Fine Needle Aspiration Cytology of the Breast: The Nonmalignant Categories

Currently, accurate diagnosis of breast lesions depends on a triple assessment approach comprising clinical, imaging and pathologic examinations. Fine needle aspiration cytology (FNAC) is widely adopted for the pathologic assessment because of its accurracy and ease of use. While much has been written about the atypical and maliganant categories of FNAC diagnosis, little covers the non-malignanat category which represents a sheer number in all FNAC cases. Moreover, any false-negative diagnosis of the non-malignant cases may lead to missed diagnosis of cancer. This paper aims to discuss the issues of smear adequacy, the cytologic features of benign breast lesions and the dilemma of a false-negative aspirate. Much has been suggested about the smear adequacy criterion, including quantifying epithelial clusters, whereas others advocate basing adequacy on qualitative quantum of using noncellular features of FNAC. Various benign lesions could be easily diagnosed at FNAC; however, they have cytologic features overlapped with malignant lesions. False negativity of FNAC does occur; this could be caused by either “true” false-negative cases attributed to suboptimal sampling technique, poor localization of the mass or nonpalpable lesions or “false” false-negative cases due to interpretational errors. Though false-positive cases are less commonly found, they will also be discussed briefly

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer