective analysis was conducted on clinical and imaging data from 270 patients with BC confirmed by surgical pathology at the Third Hospital of Shanxi Medical University between November 2022 and April 2024.
Prognostic Implications of the Bethesda System in Fine-Needle Aspiration for Follicular Thyroid Carcinoma.
Arch Pathol Lab Med
December 2024
From the Department of Medicine, Thyroid Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (SW Kim, JH Chung, TH Kim).
Context.—: Fine-needle aspiration is an effective tool for sampling thyroid nodules; its results are classified according to the Bethesda System for Reporting Thyroid Cytopathology (BSRTC), whose categories define malignancy risks.
Objective.
Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions.
Sci Rep
December 2024
Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, 250014, Shandong, People's Republic of China.
This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.
View Article and Find Full Text PDFCorrelation between ultrasonography and elastography parameters and molecular subtypes of breast cancer in young women.
Ann Med
December 2025
Department of Ultrasonographl, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, Shanxi Province, China.
Objective: To explore the differences of conventional ultrasound characteristics, elastic imaging parameters and clinicopathological characteristics of distinct molecular subtypes of breast cancer in young women, and to identify imaging parameters that exhibited significant associations with each molecular subtype.
Methods: We performed a retrospective analysis encompassing 310 young women with breast cancer. Observations were made regarding the ultrasonography and elastography characteristics of the identified breast lesions.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!