Alexandra Kazantzi

Experience with rituximab in scleroderma: results from a 1-year, proof-of-principle study

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.

Texture-Based Identification and Characterization of Interstitial Pneumonia Patterns in Lung Multidetector CT

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.

Texture classification‐based segmentation of lung affected by interstitial pneumonia in high‐resolution CT

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 students 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 students 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.

Is There a Role for B-cell Depletion as Therapy for Scleroderma? A Case Report and Review of the Literature

OBJECTIVES Rituximab (RTX) has been successfully used in the treatment of several rheumatic diseases with an acceptable safety profile. We present herein a patient with systemic sclerosis (SSc) who exhibited significant improvement of his lung function and skin fibrosis following RTX administration, and review the literature regarding the role of B-cells in SSc and the potential efficacy of RTX in its treatment. METHODS We performed an internet search using the keywords systemic sclerosis, scleroderma, rituximab, B-cells, fibrosis, interstitial lung disease (ILD), and therapy. RESULTS Our patient, a 40-year old man with severe SSc-associated ILD, received 4 courses of RTX. The patients lung function improved; forced vital capacity and diffusing capacity of carbon monoxide reached values of 35% and 33%, respectively, compared with 30% and 14% of pretreatment values. Skin thickening assessed clinically and histologically improved as well. Several lines of evidence suggest that B-cells may have a pathogenic role in SSc. B-cells from tight skin mice--an animal model of SSc--exhibit chronic hyperactivity; likewise, B-cells from patients with SSc overexpress CD19 and are chronically activated. Furthermore, studies have revealed that B-cell genes were specifically transcribed in SSc skin and that B-cell infiltration was a prominent feature of SSc-associated ILD. The potential clinical efficacy of RTX in SSc has been explored in a limited number of patients with encouraging results. Preliminary data suggest that RTX may favorably affect skin as well as lung disease in SSc. CONCLUSIONS Several basic research data underscore the potential pathogenic role of B-cells in SSc and clinical evidence suggests that RTX might be a therapeutic option in SSc. Large-scale multicenter studies are needed to evaluate the potential clinical efficacy of RTX in SSc.

Molecular cloning of the β subunit of complement component eight of rainbow trout

Complement-mediated killing of pathogens through the lytic pathway is an important effector mechanism of the innate immune response. C8 is one of the components of the lytic pathway and is composed of an alpha, beta, and gamma subunit. In the present study we report the cloning and characterization of the primary structure of the C8beta subunit in the rainbow trout (Oncorhynchus mykiss). The deduced amino acid sequence of trout C8beta shows 72 and 47% identity with that of Japanese flounder and human, respectively. It also contains many of the same structural motifs as those found in mammalian lytic components. The C8beta gene appears to exists as a single copy in the trout genome and is expressed primarily in the liver. The protein encoded by the gene was identified by Western blotting using an anti-peptide antibody and was approximately 65kDa.

Vessel Tree Segmentation in Presence of Interstitial Lung Disease in MDCT

The automated segmentation of vessel tree structures is a crucial preprocessing stage in computer aided diagnosis (CAD) schemes of interstitial lung disease (ILD) patterns in multidetector computed tomography (MDCT). The accuracy of such preprocessing stages is expected to influence the accuracy of lung CAD schemes. Although algorithms aimed at improving the accuracy of lung fields segmentation in presence of ILD have been reported, the corresponding vessel tree segmentation stage is under-researched. Furthermore, previously reported vessel tree segmentation methods have only dealt with normal lung parenchyma. In this paper, an automated vessel tree segmentation scheme is proposed, adapted to the presence of pathologies affecting lung parenchyma. The first stage of the method accounts for a recently proposed method utilizing a 3-D multiscale vessel enhancement filter based on eigenvalue analysis of the Hessian matrix and on unsupervised segmentation. The second stage of the method is a texture-based voxel classification refinement to correct possible over-segmentation. The performance of the proposed scheme, and of the previously reported technique, in vessel tree segmentation was evaluated by means of area overlap (previously reported: 0.715 ± 0.082, proposed: 0.931 ± 0.027), true positive fraction (previously reported: 0.968 ± 0.019, proposed: 0.935 ± 0.036) and false positive fraction (previously reported: 0.400 ± 0.181, proposed: 0.074 ± 0.031) on a dataset of 210 axial slices originating from seven ILD affected patient scans (used for performance evaluation out of 15). The pro posed method demonstrated a statistically significantly (p <; 0.05) higher performance as compared to the previously reported vessel tree segmentation technique. The impact of suboptimal vessel tree segmentation in a reticular pattern quantification system is also demonstrated.

Towards quantification of interstitial pneumonia patterns in lung multidetector CT

Quantification of Diffuse Parenchyma Lung Disease (DPLD) patterns challenges computer aided diagnosis schemes in Computed Tomography (CT) lung analysis. In this study, an automated scheme for volumetric quantification of Interstitial Pneumonia (IP) patterns, a subset of DPLDs, is presented, utilizing a MultiDetector CT (MDCT) data set. Initially, Lung Field (LF) segmentation is achieved by 3D automated gray level thresholding combined to wavelet highlighting, followed by a texture based border refinement step. The vessel tree volume is identified and removed from LF, resulting in Lung Parenchyma (LP) volume. Following, the abnormal LP is differentiated from normal LP utilizing a 2 class k-means clustering. Quantification of IP patterns is formulated as a three-class pattern recognition problem to classify abnormal LP into ground glass, reticular and honeycomb patterns, by means of SVM voxel classification, exploiting 3D co-occurrence features. Performance of the proposed scheme in segmenting LF, as well as in quantifying normal LP, ground glass, reticular and honeycomb patterns was evaluated by means of volume overlap on 5 MDCT scans. Volume overlap for left LF and right LF was 0.95 plusmn 0.03 and 0.96 plusmn 0.02 respectively, while for normal LP, ground glass, reticular and honeycombing patterns was 0.89 plusmn 0.02, 0.70 plusmn 0.04, 0.72 plusmn 0.05 and 0.71 plusmn 0.03, respectively.

Selecting registration schemes in case of interstitial lung disease follow-up in CT

PURPOSE Primary goal of this study is to select optimal registration schemes in the framework of interstitial lung disease (ILD) follow-up analysis in CT. METHODS A set of 128 multiresolution schemes composed of multiresolution nonrigid and combinations of rigid and nonrigid registration schemes are evaluated, utilizing ten artificially warped ILD follow-up volumes, originating from ten clinical volumetric CT scans of ILD affected patients, to select candidate optimal schemes. Specifically, all combinations of four transformation models (three rigid: rigid, similarity, affine and one nonrigid: third order B-spline), four cost functions (sum-of-square distances, normalized correlation coefficient, mutual information, and normalized mutual information), four gradient descent optimizers (standard, regular step, adaptive stochastic, and finite difference), and two types of pyramids (recursive and Gaussian-smoothing) were considered. The selection process involves two stages. The first stage involves identification of schemes with deformation field singularities, according to the determinant of the Jacobian matrix. In the second stage, evaluation methodology is based on distance between corresponding landmark points in both normal lung parenchyma (NLP) and ILD affected regions. Statistical analysis was performed in order to select near optimal registration schemes per evaluation metric. Performance of the candidate registration schemes was verified on a case sample of ten clinical follow-up CT scans to obtain the selected registration schemes. RESULTS By considering near optimal schemes common to all ranking lists, 16 out of 128 registration schemes were initially selected. These schemes obtained submillimeter registration accuracies in terms of average distance errors 0.18 ± 0.01 mm for NLP and 0.20 ± 0.01 mm for ILD, in case of artificially generated follow-up data. Registration accuracy in terms of average distance error in clinical follow-up data was in the range of 1.985-2.156 mm and 1.966-2.234 mm, for NLP and ILD affected regions, respectively, excluding schemes with statistically significant lower performance (Wilcoxon signed-ranks test, p < 0.05), resulting in 13 finally selected registration schemes. CONCLUSIONS Selected registration schemes in case of ILD CT follow-up analysis indicate the significance of adaptive stochastic gradient descent optimizer, as well as the importance of combined rigid and nonrigid schemes providing high accuracy and time efficiency. The selected optimal deformable registration schemes are equivalent in terms of their accuracy and thus compatible in terms of their clinical outcome.

Quantitative Visually Lossless Compression Ratio Determination of JPEG2000 in Digitized Mammograms

The current study presents a quantitative approach towards visually lossless compression ratio (CR) threshold determination of JPEG2000 in digitized mammograms. This is achieved by identifying quantitative image quality metrics that reflect radiologists’ visual perception in distinguishing between original and wavelet-compressed mammographic regions of interest containing microcalcification clusters (MCs) and normal parenchyma, originating from 68 images from the Digital Database for Screening Mammography. Specifically, image quality of wavelet-compressed mammograms (CRs, 10:1, 25:1, 40:1, 70:1, 100:1) is evaluated quantitatively by means of eight image quality metrics of different computational principles and qualitatively by three radiologists employing a five-point rating scale. The accuracy of the objective metrics is investigated in terms of (1) their correlation (r) with qualitative assessment and (2) ROC analysis (Az index), employing pooled radiologists’ rating scores as ground truth. The quantitative metrics mean square error, mean absolute error, peak signal-to-noise ratio, and structural similarity demonstrated strong correlation with pooled radiologists’ ratings (r, 0.825, 0.823, −0.825, and −0.826, respectively) and the highest area under ROC curve (Az, 0.922, 0.920, 0.922, and 0.922, respectively). For each quantitative metric, the highest accuracy values of corresponding ROC curves were used to define metric cut-off values. The metrics cut-off values were subsequently used to suggest a visually lossless CR threshold, estimated to be between 25:1 and 40:1 for the dataset analyzed. Results indicate the potential of the quantitative metrics approach in predicting visually lossless CRs in case of MCs in mammography.

Optimizing lung volume segmentation by texture classification

Accurate and automated Lung Field (LF) segmentation in volumetric computed tomography protocols 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 three-dimensional LF segmentation algorithm adapted to interstitial lung disease patterns (ILD) 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 3D texture features. The proposed method is evaluated on a dataset of 10 cases spanning a range of ILD patterns such as ground glass, reticular, and honeycombing. The accuracy of the method is assessed using area overlap and shape differentiation metrics (dmean, drms, and dmax), by comparing automatically derived lung borders to manually traced ones by a radiologist, and further compared to a Gray Level Thresholding-based (GLT-based) method. The proposed method demonstrated the highest segmentation accuracy, overlap=0.942, dmean=1.835 mm, drms=1.672 mm, and dmax =4.255 mm, which is statistically significant (two-tailed students t test for paired data, p<0.0001) with respect to all metrics considered as compared to the the GLT-based method overlap=0.836, dmean=2.324 mm, drms=3.890 mm,and dmax=2.946 mm. The proposed segmentation method could be used as an initial stage of a CAD scheme for ILD patterns.