Title: Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses
Authors: Timmerman, Dirk ×
Verrelst, Herman
Bourne, Tom
De Moor, Bart
Collins, W P
Vergote, Ignace
Vandewalle, Joos #
Issue Date: Jan-1999
Series Title: Ultrasound in Obstetrics & Gynecology vol:13 issue:1 pages:17-25
Abstract: OBJECTIVE: The aim of this study was to generate and evaluate artificial neural network (ANN) models from simple clinical and ultrasound-derived criteria to predict whether or not an adnexal mass will have histological evidence of malignancy. DESIGN: The data were collected prospectively from 173 consecutive patients who were scheduled to undergo surgical investigations at the University Hospitals, Leuven, between August 1994 and August 1996. The outcome measure was the histological classification of excised tissues as malignant (including borderline) or benign. METHODS: Age, menopausal status and serum CA 125 levels and sonographic features of the adnexal mass were encoded as variables. The ANNs were trained on a randomly selected set of 116 patient records and tested on the remainder (n = 57). The performance of each model was evaluated using receiver operating characteristic (ROC) curves and compared with corresponding data from an established risk of malignancy index (RMI) and a logistic regression model. RESULTS: There were 124 benign masses, five of borderline malignancy and 44 invasive cancers (of which 29% were metastatic); 37% of patients with a malignant or borderline tumor had stage I disease. The best ANN gave an area under the ROC curve of 0.979 for the whole dataset, a sensitivity of 95.9% and specificity of 93.5%. The corresponding values for the RMI were 0.882, 67.3% and 91.1%, and for the logistic regression model 0.956, 95.9% and 85.5%, respectively. CONCLUSION: An ANN can be trained to provide clinically accurate information, on whether or not an adnexal mass is malignant, from the patient's menopausal status, serum CA 125 levels, and some simple ultrasonographic criteria.
ISSN: 0960-7692
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Gynaecological Oncology
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Basic Research in Gynaecology Section (-)
Section Woman - Miscellaneous (-)
Electrical Engineering - miscellaneous
Screening, Diagnostics and Biomarkers (-)
× corresponding author
# (joint) last author

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