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Diagnostic Accuracy of Axillary Ultrasonography Compared with Intra-operative Pathological Findings in Patients with Breast Cancer.

Alireza Rezvani | Aryan Zahergivar | Pooya Iranpour | Majid Akrami | Sepehr Kazemi
Asian Pacific journal of cancer prevention : APJCP | 2018

Objective: Breast cancer is the most common cancer and leading cause of mortality in women worldwide. Diagnosis of axillary lymph node involvement, frequently by axillary ultrasonography (AUS), is an important step in patients with primary breast cancer, while the gold standard pathological examination is only applicable during surgery. Thus, we aimed to evaluate the predictive value of AUS in detection of lymph node involvement. Methods: A total of 56 patients with primary breast cancer, who were candidate of mastectomy were included in this study. A single radiologist performed pre-operative axillary sonography for all the patients and reported the results in a checklist. The results were then compared with intra-operative pathological results of lymph node dissection to evaluate the sensitivity, specificity, positive and negative predictive values of AUS in detection of the disease. Result: The results showed that pre-operative AUS had a sensitivity of 63.3%, specificity of 84.6%, positive predictive value of 82.6%, and negative predictive value of 66.6%. Correlation between axillary lymph node characteristics revealed that the absence of fatty hilum, cortical thickness, and loss of ovality of the lymph nodes were the most specific factors in detection of lymph nodes’ involvement. Conclusion: AUS has a low sensitivity and cannot determine the presence of micro-metastases and suspicious lymph nodes accurately in early stages of the disease, and it seems that defining new parameters may help for increasing the sensitivity and specificity of the axillary lymph node sonography. Apparently, pathological examination remains the gold standard diagnostic method.

Pubmed ID: 30583690

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MATLAB (tool)

RRID:SCR_001622

Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.

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