Objective. To assess the efficacy of rituximab (RTX) in SSc.Methods. Fourteen patients with SSc were evaluated. Eight patients were randomized to receive two cycles of RTX at baseline and 24 weeks [each cycle consisted of four weekly RTX infusions (375 mg/m2)] in addition to standard treatment, whereas six patients (control group) received standard treatment alone. Lung involvement was assessed by pulmonary function tests (PFTs) and chest high-resolution CT (HRCT). Skin involvement was assessed both clinically and histologically.Results. There was a significant increase of forced vital capacity (FVC) in the RTX group compared with baseline (mean ± s.d.: 68.13 ± 19.69 vs 75.63 ± 19.73, at baseline vs 1-year, respectively, P = 0.0018). The median percentage of improvement of FVC in the RTX group was 10.25%, whereas that of deterioration in the controls was 5.04% (P = 0.002). Similarly, diffusing capacity of carbon monoxide (DLCO) increased significantly in the RTX group compared with baseline (mean ± s.d.: 52.25 ± 20.71 vs 62 ± 23.21, at baseline vs 1-year respectively, P = 0.017). The median percentage of improvement of DLCO in the RTX group was 19.46%, whereas that of deterioration in the control group was 7.5% (P = 0.023). Skin thickening, assessed with the Modified Rodnan Skin Score (MRSS), improved significantly in the RTX group compared with the baseline score (mean ± s.d.: 13.5 ± 6.84 vs 8.37 ± 6.45 at baseline vs 1-year, respectively, P < 0.001).Conclusion. Our results indicate that RTX may improve lung function in patients with SSc. To confirm our encouraging results we propose that larger scale, multicentre studies with longer evaluation periods are needed.

Identification and characterization of diffuse parenchyma lung disease (DPLD) patterns challenges computer-aided schemes in computed tomography (CT) lung analysis. In this study, an automated scheme for volumetric quantification of interstitial pneumonia (IP) patterns, a subset of DPLD, is presented, utilizing a multidetector CT (MDCT) dataset. Initially, lung-field segmentation is achieved by 3-D automated gray-level thresholding combined with an edge-highlighting wavelet preprocessing step, followed by a texture-based border refinement step. The vessel tree volume is identified and removed from lung field, resulting in lung parenchyma (LP) volume. Following, identification and characterization of IP patterns is formulated as a three-class pattern classification of LP into normal, ground glass, and reticular patterns, by means of k-nearest neighbor voxel classification, exploiting 3-D cooccurrence features. Performance of the proposed scheme in indentifying and characterizing ground glass and reticular patterns was evaluated by means of volume overlap (ground glass: 0.734 +/- 0.057, reticular: 0.815 +/- 0.037), true-positive fraction (ground glass: 0.638 +/- 0.055, reticular: 0.942 +/- 0.023) and false-positive fraction (ground glass: 0.361 +/- 0.027, reticular: 0.147 +/- 0.032) on five MDCT scans.

Accurate and automated lung field (LF) segmentation in high-resolution computed tomography (HRCT) is highly challenged by the presence of pathologies affecting lung borders, also affecting the performance of computer-aided diagnosis (CAD) schemes. In this work, a two-dimensional LF segmentation algorithm adapted to interstitial pneumonia (IP) patterns is presented. The algorithm employs k-means clustering followed by a filling operation to obtain an initial LF order estimate. The final LF border is obtained by an iterative support vector machine neighborhood labeling of border pixels based on gray level and wavelet coefficient statistics features. A second feature set based on gray level averaging and gradient features was also investigated to evaluate its effect on segmentation performance of the proposed method. The proposed method is evaluated on a dataset of 22 HRCT cases spanning a range of IP patterns such as ground glass, reticular, and honeycombing. The accuracy of the method is assessed using area overlap and shape differentiation metrics (d(mean), d(rms), and d(max)), by comparing automatically derived lung borders to manually traced ones, and further compared to a gray level thresholding-based (GLT-based) method. Accuracy of the methods evaluated is also compared to interobserver variability. The proposed method incorporating gray level and wavelet coefficient statistics demonstrated the highest segmentation accuracy, averaged over left and right LFs (overlap=0.954, d(mean)=1.080 mm, d(rms)=1.407 mm, and d(max)=4.944 mm), which is statistically significant (two-tailed student's t test for paired data, p<0.0083) with respect to all metrics considered as compared to the proposed method incorporating gray level averaging and gradient features (overlap=0.918, d(mean)=2.354 mm, d(rms)=3.711 mm, and d(max)=14.412 mm) and the GLT-based method (overlap=0.897, d(mean)=3.618 mm, d(rms)=5.007 mm, and d(max)=16.893 mm). The performance of the three segmentation methods, although decreased as IP pattern severity level (mild, moderate, and severe) was increased, did not demonstrate statistically significant difference (two-tailed student's t test for unpaired data, p>0.0167 for all metrics considered). Finally, the accuracy of the proposed method, based on gray level and wavelet coefficient statistics ranges within interobserver variability. The proposed segmentation method could be used as an initial stage of a CAD scheme for IP patterns.

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