Dataset Information

Characterization and classification of lupus patients based on plasma thermograms.

ABSTRACT:

Objective

Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria.

Methods

Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers.

Results

Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia.

Conclusion

Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting.

SUBMITTER: Garbett NC

PROVIDER: S-EPMC5693473 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Characterization and classification of lupus patients based on plasma thermograms.

Garbett Nichola C NC Brock Guy N GN Chaires Jonathan B JB Mekmaysy Chongkham S CS DeLeeuw Lynn L Sivils Kathy L KL Harley John B JB Rovin Brad H BH Kulasekera K B KB Jarjour Wael N WN

PloS one 20171117 11

<h4>Objective</h4>Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent heal ...[more]

PMID: 29149219

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Project description:Breast cancer is the second most frequent cancer worldwide, following lung cancer and the fifth leading cause of cancer death and a major cause of cancer death among women. In recent years, convolutional neural networks (CNNs) have been successfully applied for the diagnosis of breast cancer using different imaging modalities. Pooling is a main data processing step in CNN that decreases the feature maps' dimensionality without losing major patterns. However, the effect of pooling layer was not studied efficiently in literature. In this paper, we propose a novel design for the pooling layer called vector pooling block (VPB) for the CCN algorithm. The proposed VPB consists of two data pathways, which focus on extracting features along horizontal and vertical orientations. The VPB makes the CNNs able to collect both global and local features by including long and narrow pooling kernels, which is different from the traditional pooling layer, that gathers features from a fixed square kernel. Based on the novel VPB, we proposed a new pooling module called AVG-MAX VPB. It can collect informative features by using two types of pooling techniques, maximum and average pooling. The VPB and the AVG-MAX VPB are plugged into the backbone CNNs networks, such as U-Net, AlexNet, ResNet18 and GoogleNet, to show the advantages in segmentation and classification tasks associated with breast cancer diagnosis from thermograms. The proposed pooling layer was evaluated using a benchmark thermogram database (DMR-IR) and its results compared with U-Net results which was used as base results. The U-Net results were as follows: global accuracy = 96.6%, mean accuracy = 96.5%, mean IoU = 92.07%, and mean BF score = 78.34%. The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.

Project description:ObjectiveIncomplete lupus erythematosus (ILE) involves clinical and/or serologic manifestations consistent with but insufficient for systemic lupus erythematosus (SLE) classification. Because the nature of ILE is poorly understood and no treatment recommendations exist, we examined the clinical manifestations, medication history, and immunologic features in a diverse collection of ILE and SLE patients.MethodsMedical records of subjects enrolled in the Lupus Family Registry and Repository were reviewed for medication history and American College of Rheumatology (ACR) classification criteria to identify ILE patients (3 ACR criteria; n = 440) and SLE patients (≥4 ACR criteria; n = 3,397). Participants completed the Connective Tissue Disease Screening Questionnaire. Anticardiolipin and plasma B lymphocyte stimulator (BLyS) were measured by enzyme-linked immunosorbent assay, antinuclear antibodies (ANAs) by indirect immunofluorescence, and 13 autoantibodies by bead-based assays.ResultsOn average, ILE patients were older than SLE patients (46.2 years versus 42.0 years; P < 0.0001), and fewer ILE patients were African American (23.9% versus 32.2%; P < 0.001). ILE patients exhibited fewer autoantibody specificities than SLE patients (1.3 versus 2.6; P < 0.0001) and were less likely to have ANA titers ≥1:1,080 (10.5% versus 19.5%; P < 0.0001). BLyS levels were intermediate in ILE patients (controls < ILE; P = 0.016; ILE < SLE; P = 0.008). Pericarditis, renal, or neurologic manifestations occurred in 12.5% of ILE patients and were associated with non-European American race/ethnicity (P = 0.012). Hydroxychloroquine use increased over time, but was less frequent in ILE than SLE patients (65.2% versus 83.1%; P < 0.0001).ConclusionAlthough usually characterized by milder symptoms, ILE manifestations may require immunomodulatory treatments. Longitudinal studies are necessary to understand how ILE affects organ damage and future SLE risk, and to delineate molecular pathways unique to ILE.

Dataset Information

Characterization and classification of lupus patients based on plasma thermograms.

Objective

Methods

Results

Conclusion

Publications

Characterization and classification of lupus patients based on plasma thermograms.

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