Cytological Diagnostic and Prognostic Methods using Immunocytochemistry (Estrogen Receptor) for Surgical Management of Breast Cancer
Rohith R Nair1, Sonali Nandish2, Prathibha R. J3, Nandini N. M.41J.S.S. Medical College, Mysore, Karnataka, India 570015
2Department of Computer Science and Engineering, JSS Science and Technology University, Mysore, Karnataka, India 570006.
3Department of Information Science and Engineering, JSS Science and Technology University, Mysore, Karnataka, India 570006.
4Department of Pathology, JSS Medical College and Hospital, Constituent of JSSAHER, Mysore, Karnataka, India 570004.
Corresponding Author E-mail: nandinimanoli65@gmail.com
Abstract: The purpose of this study was to assess the utility of fine needle aspiration cytology (FNAC), immunocytochemistry(ICC) using estrogen receptor(ER) in diagnosing breast lesions. This was done by comparing it to histopathology with immunohistochemistry(IHC), which serves as the gold standard for diagnosing these lesions. To compare these modalities of investigation,50 samples were collected using FNAC and were compared to the same samples obtained by histopathology. For FNAC the results were as follows, Sensitivity=100%, Specificity=100%,Diagnostic Accuracy=100% Positive Predictive Value(PPV)=100% and Negative Predictive Value(NPV)=100% . For ICC using ER the results were as follows, Sensitivity=100%,Accuracy=100%,Positive Predictive Value=100%,Negative Predictive Value=100%. This indicates that FNAC and ICC using ER can be used as a reliable alternative to gold-standard diagnostic tests when the latter cannot be done due to a lack of resources or in circumstances where there is a need to perform a painless, minimally invasive procedure such as in inoperable breast carcinoma. This study also involved using text data analysis on FNAC reports. On analysis, it was found that the useful words were 11.35% of the data set, implying that the process of normalization, will result in the formation of condensed data, which can then be utilized for assisting clinical chart reviews and clinical decision support systems.
Keywords: Breast Cancer; ER; FNAC; Histopathology; ICC; Immunohistochemistry; Machine Learning(ML); Natural Language Processing(NLP); Text Data Analysis Back to TOC