Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model.

INTRODUCTION: Treatment of interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF) often includes systemic corticosteroids. Use of steroid-sparing agents is amenable to avoid potential side effects. METHODS: Functional indices and high-resolution computed tomography (HRCT) patterns of patients with non-IPF ILDs receiving mycophenolate mofetil (MMF) with a minimum follow-up of 1 year were analyzed. Two independent radiologists and a machine learning software system (Imbio 1.4.2.) evaluated HRCT patterns. RESULTS: Fifty-five (n = 55) patients were included in the analysis (male: 30 [55%], median age: 65.0 [95% CI: 59.7-70.0], mean forced vital capacity %predicted [FVC %pred.] ± standard deviation [SD]: 69.4 ± 18.3, mean diffusing capacity of lung for carbon monoxide %pred. ± SD: 40.8 ± 14.3, hypersensitivity pneumonitis: 26, connective tissue disease-ILDs [CTD-ILDs]: 22, other ILDs: 7). There was no significant difference in mean FVC %pred. post-6 months (1.59 ± 2.04) and 1 year (-0.39 ± 2.49) of treatment compared to baseline. Radiographic evaluation showed no significant difference between baseline and post-1 year %ground glass opacities (20.0 [95% CI: 14.4-30.0] vs. 20.0 [95% CI: 14.4-25.6]) and %reticulation (5.0 [95% CI: 2.0-15.6] vs. 7.5 [95% CI: 2.0-17.5]). A similar performance between expert radiologists and Imbio software analysis was observed in assessing ground glass opacities (intraclass correlation coefficient [ICC] = 0.73) and reticulation (ICC = 0.88). Fourteen patients (25.5%) reported at least one side effect and 8 patients (14.5%) switched to antifibrotics due to disease progression. CONCLUSION: Our data suggest that MMF is a safe and effective steroid-sparing agent leading to disease stabilization in a proportion of patients with non-IPF ILDs. Machine learning software systems may exhibit similar performance to specialist radiologists and represent fruitful diagnostic and prognostic tools.

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.

Automated 3D ιnterstitial lung disease εxtent quantification: performance evaluation and correlation to PFTs.

In this study, the performance of a recently proposed computer-aided diagnosis (CAD) scheme in detection and 3D quantification of reticular and ground glass pattern extent in chest computed tomography of interstitial lung disease (ILD) patients is evaluated. CAD scheme performance was evaluated on a dataset of 37 volumetric chest scans, considering five representative axial anatomical levels per scan. CAD scheme reliability analysis was performed by estimating agreement (intraclass correlation coefficient, ICC) of automatically derived ILD pattern extent to semi-quantitative disease extent assessment in terms of 29-point rating scale provided by two expert radiologists. Receiver operating characteristic (ROC) analysis was employed to assess CAD scheme accuracy in ILD pattern detection in terms of area under ROC curve (A z ). Correlation of reticular and ground glass volumetric pattern extent to pulmonary function tests (PFTs) was also investigated. CAD scheme reliability was substantial for ILD extent (ICC = 0.809) and distinct reticular pattern extent (0.806) and moderate for distinct ground glass pattern extent (0.543), performing within inter-observer agreement. CAD scheme demonstrated high accuracy in detecting total ILD (A z = 0.950 ± 0.018), while accuracy in detecting distinct reticular and ground glass patterns was 0.920 ± 0.023 and 0.883 ± 0.024, respectively. Moderate and statistically significant negative correlation was found between reticular volumetric pattern extent and diffusing capacity, forced expiratory volume in 1 s, forced vital capacity, and total lung capacity (R = -0.581, -0.513, -0.494, and -0.446, respectively), similar to correlations found between radiologists' semi-quantitative ratings with PFTs. CAD-based quantification of disease extent is in agreement with radiologists' semi-quantitative assessment and correlates to specific PFTs, suggesting a potential imaging biomarker for ILD staging and management.

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 (A z 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 (A z , 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.

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 proposed 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.

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.

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 patient's 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.

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/m(2))] 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 (DL(CO)) 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 DL(CO) 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 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 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.

Molecular cloning of leukocyte cell-derived chemotaxin 2 in rainbow trout.

In humans, leukocyte cell-derived chemotaxin 2 (LECT2) is a 16kDa chemotactic protein that consists of 133 amino acids and three intramolecular disulphide bonds. Although it was originally demonstrated to have a chemotactic function in vitro, recent data sustain a further multifunctional role of LECT2 that extends from cell growth, differentiation, damage/repair process and carcinogenesis to autoimmune diseases. The in vivo function of LECT2 protein still remains obscure. In order to study the phylogeny of LECT2, a full-length cDNA clone of LECT2 gene, 720 bp in size, was isolated in rainbow trout (Oncorhynchus mykiss). Its deduced amino acid sequence of 156 residues, presents 40, 45 and 61% overall identity to human, mouse and carp LECT2 proteins, respectively. In contrast to mammalian LECT2 protein, trout LECT2 protein reveals two potential N-glycosylation sites. Phylogenetic analysis shows that trout LECT2 is clustered with the known homologous proteins. Trout LECT2 mRNA is predominately expressed in liver and spleen, showing lower expression in kidney, intestine, heart and brain.

Molecular cloning of the beta 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.