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Cited by 134Ovarian cancer diagnosis and treatmentAI in cancer detectionArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses

Timmerman, D., Verrelst, H., Bourne, T. H., De Moor, B., Collins, W. P., Vergote, I., Vandewalle, J.·1999·Ultrasound in Obstetrics & Gynecology
DOI 10.1046/j.1469-0705.1999.13010017.x
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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 border‐line 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. Copyright © 1999 International Society of Ultrasound in Obstetrics and Gynecology

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Metadata

Authors
Timmerman, D., Verrelst, H., Bourne, T. H., De Moor, B., Collins, W. P., Vergote, I., & Vandewalle, J.
Affiliations
  • Universitair Ziekenhuis Leuven (BE)
  • KU Leuven (BE)
  • St George's Hospital (GB)
  • King's College London (GB)
Corresponding author
Timmerman, D.
ORCID
Venue
Ultrasound in Obstetrics & Gynecology
Publisher
Wiley
Published
1999-01
Volume
13
Issue
1
Pages
17-25
ISSN
  • 0960-7692 (ISSN-L)
  • 1469-0705
DOI
10.1046/j.1469-0705.1999.13010017.x
OpenAlex
W2081425519
Cited by
134
References
8
Citations by year
2026: 3 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 6 · 2021: 4 · 2020: 3 · 2019: 4 · 2018: 2 · 2017: 4 · 2016: 1 · 2015: 5 · 2014: 3 · 2013: 2 · 2012: 4
FWCI
6.18
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 1452 d)
Locations
  • Ultrasound in Obstetrics and Gynecology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Oncology › Ovarian cancer diagnosis and treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
adnexal mass, ovarian cancer, artificial neural networks, CA125, risk of malignancy index, sonographic features, menopausal status, borderline ovarian tumors, diagnostic accuracy
MeSH terms
Adnexal Diseases, Adult, Aged, Aged, 80 and over, Computer Simulation, Female, Humans, Middle Aged, Neoplasm Staging, Ovarian Neoplasms, Predictive Value of Tests, Preoperative Care, Prospective Studies, Random Allocation, ROC Curve, Vagina, Reproducibility of Results, Discriminant Analysis, Neural Networks, Computer, CA-125 Antigen, Ultrasonography, Doppler, Color, Endosonography

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