Τίτλος:
Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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. Conclusion: 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. © 2017 Wiley Periodicals, Inc.
Συγγραφείς:
Makris, G.-M.
Pouliakis, A.
Siristatidis, C.
Margari, N.
Terzakis, E.
Koureas, N.
Pergialiotis, V.
Papantoniou, N.
Karakitsos, P.
Περιοδικό:
Diagnostic Cytopathology
Εκδότης:
John Wiley and Sons Inc
Λέξεις-κλειδιά:
adult; algorithm; Article; artificial neural network; cell nucleus; classifier; computer assisted diagnosis; controlled study; diagnostic accuracy; dilatation and curettage; endometrial disease; endometrium cancer; female; histopathology; human; human tissue; image analysis; major clinical study; perceptron; predictive value; sensitivity and specificity; Youden index; artificial neural network; diagnostic error; diagnostic imaging; endometrioid carcinoma; endometrium tumor, Carcinoma, Endometrioid; Diagnostic Errors; Endometrial Neoplasms; Female; Humans; Image Interpretation, Computer-Assisted; Neural Networks (Computer); Sensitivity and Specificity
