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Molecular diagnosis of orbital inflammatory disease.

ABSTRACT: Orbital inflammatory diseases include thyroid eye disease (TED), granulomatosis with polyangiitis (GPA), sarcoidosis, and nonspecific orbital inflammation (NSOI). Histopathological diagnosis usually relies on the clinical context and is not always definitive. Gene expression profiling provides diagnostic and therapeutic information in several malignancies, but its role in evaluating nonmalignant disease is relatively untested. We hypothesized that gene expression profiling could provide diagnostic information for NSOI. We collected formalin-fixed, paraffin-embedded orbital biopsies from 10 institutions and 83 subjects including 25 with thyroid eye disease, 25 nonspecific orbital inflammation, 20 healthy controls, 6 with granulomatosis with polyangiitis, and 7 with sarcoidosis. Tissues were divided into discovery and validation sets. Gene expression was quantified using Affymetrix U133 Plus 2.0 microarrays. A random forest statistical algorithm based on data from 39 probe sets identified controls, GPA, or TED with an average accuracy of 76% (p=0.02). Random forest analysis indicated that 52% of tissues from patients with nonspecific inflammation were consistent with a diagnosis of GPA. Molecular diagnosis by gene expression profiling will augment clinical data and histopathology in differentiating forms of orbital inflammatory disease.

SUBMITTER: Rosenbaum JT

PROVIDER: S-EPMC4388776 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Molecular diagnosis of orbital inflammatory disease.

Rosenbaum James T JT Choi Dongseok D Wilson David J DJ Grossniklaus Hans E HE Sibley Cailin H CH Harrington Christina A CA Planck Stephen R SR

Experimental and molecular pathology 20150114 2

Orbital inflammatory diseases include thyroid eye disease (TED), granulomatosis with polyangiitis (GPA), sarcoidosis, and nonspecific orbital inflammation (NSOI). Histopathological diagnosis usually relies on the clinical context and is not always definitive. Gene expression profiling provides diagnostic and therapeutic information in several malignancies, but its role in evaluating nonmalignant disease is relatively untested. We hypothesized that gene expression profiling could provide diagnost ...[more]

PMID: 25595914

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Project description:PurposeOrbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves' orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO.Materials and methodsGO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM).ResultsThe GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p < 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82.ConclusionThe proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.

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Molecular diagnosis of orbital inflammatory disease.

Publications

Molecular diagnosis of orbital inflammatory disease.

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