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Abstract #2222

Texture and Regression Tree Analysis in the Characterisation of Ovarian Lesions

Peter Gibbs 1 , Martine Dujardin 1 , and Lindsay Turnbull 1

1 MRI Centre, HYMS at University of Hull, Hull, East Yorkshire, United Kingdom

MRI is the preferred technique for characterising complex adnexal masses. However, the presence of solid components in both benign and malignant lesions causes diagnostic difficulties. In this work the utility of co-occurrence matrix based textural analysis in the diagnosis of ovarian malignancy is explored. Significant differences between four groups (ovarian cancer, borderline ovarian tumour, cystadenoma and cystadenofibroma) were found for 8 of 16 calculated texture parameters. Regression tree analysis yielded a robust diagnostic model, based on 3 texture parameters, with an overall accuracy of 70%.

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