Introduction: Limited data are available on the incidence of primary ophthalmic cancers worldwide.
We describe the incidence and trends of primary ophthalmic cancers in Singapore.
Methods: Data on ophthalmic cancers diagnosed in Singapore from 1996 to 2016 were retrieved
from the Singapore Cancer Registry for analysis. All were histologically proven primary ophthalmic
cancers. Calculations of incidence and age-specific frequency of ophthalmic malignancy were made.
Results: A total of 297 cases were included, with males constituting 59.9%. The race distribution
was 78.5% Chinese, 16.5% Malay, 3.7% Indians and 1.3% others. There was an overall increase in
ophthalmic malignancies. The mean age of onset was 47.4 years. The most common cancers were
retinoblastoma (93.3%) in patients younger than 15 years, and lymphoma (71.3%) in patients aged
15 years and older. There has been an increase in lymphomas from 16.7% in 1968–1995 to 71.3%
in 1996–2016 in those aged 15 years and older. The most common types of ophthalmic cancer according
to location are lymphoma of the orbit, conjunctiva, cornea and lacrimal gland; retinoblastoma of the
retina; and malignant melanoma of the choroid and ciliary body.
Conclusion: Our study reported the incidence and trends of ophthalmic cancer in the Singapore
population and showed an overall increase in ophthalmic malignancies in Singapore from 1996–2016. A
substantial increase in lymphomas over the last 2 decades was noted. The data could aid clinicians,
epidemiologists and policymakers in implementing strategies to address trends in ophthalmic cancers
and spur aetiological research to improve quality of life in pa tients with such cancers.
Keywords: Aetiology; epidemiology; malignancy; orbital cancers
Background: Diabetic retinopathy (DR) screening using colour retinal photographs is cost-effective and time-efficient. In real-world clinical settings, DR severity is frequently graded by individuals of different expertise levels. We aim to determine the agreement in DR severity grading between human graders of varying expertise and an automated deep learning DR screening software (ADLS). Methods: Using the International Clinical DR Disease Severity Scale, two hundred macula-centred fundus photographs were graded by retinal specialists, ophthalmology residents, family medicine physicians, medical students, and the ADLS. Based on referral urgency, referral grading was divided into no referral, non-urgent referral, and urgent referral to an ophthalmologist. Inter-observer and intra-group variations were analysed using Gwet’s agreement coefficient, and the performance of ADLS was evaluated using sensitivity and specificity. Results: The agreement coefficient for inter-observer and intra-group variability ranged from fair to very good, and moderate to good, respectively. The ADLS showed a high area under curve of 0.879, 0.714, and 0.836 for non-referable DR, non-urgent referable DR, and urgent referable DR, respectively, with varying sensitivity and specificity values. Conclusion: Inter-observer and intra-group agreements among human graders vary widely, but ADLS is a reliable and reasonably sensitive tool for mass screening to detect referable DR and urgent referable DR.
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