PMID: 32521931Jun 12, 2020Paper

The utility of texture-based classification of different types of ascites on magnetic resonance

Journal of B.U.ON. : Official Journal of the Balkan Union of Oncology
Paul-Andrei ȘtefanCarmen Mihaela Mihu

Abstract

To quantify specific characteristics of different types of ascitic fluid on magnetic resonance (MR) images and to determine their utility for computer-assisted lesion classification. The MR images of 48 patients with intra-abdominal fluid were retrospectively analyzed. Patients were grouped according to the underlying disease and pathological outcomes. The fluid texture was analyzed on Breath Hold Axial T2 FatSat FIESTA sequence, using MaZda software. Most discriminative texture features for the classification of different types of ascites were selected based on Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC). Computer-assisted classification based on k-nearest-neighbor (k-NN) and artificial neural network (ANN) was performed and then accuracy, sensitivity and specificity were calculated. Adequate discriminative power for differentiating benign ascites from malignant ascites was achieved for two textural features, namely the Run Length Nonuniformity computed from both vertical and horizontal directions with 91.84% accuracy (sensitivity 100%; specificity 42.86%), and ten features for differentiating bland from hemorrhagic fluid with 90.00% accuracy (sensitivity 9...Continue Reading

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