Clinical decision support for ovarian tumor diagnosis using Bayesian models: Results from the IOTA study

Authors:
Ben Van Calster;Olivier Gevaert;Caroline Van Holsbeke;Bart De Moor;Sabine Van Huffel;Dirk Timmerman
Affiliations:
Dept. of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium;Dept. of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium;Dept. of Obstetrics and Gynecology, University Hospitals K.U. Leuven, Belgium and Dept. of Obstetrics and Gynecology, Ziekenhuis Oost-Limburg Genk, Belgium;Dept. of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium;Dept. of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium;Dept. of Obstetrics and Gynecology, University Hospitals K.U. Leuven, Belgium
Venue:
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Year:
2009

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Abstract

Many sophisticated methods exist to develop clinical decision support systems for daily clinical practice. In the core medical community, however, researchers often stick to basic methods due to lack of expertise. The International Ovarian Tumor Analysis (IOTA) study group, however, aims to explore advanced mathematical modeling options for ovarian tumor diagnosis through interdisciplinary collaborations involving clinicians, statisticians, and engineers. This resulted in several projects involving Bayesian models to distinguish between benign and adnexal ovarian tumors (binary classification). This chapter describes these projects. Major findings are that the classification of ovarian tumors appears to be a fairly linear problem, that benign and malignant tumors can be predicted with high accuracy, that complex black-box models can be further clarified using rule extraction, that input selection incorporating the cost of the available inputs leads to well-performing models with low total input cost, and that the widely used yet controversial and costly CA-125 tumor marker is not indispensable in mathematical diagnostic models. In conclusion, the interdisciplinary approach adopted by IOTA has resulted in useful clinical and technical insights concerning ovarian tumor diagnosis.