Fine-needle aspiration cytology (FNA) is less traumatic and technically easy to apply to small breast tumors.
A total of 382 cases of palpable breast lesions that had undergone fine needle aspiration and histopathologic diagnosis were reviewed with an emphasis on the rate of false positive diagnoses in benign breast lesions.
A diagnosis of " malignant " was made in 98 of the 382 specimens (25.6%). The predictive value for malignancy was 97.9%. The sensitivity, specificity, and accuracy of FNA were 86.3%, 98.2%, and 93.2%, respectively, when the " suspicious " group was considered positive for malignancy. The histologic subtypes of the 4 false-positive cases were epithelial proliferative lesions, ductal or lobular hyperplasia. None of these 4 cases were definitely diagnosed as " malignant " by radiological studies. Four false-negative cases by FNA were suspicious for malignancy radiologically. There was no specific pathological subtype associated with false-negative status on FNA in this study.
Palpable breast tumors can be definitively diagnosed based on a combination of physical examination, radiological studies and FNA, when the radiological studies concur with the diagnosis by FNA.
"Fine needle Aspiration Cytology has been proved to be a highly efficacious method in diagnosis of palpable breast lesion in our study. The sensitivity of 98.07% and specificity of 100% obtained in our study were in accordance to sensitivity of 86-99% and specificity of 92-100% reported in various studies (Jayaram et al., 1996; Rubin et al., 1997; Argia et al., 2002; Hussain, 2005; Muhamed et al., 2005; Ishikawa et al., 2007). "
[Show abstract][Hide abstract] ABSTRACT: To determine the pattern of disease in patients presenting with breast lumps and to determine the sensitivity and specificity of fine needle aspiration cytology of benign and malignant diseases as a diagnostic tool by correlating with histopathology findings. This retrospective study was carried out in the Department of Pathology, Maharaja Agrasen Medical College, Agroha, from Jan 2008 to April 2012. Fine needle aspiration cytology was performed on 370 cases and out of these 52 cases were received in the Department for histopathological examination. Fibroadenoma was the most common disease encountered, in 88 (24%), with a peak incidence in second and third decade of life. Fibrocystic disease was second, being common in the third and fourth decades of life. Peak incidences of duct ectasia, granulomatous and tubercular mastitis were seen in the third decade. Gynaecomastia showed two peak incidences in second and sixth decades of life . Out of total 370 cases undergoing fine needle aspiration, benign cases were 316 (85.4%), malignant and suspicious were 54 (14.6%) and 10 (2.70%) respectively. Out of total 22 histological confirmed malignant lesions 19 were interpreted as malignant cytologically while two as suspicious and one as benign. All thirty histologically confirmed benign cases were diagnosed as benign cytologically. The sensitivity, specificity, positive and negative predictive values were 98%, 100%, 100% and 96.4% respectively. FNA cytology is highly accurate for diagnosis of breast masses. However, the clinician should correlate FNA cytological results with physical examination and imaging findings to prevent false negative and false positive events and to obtain optimal management of their patients.
Asian Pacific journal of cancer prevention: APJCP 12/2013; 14(12):7257-60. DOI:10.7314/APJCP.2013.14.12.7257 · 2.51 Impact Factor
"Palpable breast lesions can be accurately diagnosed by preoperative tests (like physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy)  . Mammography is most often used for screening purposes rather than for precise diagnosis. "
[Show abstract][Hide abstract] ABSTRACT: The purpose of this paper is to develop an intelligent diagnosis system for breast cancer classification. Artificial Neural Networks and Support Vector Machines were being developed to classify the benign and malignant of breast tumor in fine needle aspiration cytology. First the features were extracted from 92 FNAC image. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used namely multilayer perceptron (MLP) using back-propagation algorithm, probabilistic neural networks (PNN), learning vector quantization (LVQ) and support vector machine (SVM). The classification results were obtained using 10-fold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity and specificity. The method was evaluated using six different datasets including four datasets related to our work and two other benchmark datasets for comparison. The optimum network for classification of breast cancer cells was found using probabilistic neural networks. This is followed in order by support vector machine, learning vector quantization and multilayer perceptron. The results showed that the predictive ability of probabilistic neural networks and support vector machine are stronger than the others in all evaluated datasets.
"False-positive cases included cases with papilloma or fibroadenoma, which involve marked hyperplasia of epithelial cells
[12,13]. Therefore, particular care is needed during cytology of cases in which a papillary structure or a cribriform pattern is seen. "
[Show abstract][Hide abstract] ABSTRACT: BackgroundWe previously investigated the current status of breast cytology cancer screening at seven institutes in our area of southern Fukuoka Prefecture, and found some differences in diagnostic accuracy among the institutions. In the present study, we evaluated the cases involved and noted possible reasons for their original cytological classification as inadequate, indeterminate, false-negative and false-positive according to histological type.MethodsWe evaluated the histological findings in 5693 individuals who underwent cytological examination for breast cancer (including inadequate, indeterminate, false-negative and false-positive cases), to determine the most common histological types and/or features in these settings and the usefulness/limitations of cytological examination for the diagnosis of breast cancer.ResultsAmong 1152 cytologically inadequate cases, histology revealed that 75/173 (43.6%) cases were benign, including mastopathy (fibrocystic disease) in 38.6%, fibroadenoma in 24.0% and papilloma in 5.3%. Ninety-five of 173 (54.9%) cases were histologically malignant, with scirrhous growing type, invasive ductal carcinoma (SIDC) being significantly more frequent (49.5%) than papillotubular growing type (Papi-tub) (P < 0.0001), solid-tubular growing type (P = 0.0001) and ductal carcinoma in situ (DCIS) (P = 0.0001). Among 458 indeterminate cases, 54/139 (38.8%) were histologically benign (mastopathy, 30.0%; fibroadenoma, 27.8%; papilloma, 26.0%) and 73/139 (52.5%) were malignant, with SIDC being the most frequent malignant tumor (37.0%). Among 52 false-negative cases, SIDC was significantly more frequent (42.3%) than DCIS (P = 0.0049) and Papi-tub (P = 0.001). There were three false-positive cases, with one each of fibroadenoma, epidermal cyst and papilloma.ConclusionsThe inadequate, indeterminate, false-negative and false-positive cases showed similar histological types, notably SIDC for malignant tumors, and mastopathy, fibroadenoma and papilloma for benign cases. We need to pay particular attention to the collection and assessment of aspirates for these histological types of breast disease. In particular, several inadequate, indeterminate and false-negative cases with samples collected by aspiration were diagnosed as SIDC. These findings should encourage the use of needle biopsy rather than aspiration when this histological type is identified on imaging. Namely, good communication between clinicians and pathological staff, and triple assessment (i.e., clinical, pathological and radiological assessment), are important for accurate diagnosis of aspiration samples.Virtual slidesThe virtual slide(s) for this article can be found here:
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