Computer-aided diagnosis for prostate cancer detection in the peripheral zone via multisequence MRI

Emilie Niaf; Olivier Rouvière; Carole Lartizien

doi:10.1117/12.877231

9 March 2011 Computer-aided diagnosis for prostate cancer detection in the peripheral zone via multisequence MRI

Emilie Niaf, Olivier Rouvière, Carole Lartizien

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79633P (2011) https://doi.org/10.1117/12.877231
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

We propose a Computer Assisted Diagnosis Interview (CADi) scheme for determining a likelihood measure of prostate cancer presence in the peripheral zone (PZ) based on multisequence magnetic resonance imaging, including T2-weighted (T2w), diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) MRI at 1.5 Tesla (T). Based on a feature set derived from the gray level images, including first order statistics, Haralick's features, gradient features, semi-quantitative and quantitative (pharmacokinetic modeling) dynamic parameters, we trained and compared four kinds of classifiers: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), k-Nearest Neighbours (KNN) and Naïve Bayes (NB). The aim is twofold: we try to discriminate between the relevant features as well as creating an efficient classifier using these features. The database consists of 23 radical prostatectomy patients. Using histologic sections as the gold standard, both cancers and non-malignant tissues (suspicious and clearly benign) were annotated in consensus on all MR images by two radiologists, a histopathologist and a researcher. Diagnostic performances were evaluated based on a ROC curves analysis. From the outputs of all evaluated feature selection methods on the test bench, we discriminated a restrictive set of about 20 highly informative features. Quantitative evaluation of the diagnostic performance yielded to a maximal Area Under the ROC Curve (AUC) of 0.89. Moreover, the optimal CADi scheme outperformed, in terms of specificity, our human experts in differentiating malignant from suspicious tissues, thus demonstrating its potential for assisting cancer identification in the PZ.

Citation Download Citation

Emilie Niaf, Olivier Rouvière, and Carole Lartizien "Computer-aided diagnosis for prostate cancer detection in the peripheral zone via multisequence MRI", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633P (9 March 2011); https://doi.org/10.1117/12.877231

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