Correlation of Automated Computed Tomography Volumetric Analysis Metrics With Motility Disturbances in Thyroid Eye Disease.

PURPOSE: The authors sought to examine relationships between CT metrics derived via an automated method and clinical parameters of extraocular muscle changes in thyroid eye disease (TED). METHODS: CT images of 204 orbits in the setting of TED were analyzed with an automated segmentation tool developed at the institution. Labels were applied to orbital structures of interest on the study images, which were then registered against a previously established atlas of manually indexed orbits derived from 35 healthy individuals. Point-wise correspondences between study and atlas images were then compared via a fusion algorithm to highlight metrics of interest where TED orbits differed from healthy orbits. RESULTS: Univariate analysis demonstrated several correlations between CT metrics and clinical data. Metrics pertaining to the extraocular muscles-including average diameter, maximum diameter, and muscle volume-were strongly correlated (p < 0.05) with the presence of ocular motility deficits with regards to the superior, inferior, and lateral recti (with exception of superior rectus motility deficits being mildly correlated with muscle volume [p = 0.09]). Motility defects of the medial rectus were strongly correlated with muscle volume, and only weakly correlated with average and maximum muscle diameter. CONCLUSIONS: The novel method of automated imaging metrics may provide objective, rapid clinical information which may have utility in prevention and recognition of visual impairments in TED before they reach an advanced or irreversible stage and while they are able to be improved with immunomodulatory treatments.

Assessment of Orbital Computed Tomography (CT) Imaging Biomarkers in Patients with Thyroid Eye Disease.

To understand potential orbital biomarkers generated from computed tomography (CT) imaging in patients with thyroid eye disease. This is a retrospective cohort study. From a database of an ongoing thyroid eye disease research study at our institution, we identified 85 subjects who had both clinical examination and laboratory records supporting the diagnosis of thyroid eye disease and concurrent imaging prior to any medical or surgical intervention. Patients were excluded if imaging quality or type was not amenable to segmentation. The images of 170 orbits were analyzed with the developed automated segmentation tool. The main outcome measure was to cross 25 CT structural metrics for each eye with nine clinical markers using a Kendall rank correlation test to identify significant relationships. The Kendall rank correlation test between automatically calculated CT metrics and clinical data demonstrated numerous correlations. Extraocular rectus muscle metrics, such as the average diameter of the superior, medial, and lateral rectus muscles, showed a strong correlation (p < 0.05) with loss of visual acuity and presence of ocular motility defects. Hertel measurements demonstrated a strong correlation (p < 0.05) with volumetric measurements of the optic nerve and other orbital metrics such as the crowding index and proptosis. Optic neuropathy was strongly correlated (p < 0.05) with an increase in the maximum diameter of the superior muscle. This novel method of automated imaging metrics may provide objective, rapid clinical information. This data may be useful for appreciation of severity of thyroid eye disease and recognition of risk factors of visual impairment from dysthyroid optic neuropathy from CT imaging.

Phenotype Analysis of Early Risk Factors from Electronic Medical Records Improves Image-Derived Diagnostic Classifiers for Optic Nerve Pathology.

We examine imaging and electronic medical records (EMR) of 588 subjects over five major disease groups that affect optic nerve function. An objective evaluation of the role of imaging and EMR data in diagnosis of these conditions would improve understanding of these diseases and help in early intervention. We developed an automated image-processing pipeline that identifies the orbital structures within the human eyes from computed tomography (CT) scans, calculates structural size, and performs volume measurements. We customized the EMR-based phenome-wide association study (PheWAS) to derive diagnostic EMR phenotypes that occur at least two years prior to the onset of the conditions of interest from a separate cohort of 28,411 ophthalmology patients. We used random forest classifiers to evaluate the predictive power of image-derived markers, EMR phenotypes, and clinical visual assessments in identifying disease cohorts from a control group of 763 patients without optic nerve disease. Image-derived markers showed more predictive power than clinical visual assessments or EMR phenotypes. However, the addition of EMR phenotypes to the imaging markers improves the classification accuracy against controls: the AUC improved from 0.67 to 0.88 for glaucoma, 0.73 to 0.78 for intrinsic optic nerve disease, 0.72 to 0.76 for optic nerve edema, 0.72 to 0.77 for orbital inflammation, and 0.81 to 0.85 for thyroid eye disease. This study illustrates the importance of diagnostic context for interpretation of image-derived markers and the proposed PheWAS technique provides a flexible approach for learning salient features of patient history and incorporating these data into traditional machine learning analyses.

Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections.

The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm(-1)) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255-1266, 1552 cm(-1)). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5% accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.

Robust optic nerve segmentation on clinically acquired computed tomography.

The optic nerve (ON) plays a critical role in many devastating pathological conditions. Segmentation of the ON has the ability to provide understanding of anatomical development and progression of diseases of the ON. Recently, methods have been proposed to segment the ON but progress toward full automation has been limited. We optimize registration and fusion methods for a new multi-atlas framework for automated segmentation of the ONs, eye globes, and muscles on clinically acquired computed tomography (CT) data. Briefly, the multi-atlas approach consists of determining a region of interest within each scan using affine registration, followed by nonrigid registration on reduced field of view atlases, and performing statistical fusion on the results. We evaluate the robustness of the approach by segmenting the ON structure in 501 clinically acquired CT scan volumes obtained from 183 subjects from a thyroid eye disease patient population. A subset of 30 scan volumes was manually labeled to assess accuracy and guide method choice. Of the 18 compared methods, the ANTS Symmetric Normalization registration and nonlocal spatial simultaneous truth and performance level estimation statistical fusion resulted in the best overall performance, resulting in a median Dice similarity coefficient of 0.77, which is comparable with inter-rater (human) reproducibility at 0.73.