Application of discriminant analysis and quantitative cytologic examination to gastric lesions.
OBJECTIVE
To investigate of the potential value of morphometry and discriminant analysis for the classification of benign and malignant gastric cells and lesions.


METHODS
The data set consisted of 13,300 cells from 120 cases composed of 30 cases of cancer, 26 cases of gastritis and 64 cases of ulcer according to the final histologic diagnosis. The cytologic diagnosis was divided into 5 categories (gastritis, ulcer, inflammatory dysplasia, cancer and true dysplasia). Classification was attempted at 2 levels: the cell level to classify individual cells and the case level to classify individual cases. For the cellular classification the measured cells from 50% of available cases were selected as a training set to construct a model. The cells from the remaining cases were used as a test set to validate the model. Similarly for case classification, the same 50% of cases that were used for cell classification were used as a training set and the remaining cases as a test set. Images of routinely processed gastric smears stained by the Papanicolaou technique were analyzed by a customized image analysis system.


RESULTS
Application of discriminant analysis on the test set gave correct classification of 98.4% of benign cells and 67.1% of malignant cells. On case classification, 100% accuracy was achieved for benign and malignant cases, both for the training and test sets.


CONCLUSIONS
The application of discriminant analysis described in this paper could produce significant classification results at the cellular and individual case level.
Karakitsos
Petros
P
Department of Cytopathology, Attikon University Hospital, Athens, Greece.
Megalopoulou
Tatiana Mona
TM
Pouliakis
Abraham
A
Tzivras
Michalis
M
Archimandritis
Athanasios
A
Kyroudes
Aspasia
A
D048429:Cell Size; D003581:Cytodiagnosis; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005756:Gastritis / Q000175:diagnosis / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015203:Reproducibility of Results; D012984:Software; D013194:Staining and Labeling; D013274:Stomach Neoplasms / Q000175:diagnosis* / Q000473:pathology; D013276:Stomach Ulcer / Q000175:diagnosis / Q000473:pathology