Cytologic criteria used to diagnose adenocarcinoma in pleural effusions.
A stepwise logistic regression analysis was employed to evaluate the usefulness of the following criteria to distinguish adenocarcinoma in pleural effusion from benign pleural effusion: increased size of nucleus, increased nuclear to cytoplasmic ratio, irregular nuclear borders, sharply defined cytoplasmic boundaries, large nucleoli, aggregates with variable size nuclei, aggregates with nuclear overlap, irregular noncentral vacuoles, aggregates with nuclear molding, multinucleation, three-dimensional aggregates, aggregates with large cytoplasmic vacuoles, atypical mitoses, nuclear vacuoles, homogeneous cytoplasm, aggregates with associated lymphocytes and neutrophils, and uniform size aggregates. A total of 223 patients with benign pleural effusion cases and 221 patients with adenocarcinoma in their pleural effusion were scored as to the presence or absence of the above criteria. The resulting data were subjected to a stepwise logistic regression analysis that chose increased nuclear/cytoplasmic ratio, irregular nuclear borders, large nucleoli, sharply defined cytoplasmic boundaries, and three-dimensional aggregates as the best criteria to differentiate adenocarcinoma in pleural effusion from benign pleural effusion. When used together, these five features had a sensitivity of 94% and a specificity of 93% for predicting adenocarcinoma.
Bottles
K
K
Department of Pathology, Veteran's Administration Medical Center, Iowa City, Iowa.
Reznicek
M J
MJ
Holly
E A
EA
Ahn
D K
DK
Layfield
L J
LJ
Cohen
M B
MB
D000230:Adenocarcinoma / Q000473:pathology*; D002449:Cell Aggregation; D002466:Cell Nucleolus / Q000648:ultrastructure; D002467:Cell Nucleus / Q000648:ultrastructure; D003593:Cytoplasm / Q000648:ultrastructure; D003937:Diagnosis, Differential; D006801:Humans; D008214:Lymphocytes / Q000473:pathology; D008938:Mitosis; D009504:Neutrophils / Q000473:pathology; D010996:Pleural Effusion / Q000473:pathology*; D010997:Pleural Neoplasms / Q000473:pathology*; D012044:Regression Analysis