Classification of interstitial lung disease patterns with topological texture features

Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.Comment: 8 pages, 5 figures, Proceedings SPIE Medical Imaging 201

Monthly intravenous methylprednisolone in relapsing-remitting multiple sclerosis - reduction of enhancing lesions, T2 lesion volume and plasma prolactin concentrations

BACKGROUND: Intravenous methylprednisolone (IV-MP) is an established treatment for multiple sclerosis (MS) relapses, accompanied by rapid, though transient reduction of gadolinium enhancing (Gd+) lesions on brain MRI. Intermittent IV-MP, alone or with immunomodulators, has been suggested but insufficiently studied as a strategy to prevent relapses. METHODS: In an open, single-cross-over study, nine patients with relapsing-remitting MS (RR-MS) underwent cranial Gd-MRI once monthly for twelve months. From month six on, they received a single i.v.-infusion of 500 mg methylprednisolone (and oral tapering for three days) after the MRI. Primary outcome measure was the mean number of Gd+ lesions during treatment vs. baseline periods; T2 lesion volume and monthly plasma concentrations of cortisol, ACTH and prolactin were secondary outcome measures. Safety was assessed clinically, by routine laboratory and bone mineral density measurements. Soluble immune parameters (sTNF-RI, sTNF-RII, IL1-ra and sVCAM-1) and neuroendocrine tests (ACTH test, combined dexamethasone/CRH test) were additionally analyzed. RESULTS: Comparing treatment to baseline periods, the number of Gd+ lesions/scan was reduced in eight of the nine patients, by a median of 43.8% (p = 0.013, Wilcoxon). In comparison, a pooled dataset of 83 untreated RR-MS patients from several studies, selected by the same clinical and MRI criteria, showed a non-significant decrease by a median of 14% (p = 0.32). T2 lesion volume decreased by 21% during treatment (p = 0.001). Monthly plasma prolactin showed a parallel decline (p = 0.027), with significant cross-correlation with the number of Gd+ lesions. Other hormones and immune system variables were unchanged, as were ACTH test and dexamethasone-CRH test. Treatment was well tolerated; routine laboratory and bone mineral density were unchanged. CONCLUSION: Monthly IV-MP reduces inflammatory activity and T2 lesion volume in RR-MS