Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions.
BACKGROUND
This study aims to investigate the efficacy of an Artificial Neural Network based on Multi-Layer Perceptron (ANN-MPL) to discriminate between benign and malignant endometrial nuclei and lesions in cytological specimens.


METHODS
We collected 416 histologically confirmed liquid-based cytological smears from 168 healthy patients, 152 patients with malignancy, 52 with hyperplasia without atypia, 20 with hyperplasia with atypia, and 24 patients with endometrial polyps. The morphometric characteristics of 90 nuclei per case were analyzed using a custom image analysis system; half of them were used to train the MPL-ANN model, which classified each nucleus as benign or malignant. Data from the remaining 50% of cases were used to evaluate the performance and stability of the ANN. The MLP-ANN for the nuclei classification (numeric and percentage classifiers) and the algorithms for the determination of the optimum threshold values were estimated with in-house developed software for the MATLAB v2011b programming environment; the diagnostic accuracy measures were also calculated.


RESULTS
The accuracy of the MPL-ANN model for the classification of endometrial nuclei was 81.33%, while specificity was 88.84% and sensitivity 69.38%. For the case classification based on numeric classifier the overall accuracy was 90.87%, the specificity 93.03% and the sensitivity 87.79%; the indices for the percentage classifier were 95.91%, 93.44%, and 99.42%, respectively.


CONCLUSIONS
Computerized systems based on ANNs can aid the cytological classification of endometrial nuclei and lesions with sufficient sensitivity and specificity. Diagn. Cytopathol. 2017;45:202-211. © 2016 Wiley Periodicals, Inc.
Unit of Gynecological Oncology, Third Department of Obstetrics and Gynecology, General Hospital "Attikon", Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Georgios-Marios
GM
Makris
Department of Cytopathology, General Hospital "Attikon", Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Abraham
http://orcid.org/0000-0002-0074-3619
A
Pouliakis
Assisted Reproduction Unit, Third Department of Obstetrics and Gynecology, General Hospital "Attikon", Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Charalampos
C
Siristatidis
Department of Cytopathology, General Hospital "Attikon", Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Niki
N
Margari
2nd Department of Gynecology, St. Savas Hospital, Athens, Greece.
Emmanouil
E
Terzakis
2nd Department of Gynecology, St. Savas Hospital, Athens, Greece.
Nikolaos
N
Koureas
Unit of Gynecological Oncology, Third Department of Obstetrics and Gynecology, General Hospital "Attikon", Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Vasilios
V
Pergialiotis
Unit of Gynecological Oncology, Third Department of Obstetrics and Gynecology, General Hospital "Attikon", Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Nikolaos
N
Papantoniou
Department of Cytopathology, General Hospital "Attikon", Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Petros
P
Karakitsos
D018269:Carcinoma, Endometrioid / Q000000981:diagnostic imaging*; D003951:Diagnostic Errors; D016889:Endometrial Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity
artificial neural networks; computer-assisted diagnosis; endometrial cytology; liquid-based cytology; multilayer perceptron