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Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery

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Abstract

In this paper, we applied Bayesian multi-layer perceptrons (MLP) using the evidence procedure to predict malignancy of ovarian masses in a large (n = 1,066) multi-centre data set. Automatic relevance determination (ARD) was used to select the most relevant inputs. Fivefold cross-validation (5CV) and repeated 5CV was used to select the optimal combination of input set and number of hidden neurons. Results indicate good performance of the models with area under the receiver operating characteristic curve values of 0.93–0.94 on independent data. Comparison with a linear benchmark model and a previously developed logistic regression model shows that the present problem is very well linearly separable. A resampling analysis further shows that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance. This paper shows that Bayesian MLPs, although not frequently used, are a useful tool for detecting malignant ovarian tumours.

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Acknowledgments

This research was supported by the Research Council KUL: GOA-AMBioRICS, CoE EF/05/006 Optimization in Engineering, several PhD/postdoc and fellow grants, Flemish Government: FWO [PhD/postdoc grants, projects, G.0407.02 (support vector machines), G.0360.05 (EEG, Epileptic), G.0519.06 (Noninvasive brain oxygenation), FWO-G.0321.06 (tensors/spectral analysis), research communities (ICCoS, ANMMM)]; IWT (PhD grants), Belgian Federal Science Policy Office IUAP P5/22 (`Dynamical Systems and Control: Computation, Identification and Modelling’), EU: BIOPATTERN (FP6-2002-IST 508803), ETUMOUR (FP6-2002-LIFESCIHEALTH 503094), Healthagents (IST 2004–27214), and by research grants from the Swedish Medical Research Council (grants nos. K98-17X-11605-03A, K2001-72X-11605-06A and K2002-72X-11605-07B), by funds administered by Malmö University Hospital, Allmänna Sjukhusets i Malmö Stiftelse för bekämpande av cancer (the Malmö General Hospital Foundation for fighting against cancer) and ALF-medel (a Swedish governmental grant from the region of Scania).

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Authors and Affiliations

  1. Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium

    Ben Van Calster & Sabine Van Huffel

  2. Department of Obstetrics and Gynaecology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium

    Dirk Timmerman, Caroline Van Holsbeke & Ignace Vergote

  3. Neural Computing Research Group (NCRG), Aston University, Aston Triangle, Birmingham, B4 7ET, UK

    Ian T. Nabney

  4. Department of Obstetrics and Gynaecology, Malmö University Hospital, 205 02, Malmö, Sweden

    Lil Valentin

  5. Gynecologic Oncology Unit, Università Cattolica del Sacro Cuore, Largo Agostino Gemelli 8, 00168, Rome, Italy

    Antonia C. Testa

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  1. Ben Van Calster
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  2. Dirk Timmerman
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  3. Ian T. Nabney
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  7. Ignace Vergote
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  8. Sabine Van Huffel
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Corresponding author

Correspondence to Ben Van Calster.

Additional information

Parts of this work were originally presented at the 28th annual international conference of the IEEE Engineering in Medicine and Biology Society, 31 August–3 September 2006, New York City (NY), USA.

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Van Calster, B., Timmerman, D., Nabney, I.T. et al. Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery. Neural Comput & Applic 17, 489–500 (2008). https://doi.org/10.1007/s00521-007-0147-1

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  • DOI: https://doi.org/10.1007/s00521-007-0147-1

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