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Clinical Sciences |

Clinical and Soft-Tissue Computed Tomographic Predictors of Dysthyroid Optic Neuropathy:  Refinement of the Constellation of Findings at Presentation FREE

Ezekiel Weis, MD, MPH; Manraj K. S. Heran, MD; Ashu Jhamb, MD; Andy K. Chan, MD; Jack P. Chiu, MD; Michael C. Hurley, MD; Jack Rootman, MD
[+] Author Affiliations

Author Affiliations: Department of Ophthalmology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada (Dr Weis); Departments of Radiology (Drs Heran, Chan, Chiu, and Hurley) and Ophthalmology and Visual Science (Dr Rootman), Vancouver General Hospital and The University of British Columbia, Vancouver, British, Columbia, Canada; and Department of Radiology, Royal Brisbane Hospital, Brisbane, Queensland, Australia (Dr Jhamb).


Arch Ophthalmol. 2011;129(10):1332-1336. doi:10.1001/archophthalmol.2011.276.
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Published online

Objective To evaluate the ability to predict the presence of dysthyroid optic neuropathy (DON) using computed tomography assessment of soft-tissue and clinical features.

Study Design A retrospective consecutive case series of patients with thyroid-related orbitopathy.

Results One hundred eighty-nine orbits from 99 patients were evaluated. Statistically significant clinical predictors of DON on univariate analysis included a difference in intraocular pressure from primary gaze to upgaze (P = .02), the presence of lagophthalmos (P = .04), and inflammation as measured by the VISA (vision, inflammation, strabismus, appearance/exposure) inflammatory scale (P = .004). Dysthyroid optic neuropathy was inversely related to the marginal reflex distance (P = .01), levator function (P = .02), total ductions (P = .003), and interpalpebral fissure (P = .04). Statistically significant radiologic predictors determined on univariate analysis included apical crowding (P < .001), presence of enlarged tendons (P = .004), increasing total rectus diameter (P = .02), and presence of small, low densities within the recti muscles (P = .04). Multivariate analysis found only total ductions (P = .02) and marginal reflex distance (P = .04) determined on clinical examination and apical crowding shown on computed tomography (P = .003) to be significantly associated with DON. Receiver operating characteristic curves were used to evaluate the ability of the clinical and radiologic assessment, as well as the combination of these assessments, to predict DON. All 3 models were strong predictors of DON, with no statistically significant differences in the area under the receiver operating characteristic curve among them (P = .14).

Conclusions Total ductions, marginal reflex distance, and apical crowding observed on computed tomography scans are able to predict the presence of DON with high sensitivity, specificity, positive predictive value, and negative predictive value. Eyelid ptosis is a novel predictor of DON.

Figures in this Article

Dysthyroid optic neuropathy (DON) is a serious complication of thyroid-related orbitopathy, occurring in approximately 5% of cases.1 Without timely diagnosis and intervention, vision loss can occur, making accurate and prompt recognition and management essential for a good outcome.

The most commonly accepted cause of optic neuropathy is compression of the optic nerve2 or of its blood supply3 resulting from an increase in the volume of orbital soft tissues at the orbital apex. Increased retrobulbar pressure4 and optic nerve stretch have been hypothesized to be causes in a small percentage of cases.5

Thyroid-related orbitopathy can often have equivocal direct optic nerve function testing, which makes the distinction between probable and definite DON difficult.6 Similarly, some patients are assessed early in the disease, before DON has developed. Consequently, the development of a well-defined constellation of clinical and radiologic findings might aid the physician in diagnosing DON and devising a safe follow-up schedule for patients at risk for developing DON.

Previous studies have evaluated the clinical examination7,8 and radiologic2,7,916 findings associated with DON. The present study builds on this primarily correlative knowledge base by evaluating these associations with clinically applicable analyses, including sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). Novel, theoretically important predictors are also evaluated in an attempt to clarify the constellation of findings on diagnosis of DON.

In a retrospective consecutive case series design, patients with thyroid orbitopathy referred to a single orbital practice (J.R.) were evaluated. Participants were required to have an orbital computed tomography (CT) scan with true axial and either true or coronal reconstructs. Patients were evaluated clinically, using the VISA (vision, inflammation, strabismus, appearance/exposure) scoring system, on their initial visit.17 The version of the VISA inflammatory scale, one of the 4 components of the VISA scoring system, is based on the previously validated18 clinical activity score. The VISA inflammatory scale is given in the following tabulation:

A diagnosis of DON was made when a decrease in visual function was believed to be caused by optic neuropathy secondary to thyroid-related orbitopathy. Visual acuity, relative afferent papillary defect, and color vision were assessed in all patients. Visual field testing and visual evoked potential were selectively used in patients with equivocal basic findings. Patients with other causes of visual dysfunction that could explain the vision loss, including prior ocular disease and corneal changes, were excluded. Other factors that were evaluated through clinical examination included the inflammatory score of the VISA scale, marginal reflex distance, lower eyelid retraction, lagophthalmos, levator function, interpalpebral fissure distance, presence of corneal epithelial keratopathy, fat prolapse, total strabismic deviation, total ductions, intraocular pressure in upgaze, intraocular pressure in primary gaze, intraocular pressure difference from upgaze to primary position or 5 degrees downgaze, and proptosis.

The CT evaluations were performed by a neuroradiologist. Maximum recti muscle diameters were evaluated horizontally, using axial scans for the medial and lateral recti, and vertically, using coronal images for the inferior rectus and superior muscle group (superior rectus and levator palpebrae superioris). The maximal inferolateral diameter of the superior oblique diameter was measured using coronal images; the inferior oblique was not measured. Each muscle was evaluated separately in the analysis in addition to a total recti muscle diameter variable. Apical crowding was graded as 0 if no effacement of perineural fat planes was noted; 1 (mild), with 1% to 25% effacement; 2 (moderate), with 26% to 50% effacement; and 3 (severe), with greater than 50% effacement (Figure 1). Proptosis was measured from the posterior globe, using a perpendicular line from the scleral margin to the interzygomatic line on axial scans at the midglobe level. Both axial and coronal planes were used to measure the maximum superior ophthalmic vein diameter. Maximum optic nerve sheath diameter was measured only in an axial plane because of the difficulty with volume averaging from the posterior edge of the sclera in the coronal cuts. Optic nerve sheath diameters were measured just posterior to the globe, at the midorbit, and at the orbital apex. Lacrimal gland displacement was defined as present if half the gland was anterior to the frontozygomatic process on axial images. Two distinct dichotomous variables were used to describe the presence of low-density foci (small or confluent) in the extraocular muscles. Superior ophthalmic vein margin irregularity, presence of tendon enlargement, anterior-posterior globe diameter on axial images, and the presence of intracranial fat prolapse were also evaluated.14

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Figure 1. Grading of apical crowding on computed tomography coronal scans.

STATISTICAL ANALYSIS

With use of the generalized estimating equation, both eyes of each patient were included in this study to increase statistical power and account for the correlation between eyes.19 Univariate predictors of DON were evaluated using logistic regression, and statistical significance was defined as P < .05. A multivariate model was then built, using all variables found to be associated (P ≤ .1) on the univariate analysis in a forward stepwise fashion. Insignificant predictors were then removed in the order of descending P values to reduce issues of colinearity.

The final multivariate regression model was used to develop a receiver operating characteristic (ROC) curve. The area under the ROC curve was calculated20 to provide a descriptor of the model's ability to predict the presence of optic neuropathy. Two additional ROC curves using clinical and radiologic variables separately were then evaluated. The areas under the ROC curve from each model were then compared for statistical differences.21

To assess the performance of the predictive model, separate thresholds for disease presence were explored in an effort to maximize sensitivity and specificity or PPV and NPV. Because no formal threshold was selected, multiple maximizing thresholds are reported for the purpose of comprehensively evaluating the models' predictive potential.

One hundred eighty-nine orbits from 99 consecutive patients were evaluated. Table 1 reports the baseline demographic data and clinical examination findings, respectively, for all patients; Table 2 lists the significant variables found with univariate and multivariate regressions. Of note, total ductions and small hypodensities in the recti muscles were 2 variables inversely associated with DON. As total ductions decreased, the risk of DON increased, and the absence of hypodensities within the recti muscles was a risk factor for DON.

Table Graphic Jump LocationTable 1. Basic Demographic Data and Clinical Examination Findings at Baseline
Table Graphic Jump LocationTable 2. Univariate and Multivariate Predictors of DON

The area under the ROC curve for the combined multivariate model was 0.94 (95% confidence interval, 0.86-1.00) (Figure 2). The area under the ROC curve for the clinical-only model was 0.88 (95% confidence interval, 0.75-1.00), and the radiologic-only model found an area of 0.86 (0.79-0.93). An overall χ2 test comparing the area under the ROC curve of these 3 curves did not find a significant difference (P = .14) (Figure 2).

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Figure 2. ROC curves of the combined multivariate model and the simplified clinical-only and radiologic-only models. ROC indicates receiver operating characteristic.

Thresholds for maximizing PPV and NPV, as well as sensitivity and specificity, were investigated for the 3 models. For the combined clinical and radiologic multivariate model, a threshold of 0.6 resulted in a PPV of 100% and an NPV of 95%, with 96% of patients correctly predicted to have optic neuropathy. In terms of the sensitivity and specificity for this model, a cutoff of 0.1 resulted in a sensitivity of 91% and a specificity of 88%, with 88% of patients correctly classified (Figure 3). A cutoff of 0.5 in the clinical-only model resulted in a PPV of 83% and an NPV of 94%, with 94% of patients correctly classified, and a cutoff of 0.5 in the radiologic-only model found a PPV of 100% and an NPV of 92%, with 92% of patients correctly classified. In terms of sensitivity and specificity, for the clinical-only model, a threshold of 0.2 resulted in a sensitivity of 73% and a specificity of 92%; in the radiologic-only model, a threshold of 0.1 provided a sensitivity of 94% and a specificity of 73%. Figure 4 provides a graphical representation of the difference between the PPVs and NPVs of the 3 models.

Place holder to copy figure label and caption
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Figure 3. Graphical representation of sensitivity and specificity trade-off in combined multivariate model.

Place holder to copy figure label and caption
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Figure 4. Comparison of PPVs and NPVs between all multivariate models. NPV indicates negative predictive value; PPV, positive predictive value.

Assessment of the clinical and radiologic features of patients with thyroid-related orbitopathy provides a constellation of findings highly associated with optic neuropathy (Table 2). We hope that measurement of these variables will aid physicians in predicting and managing thyroid-related orbitopathy in situations in which direct optic nerve examination and function are equivocal and in identifying patients at high risk for developing DON.

The clinical significance and applicability of these findings were evaluated by considering different thresholds on each of the 3 models. This evaluation revealed that high measures of sensitivity, specificity, NPV, and PPV can be obtained from these models. No statistically significant difference in the area under the ROC curve was detected among the 3 models (combined radiologic and clinical, clinical-only, and radiologic-only). Similarly, almost no difference between the PPV and NPV of these models was found. However, the sensitivity of the clinical-only model was lower than that of the other 2 models, and the specificity of the radiologic-only model was lower than that of the other 2 models. Thus, to maximize sensitivity and specificity, both clinical and radiologic predictors are required.

To our knowledge, statistical confirmation of the inverse relationship of marginal reflex distance, interpalpebral fissures, and levator function with DON has not previously been published. One of us (J.R.) was the first to report this finding in a descriptive manner,22 and we were the first to determine it with statistically valid methods.23 Another independent group24 described a case of DON and ptosis that demonstrated improvement in the ptosis after a decompression operation. The development of the multivariate model demonstrated that only the marginal reflex distance was a significant predictor. This suggests that these 3 univariate associations are not independent and all probably represent different measurements of the same underlying process. We speculate that apical crowding, associated venous congestion due to outflow stasis, and direct superior muscle group involvement contribute to the features of a DON-related orbital apex syndrome.22 The relationship between blepharoptosis and DON was independent of upper eyelid edema; this was shown when insertion of the VISA inflammatory scale into the model did not change the measured association between the marginal reflex distance and DON. Thus, there is some evidence that this association is not a simple mechanical blepharoptosis due to upper eyelid edema.

An inverse relationship between small hypodensities in the recti muscles and DON was demonstrated. Thus, patients with these hypodensities were less likely to develop DON. Because these hypodensities have the same density as fat, we hypothesize that they represent differentiation of fibroblasts into adipocytes.25,26 This differentiation is believed to occur in the later stages of disease; therefore, hypodensities are more likely to be present in patients in the inactive phase of the disease and are thus less likely to be apparent on the initial examination of DON.

The primary weakness of this study is its retrospective design and the issues related to such a design. Furthermore, 9% of our cohort had DON compared with 5% described in previous work.1 This result is in keeping with the tertiary/quaternary care that was provided at this single surgeon's orbit clinic. Therefore, it is recommended that, if these data are applied to a more primary care clinic with a lower prevalence of DON, the prevalence-insensitive measures of this study (ie, sensitivity and specificity) should be applied rather than the prevalence-sensitive measures (ie, PPV and NPV). Further research will evaluate the interobserver and intraobserver reliability of the radiologic assessments. Volumetric analysis and orbital bony anatomy will also be evaluated in an attempt to increase our ability to radiologically detect DON and orbital bony configurations at risk for DON.

We conclude that clinical features other than direct assessment of optic nerve function and CT evaluation of patients with thyroid orbitopathy are able to predict the presence of DON with high sensitivity, specificity, PPV, and NPV. To our knowledge, this is the first statistical confirmation of the association between ptosis and DON to be published.

Correspondence: Ezekiel Weis, MD, MPH, Department of Ophthalmology, Faculty of Medicine and Dentistry, University of Alberta, 10240 Kingsway Ave, Room 2319, Edmonton AB T5H 3V9, Canada (ezekiel_weis@post.harvard.edu).

Submitted for Publication: October 12, 2010; accepted February 9, 2011.

Financial Disclosure: None reported.

Wiersinga WM, Bartalena L. Epidemiology and prevention of Graves' ophthalmopathy.  Thyroid. 2002;12(10):855-860
PubMed   |  Link to Article
Feldon SE, Muramatsu S, Weiner JM. Clinical classification of Graves' ophthalmopathy: identification of risk factors for optic neuropathy.  Arch Ophthalmol. 1984;102(10):1469-1472
PubMed   |  Link to Article
Dosso A, Safran AB, Sunaric G, Burger A. Anterior ischemic optic neuropathy in Graves' disease.  J Neuroophthalmol. 1994;14(3):170-174
PubMed   |  Link to Article
Koorneef L, Schmidt ED. The orbit: structure, autoantigens, and pathology. In: Wall J, How J, eds. Graves' Ophthalmolopathy. Vol 26. Oxford, England: Blackwell Scientific Publications; 1990:1-21
Wiersinga WM, ed, Kahaly GJ, edGraves Orbitopathy: A Multidisciplinary Approach. Basel, Switzerland: Karger; 2007
McKeag D, Lane C, Lazarus JH,  et al; European Group on Graves' Orbitopathy (EUGOGO).  Clinical features of dysthyroid optic neuropathy: a European Group on Graves' Orbitopathy (EUGOGO) survey.  Br J Ophthalmol. 2007;91(4):455-458
PubMed   |  Link to Article
Neigel JM, Rootman J, Belkin RI,  et al.  Dysthyroid optic neuropathy: the crowded orbital apex syndrome.  Ophthalmology. 1988;95(11):1515-1521
PubMed
Hallin ES, Feldon SE. Graves' ophthalmopathy, II: correlation of clinical signs with measures derived from computed tomography.  Br J Ophthalmol. 1988;72(9):678-682
PubMed   |  Link to Article
Trokel SL, Hilal SK. Submillimeter resolution CT scanning of orbital diseases.  Ophthalmology. 1980;87(5):412-417
PubMed
Kennerdell JS, Rosenbaum AE, El-Hoshy MH. Apical optic nerve compression of dysthyroid optic neuropathy on computed tomography.  Arch Ophthalmol. 1981;99(5):807-809
PubMed   |  Link to Article
Giaconi JA, Kazim M, Rho T, Pfaff C. CT scan evidence of dysthyroid optic neuropathy.  Ophthal Plast Reconstr Surg. 2002;18(3):177-182
PubMed   |  Link to Article
Murakami Y, Kanamoto T, Tuboi T, Maeda T, Inoue Y. Evaluation of extraocular muscle enlargement in dysthyroid ophthalmopathy.  Jpn J Ophthalmol. 2001;45(6):622-627
PubMed   |  Link to Article
Birchall D, Goodall KL, Noble JL, Jackson A. Graves ophthalmopathy: intracranial fat prolapse on CT images as an indicator of optic nerve compression.  Radiology. 1996;200(1):123-127
PubMed
Nugent RA, Belkin RI, Neigel JM,  et al.  Graves orbitopathy: correlation of CT and clinical findings.  Radiology. 1990;177(3):675-682
PubMed
Feldon SE, Lee CP, Muramatsu SK, Weiner JM. Quantitative computed tomography of Graves' ophthalmopathy: extraocular muscle and orbital fat in development of optic neuropathy.  Arch Ophthalmol. 1985;103(2):213-215
PubMed   |  Link to Article
Barrett L, Glatt HJ, Burde RM, Gado MH. Optic nerve dysfunction in thyroid eye disease: CT.  Radiology. 1988;167(2):503-507
PubMed
Dolman PJ, Rootman J. VISA classification for Graves orbitopathy.  Ophthal Plast Reconstr Surg. 2006;22(5):319-324
PubMed   |  Link to Article
Mourits MP, Koornneef L, Wiersinga WM, Prummel MF, Berghout A, van der Gaag R. Clinical criteria for the assessment of disease activity in Graves' ophthalmopathy: a novel approach.  Br J Ophthalmol. 1989;73(8):639-644
PubMed   |  Link to Article
Glynn RJ, Rosner B. Comparison of alternative regression models for paired binary data.  Stat Med. 1994;13(10):1023-1036
PubMed   |  Link to Article
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve.  Radiology. 1982;143(1):29-36
PubMed
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.  Biometrics. 1988;44(3):837-845
PubMed   |  Link to Article
Rootman JR, edDiseases of the Orbit: A Multidisciplinary Approach. 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2003
Weis E, Jhamb A, Chan AK,  et al.  Clinical-radiologic correlations in thyroid orbitopathy. Paper presented at: Swiss Symposium on Thyroid Eye Disease; December 7, 2007; Pontresina, Switzerland
Naseem M, Donker DL, Paridaens D. Blepharoptosis as a sign of severe Graves' orbitopathy.  Eye (Lond). 2009;23(8):1743-1744
PubMed   |  Link to Article
Sorisky A, Pardasani D, Gagnon A, Smith TJ. Evidence of adipocyte differentiation in human orbital fibroblasts in primary culture.  J Clin Endocrinol Metab. 1996;81(9):3428-3431
PubMed   |  Link to Article
Valyasevi RW, Erickson DZ, Harteneck DA,  et al.  Differentiation of human orbital preadipocyte fibroblasts induces expression of functional thyrotropin receptor.  J Clin Endocrinol Metab. 1999;84(7):2557-2562
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Grading of apical crowding on computed tomography coronal scans.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. ROC curves of the combined multivariate model and the simplified clinical-only and radiologic-only models. ROC indicates receiver operating characteristic.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 3. Graphical representation of sensitivity and specificity trade-off in combined multivariate model.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 4. Comparison of PPVs and NPVs between all multivariate models. NPV indicates negative predictive value; PPV, positive predictive value.

Tables

Table Graphic Jump LocationTable 1. Basic Demographic Data and Clinical Examination Findings at Baseline
Table Graphic Jump LocationTable 2. Univariate and Multivariate Predictors of DON

References

Wiersinga WM, Bartalena L. Epidemiology and prevention of Graves' ophthalmopathy.  Thyroid. 2002;12(10):855-860
PubMed   |  Link to Article
Feldon SE, Muramatsu S, Weiner JM. Clinical classification of Graves' ophthalmopathy: identification of risk factors for optic neuropathy.  Arch Ophthalmol. 1984;102(10):1469-1472
PubMed   |  Link to Article
Dosso A, Safran AB, Sunaric G, Burger A. Anterior ischemic optic neuropathy in Graves' disease.  J Neuroophthalmol. 1994;14(3):170-174
PubMed   |  Link to Article
Koorneef L, Schmidt ED. The orbit: structure, autoantigens, and pathology. In: Wall J, How J, eds. Graves' Ophthalmolopathy. Vol 26. Oxford, England: Blackwell Scientific Publications; 1990:1-21
Wiersinga WM, ed, Kahaly GJ, edGraves Orbitopathy: A Multidisciplinary Approach. Basel, Switzerland: Karger; 2007
McKeag D, Lane C, Lazarus JH,  et al; European Group on Graves' Orbitopathy (EUGOGO).  Clinical features of dysthyroid optic neuropathy: a European Group on Graves' Orbitopathy (EUGOGO) survey.  Br J Ophthalmol. 2007;91(4):455-458
PubMed   |  Link to Article
Neigel JM, Rootman J, Belkin RI,  et al.  Dysthyroid optic neuropathy: the crowded orbital apex syndrome.  Ophthalmology. 1988;95(11):1515-1521
PubMed
Hallin ES, Feldon SE. Graves' ophthalmopathy, II: correlation of clinical signs with measures derived from computed tomography.  Br J Ophthalmol. 1988;72(9):678-682
PubMed   |  Link to Article
Trokel SL, Hilal SK. Submillimeter resolution CT scanning of orbital diseases.  Ophthalmology. 1980;87(5):412-417
PubMed
Kennerdell JS, Rosenbaum AE, El-Hoshy MH. Apical optic nerve compression of dysthyroid optic neuropathy on computed tomography.  Arch Ophthalmol. 1981;99(5):807-809
PubMed   |  Link to Article
Giaconi JA, Kazim M, Rho T, Pfaff C. CT scan evidence of dysthyroid optic neuropathy.  Ophthal Plast Reconstr Surg. 2002;18(3):177-182
PubMed   |  Link to Article
Murakami Y, Kanamoto T, Tuboi T, Maeda T, Inoue Y. Evaluation of extraocular muscle enlargement in dysthyroid ophthalmopathy.  Jpn J Ophthalmol. 2001;45(6):622-627
PubMed   |  Link to Article
Birchall D, Goodall KL, Noble JL, Jackson A. Graves ophthalmopathy: intracranial fat prolapse on CT images as an indicator of optic nerve compression.  Radiology. 1996;200(1):123-127
PubMed
Nugent RA, Belkin RI, Neigel JM,  et al.  Graves orbitopathy: correlation of CT and clinical findings.  Radiology. 1990;177(3):675-682
PubMed
Feldon SE, Lee CP, Muramatsu SK, Weiner JM. Quantitative computed tomography of Graves' ophthalmopathy: extraocular muscle and orbital fat in development of optic neuropathy.  Arch Ophthalmol. 1985;103(2):213-215
PubMed   |  Link to Article
Barrett L, Glatt HJ, Burde RM, Gado MH. Optic nerve dysfunction in thyroid eye disease: CT.  Radiology. 1988;167(2):503-507
PubMed
Dolman PJ, Rootman J. VISA classification for Graves orbitopathy.  Ophthal Plast Reconstr Surg. 2006;22(5):319-324
PubMed   |  Link to Article
Mourits MP, Koornneef L, Wiersinga WM, Prummel MF, Berghout A, van der Gaag R. Clinical criteria for the assessment of disease activity in Graves' ophthalmopathy: a novel approach.  Br J Ophthalmol. 1989;73(8):639-644
PubMed   |  Link to Article
Glynn RJ, Rosner B. Comparison of alternative regression models for paired binary data.  Stat Med. 1994;13(10):1023-1036
PubMed   |  Link to Article
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve.  Radiology. 1982;143(1):29-36
PubMed
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.  Biometrics. 1988;44(3):837-845
PubMed   |  Link to Article
Rootman JR, edDiseases of the Orbit: A Multidisciplinary Approach. 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2003
Weis E, Jhamb A, Chan AK,  et al.  Clinical-radiologic correlations in thyroid orbitopathy. Paper presented at: Swiss Symposium on Thyroid Eye Disease; December 7, 2007; Pontresina, Switzerland
Naseem M, Donker DL, Paridaens D. Blepharoptosis as a sign of severe Graves' orbitopathy.  Eye (Lond). 2009;23(8):1743-1744
PubMed   |  Link to Article
Sorisky A, Pardasani D, Gagnon A, Smith TJ. Evidence of adipocyte differentiation in human orbital fibroblasts in primary culture.  J Clin Endocrinol Metab. 1996;81(9):3428-3431
PubMed   |  Link to Article
Valyasevi RW, Erickson DZ, Harteneck DA,  et al.  Differentiation of human orbital preadipocyte fibroblasts induces expression of functional thyrotropin receptor.  J Clin Endocrinol Metab. 1999;84(7):2557-2562
PubMed   |  Link to Article

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