Differentiation of transitional zone prostate cancer from benign hyperplasia nodules: evaluation of discriminant criteria at multiparametric MRI.

AIM: To assess topographical and magnetic resonance imaging (MRI) features in characterizing prostate transitional zone (TZ) nodules. MATERIALS AND METHODS: Two radiologists evaluated all TZ nodules visible at multiparametric MRI in 52 consecutive patients who underwent radical prostatectomy. The radiologists assessed topographical (anteroposterior and superior-inferior location, crossing of the sagittal midline) and T2-weighted (shape, presence and distinctness of capsule, distinctness of contours, presence of cysts) features, the apparent diffusion coefficient (ADC), and eight semi-quantitative and quantitative enhancement parameters derived from dynamic contrast-enhanced (DCE) imaging. The nature of the nodules was assessed using prostatectomy specimens. Five statistical methods taking into account multiple testing were used. RESULTS: One hundred and thirty-seven nodules (117 benign, 20 malignant) were evaluated. Mean ADC, all topographical, and all T2-weighted features were significant predictors of malignancy according to at least four out of the five statistical methods. Particularly, 20/20 and 18/20 cancers involved the anterior and apical third of the TZ, respectively. None of the enhancement parameters was significantly different between cancers and benign nodules. By assessing the presence of cysts, the nodules' capsule, and their anteroposterior and superior-inferior location, 111/117 benign nodules were correctly diagnosed, without misclassifying any cancer. CONCLUSION: Topographical, T2-weighted, and diffusion-weighted features can be used to characterize TZ nodules. DCE imaging does not seem to provide additional information.

Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information.

We have designed a computer-aided diagnosis system to discriminate between hypermetabolic cancer lesions and hypermetabolic inflammatory or physiological but noncancerous processes in FDG PET/CT exams of lymphoma patients. Detection performance of the support vector machine (SVM) classifier was assessed based on feature sets including 105 positron emission tomography (PET) and Computed tomography (CT) characteristics derived from the clinical practice and from more sophisticated texture analysis. An original feature selection method based on combining different filter methods was proposed. The evaluation database consisted of 156 lymphomatous and 32 suspicious but nonlymphomatous regions of interest. Different types of training databases including either the PET and CT features or the PET features only, with or without feature selection, were evaluated to assess the added value of multimodality and texture information on classification performance. An optimization study was conducted for each classifier separately to select the best combination of parameters. Promising classification performance was achieved by the SVM classifier combined with the 12 most discriminant PET and CT features with a value of the area under the receiver operating curve of 0.91.