A step-wise logistic regression analysis of hepatocellular carcinoma an aspiration biopsy study

Open Access
Publisher Website
Google Scholar
Abstract
Fine needle aspiration biopsy (FNAB) has become a popular method to diagnose mass lesions of the liver. Although several reports have listed FNAB criteria to be used to diagnose both primary and metastatic tumors of the liver, none have separated key cytologic criteria from secondary criteria. We reviewed the FNAB smears from 35 patients with proven hepatocellular carcinoma and 74 patients with proven metastatic tumors in the liver. All specimens were coded as to the presence or absence of the following variables: polygonal cells with centrally placed nuclei; well‐defined, granular cytoplasm; large nucleoli; small cytoplasmic vacuoles; large cytoplasmic vacuoles; bile; polymorphonuclear leukocytes; malignant cells separated by sinusoidal vessels; endothelial cells surrounding tumor cell clusters; multi‐nucleated tumor giant cells; basophilic intracytoplasmic inclusions; eosinophilic intracytoplasmic inclusions; and intranuclear cytoplasmic inclusions. A step‐wise logistic regression analysis was performed on the data to determine the variables predictive of hepatocellular carcinoma. The statistical analysis selected polygonal cells with centrally placed nuclei, malignant cells separated by sinusoidal capillaries, and bile as the key cytologic criteria for hepatocellular carcinoma. Endothelial cells surrounding tumor cell clusters and intranuclear cytoplasmic inclusions were selected as secondary criteria by this analysis.